GEOmedia 3 2022

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Rivista bimestrale - anno XXVI - Numero 3/<strong>2022</strong> - Sped. in abb. postale 70% - Filiale di Roma<br />


GIS<br />




3D<br />


CAD<br />

BIM<br />


WEBGIS<br />

UAV<br />



LBS<br />


GNSS<br />



LiDAR<br />


May/June <strong>2022</strong> year XXVI N°3<br />

Mobile Robotics and<br />

Autonomous Mapping<br />









Inspiration for a smarter World<br />

This is the motto of the Intergeo Conference <strong>2022</strong>, where the English language edition of<br />

<strong>GEOmedia</strong>, usually issued as the third of the year in the summer, will be distributed on next<br />

October.<br />

The Conference will highlight current developments in surveying with following main topics:<br />

Digital Twins and their value creation<br />

4D-geodata and geospatial IoT<br />

Potentials of remote sensing<br />

Industrial surveying, measurement systems and robotics<br />

Smart Cities and mobility in the context of climate change and sustainability<br />

Mobile mapping, Web services and geoIT in disaster management<br />

Spatial reference and positioning<br />

Earth observation and Galileo<br />

Trend topics such as Building Information Modeling (BIM) and the diverse application<br />

possibilities of the Digital Twins, but also the current requirements for the Smart City and<br />

rural areas have their fixed place in the Conference. The Digital Twins will be a matter of<br />

particular importance in this edition. The focus will be on their use in Building Information<br />

Modeling, smart planning and construction.<br />

But let’s go to see the content of this issue where we start with a Focus “From the field to<br />

the clouds: data collection and publication with QGIS” by Paolo Cavallini, Matteo Ghetta<br />

and Ulisse Cavallini, about the main solutions available for data collection and seamless<br />

publications over the web: MerginMaps, Qfield, Lizmap, with an example form a water<br />

resources project in Gambia. A following Focus is on “Open-source GIS software and<br />

components for modelling watershed phenomena”, by Flavio Lupia and Giuseppe Pulighe,<br />

over the recent version of the Soil and Water Assessment Tool (SWAT) that was implemented<br />

by a dedicated QGIS plugin (QSWAT), widening the userbase and the potential modelling<br />

application worldwide. Then we’ll go to the Reports “Mobile Robotics and Autonomous<br />

Mapping: Technology for a more Sustainable Agriculture” by Eleonora Maset, Lorenzo Scalera<br />

and Diego Tiozzo Fasiolo, concerning the automation in geomatics for agriculture using<br />

robotics platforms, that must be equipped with appropriate technology. And “Geographical<br />

Information: the Italian Scientific Associations and... the Big Tech” from Valerio Zunino,<br />

observing that while the World is changing, the Italian Scientific Associations of Geographical<br />

Information are not.<br />

Marco Lisi, in “Time and Longitude: an unexpected affinity”, talks about the Time, the fourth<br />

dimension, becoming increasingly important in all aspects of technology and science.<br />

Finally, don’t miss “Potatoes, Artificial Intelligence and other amenities: playing with Colors<br />

on Panchromatic Aerial Photographs”, by Gianluca Cantoro from Italian National AirPhoto<br />

Archive (Aerofototeca Nazionale, AFN), discussing about the use of historical photographs,<br />

whether taken from the air or from the ground, are usually synonyms of grayscale or sepia<br />

prints.<br />

Enjoy your reading,<br />

Renzo Carlucci

In this<br />

issue...<br />

FOCUS<br />

REPORT<br />


From the field<br />

to the clouds:<br />

data collection and<br />

publication with QGIS<br />

By Paolo Cavallini,<br />

Matteo Ghetta,<br />

Ulisse Cavallini<br />

6<br />

24 ESA Image<br />

32 NEWS<br />


46 AGENDA<br />

12<br />

Open-source GIS<br />

software and components<br />

for modelling watershed<br />

phenomena<br />

Understanding the soil<br />

and water components<br />

under different<br />

management options with<br />

QGIS and the SWAT<br />

By Flavio Lupia<br />

and Giuseppe Pulighe<br />

On the cover the<br />

sensorized mobile<br />

platform developed<br />

at University of<br />

Udine, Italy.<br />

geomediaonline.it<br />

<strong>GEOmedia</strong>, published bi-monthly, is the Italian magazine for<br />

geomatics. Since more than 20 years publishing to open a<br />

worldwide window to the Italian market and vice versa.<br />

Themes are on latest news, developments and applications in<br />

the complex field of earth surface sciences.<br />

<strong>GEOmedia</strong> faces with all activities relating to the acquisition,<br />

processing, querying, analysis, presentation, dissemination,<br />

management and use of geo-data and geo-information. The<br />

magazine covers subjects such as surveying, environment,<br />

mapping, GNSS systems, GIS, Earth Observation, Geospatial<br />

Data, BIM, UAV and 3D technologies.

Mobile Robotics and<br />

Autonomous Mapping:<br />

Technology for a more<br />

Sustainable Agriculture<br />

by Eleonora Maset,<br />

Lorenzo Scalera,<br />

Diego Tiozzo Fasiolo<br />

16<br />

ADV<br />

Ampere 45<br />

Epsilon 33<br />

Esri Italia 23<br />

Geomax 31<br />

Gter 15<br />

INTERGEO 35<br />

Nais 39<br />

Planetek 48<br />

Stonex 47<br />

Strumenti Topografici 2<br />

Geographical<br />

Information: Our<br />

Associations and ...<br />

the Big Tech<br />

by Valerio Zunino<br />

20<br />

Teorema 46<br />

In the background image:<br />

Bonn, Germany. This Esa<br />

Image of the week, also featured<br />

on the Earth from<br />

Space video programme, was<br />

captured by the Copernicus<br />

Sentinel-2 mission, that with<br />

its high-resolution optical camera,<br />

can image up to 10 m<br />

ground resolution.<br />

(Credits: ESA)<br />

26<br />

Time and<br />

Longitude: an<br />

unexpected<br />

affinity<br />

by Marco Lisi<br />

Chief Editor<br />

RENZO CARLUCCI, direttore@rivistageomedia.it<br />

Editorial Board<br />

Vyron Antoniou, Fabrizio Bernardini, Caterina Balletti,<br />

Roberto Capua, Mattia Crespi, Fabio Crosilla,<br />

Donatella Dominici, Michele Fasolo, Marco Lisi,<br />

Flavio Lupia, Luigi Mundula, Beniamino Murgante,<br />

Aldo Riggio, Monica Sebillo, Attilio Selvini, Donato Tufillaro<br />

Managing Director<br />

FULVIO BERNARDINI, fbernardini@rivistageomedia.it<br />

Editorial Staff<br />


redazione@rivistageomedia.it<br />

Marketing Assistant<br />

TATIANA IASILLO, diffusione@rivistageomedia.it<br />

Design<br />

DANIELE CARLUCCI, dcarlucci@rivistageomedia.it<br />

MediaGEO soc. coop.<br />

Via Palestro, 95 00185 Roma<br />

Tel. 06.64871209 - Fax. 06.62209510<br />

info@rivistageomedia.it<br />

ISSN 1128-8132<br />

Reg. Trib. di Roma N° 243/2003 del 14.05.03<br />

Stampa: System Graphics Srl<br />

Via di Torre Santa Anastasia 61 00134 Roma<br />

Paid subscriptions<br />

Science & Technology Communication<br />

<strong>GEOmedia</strong> is available bi-monthly on a subscription basis.<br />

The annual subscription rate is € 45. It is possible to subscribe at any time via<br />

https://geo4all.it/abbonamento. The cost of one issue is € 9 €, for the previous<br />

issue the cost is € 12 €. Prices and conditions may be subject to change.<br />

Issue closed on: 28/07/<strong>2022</strong><br />

una pubblicazione<br />

Science & Technology Communication

FOCUS<br />

From the field to the clouds:<br />

data collection and<br />

publication with QGIS<br />

By Paolo Cavallini, Matteo Ghetta, Ulisse Cavallini<br />

Open source GIS, and in particular QGIS, is a leading free and open<br />

source solution for desktop mapping since many years already.<br />

Its versatility, ease of use, and analytical power have made it the<br />

software of choice for many professionals around the world (see<br />

https://analytics.qgis.org). Field data collection and checking, and<br />

web publication are attracting more attention in the recent years.<br />

A whole suite of integrated tools is now available to implement a<br />

complete workflow, all centered on QGIS.<br />

Central to all tools is the QGIS project, designed and created using<br />

QGIS Desktop. Its power in creating beautiful and rich styling is<br />

probably unsurpassed, with expression-based styling, fusion modes,<br />

and a huge set of other functions. The same project can be used on a<br />

mobile device, and exposed through a web service (WMS, WFS, WCS,<br />

WPS) and a complete web interface.<br />

Mobile<br />

Over the 20+ of life of QGIS,<br />

a number of mobile interfaces<br />

have been designed, from<br />

the first attempt to run the<br />

whole of QGIS on Android,<br />

or a simplified interface on<br />

Windows Mobile, to proper<br />

mobile apps. Currently the<br />

most important and used<br />

solutions to use QGIS on a<br />

mobile are Qfield (https://<br />

qfield.org/) and MerginMaps<br />

(https://merginmaps.com/).<br />

Both are generic tools, that<br />

can be effectively employed<br />

in a wide variety of contexts.<br />

Their flexibility stems from<br />

the ease with which they<br />

can be configured, simply<br />

through QGIS projects, that<br />

include sophisticated styling<br />

and simple to complex forms,<br />

with all expected functionalities<br />

such as custom menus<br />

(drop-down, checkbox,<br />

calendar etc.), relations,<br />

constraints, default values,<br />

user guiding tips, etc. While<br />

still relatively new entries in<br />

the market, they have been<br />

successfully employed in<br />

extensive surveys, from single<br />

users up to thousands of field<br />

surveyors simultaneously collecting<br />

data in the field.<br />

MerginMaps<br />

MerginMaps is a web service,<br />

written in Python with flask,<br />

that manages the synchronization<br />

process of a QGIS<br />

6 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

FOCUS<br />

project, and all the related<br />

files. For media files, it is<br />

not unlike other cloud file<br />

management service. Unlike<br />

other cloud file managers,<br />

though, MerginMaps is able<br />

to manage geospatial information,<br />

primarily in the form<br />

of geopackage files. When a<br />

new version of a geopackage<br />

is uploaded, for example because<br />

a surveyor added some<br />

features and is uploading<br />

them back on the centralized<br />

server, the geodiff library is<br />

used to check for changes,<br />

merge them and solve any<br />

conflicts. This enables some<br />

flexibility in the downloading<br />

and uploading of new data,<br />

since multiple surveyors can<br />

add features for their area<br />

of interest, upload different<br />

versions of the modified geopackage,<br />

and MerginMaps<br />

will take care of adding every<br />

new feature to the centralized<br />

repository.<br />

Lutra Consulting, the firm<br />

developing MerginMaps,<br />

offers an official hosted instance,<br />

a reliable way to use<br />

MerginMaps without the<br />

need for configuration and<br />

installation on a local server.<br />

The official hosted instance<br />

offers a generous free trial<br />

for non-commercial usage.<br />

Pricing is clear and reasonable,<br />

with no per-user pricing,<br />

and the support is quick and<br />

responsive.<br />

The surveying process is effectively<br />

split in two. The<br />

first phase involves the generation<br />

of the QGIS project,<br />

the related layers, and the<br />

form structure, and the<br />

subsequent upload to the<br />

MerginMaps web service. Far<br />

from being complicated, this<br />

phase still requires a good understanding<br />

of GIS software<br />

and data formats.<br />

Once the project is uploaded<br />

and ready, the surveying phase<br />

can begin. Due to the easeof-use<br />

of the mobile application,<br />

the surveying requires<br />

minimal technical skill, and<br />

operators can be trained in a<br />

matter of few few days. From<br />

their point of view, the intricacies<br />

of the project are invisible:<br />

they just need to add or<br />

modify the features according<br />

to the form, preconfigured<br />

through QGIS, and click on<br />

the synchronization button<br />

once they are online. A very<br />

recent addition, the option<br />

to automatically upload new<br />

changes whenever an internet<br />

connection is detected, further<br />

simplifies this.<br />

The project folder itself is<br />

what is visible from the web<br />

interface. In order to manipulate<br />

the project, and the<br />

geospatial data, MerginMaps<br />

can be accessed from QGIS,<br />

through the official plugin,<br />

and through the MerginMaps<br />

mobile application, available<br />

for Android and iOS.<br />

The MerginMaps application<br />

has a special focus on simple<br />

UI and UX design, in order<br />

to be accessible by everyone,<br />

regardless of their GIS experience<br />

and in demanding field<br />

conditions.<br />

Qfield<br />

Qfield has been the first natively<br />

Android mobile application<br />

connected to QGIS.<br />

Downloaded around halfmillion<br />

times it is available<br />

for Android and now iOS.<br />

The idea of the usage is very<br />

simple: the user sets up a<br />

project in QGIS and thanks<br />

to the plugin QfieldSync it<br />

will be packaged in a folder.<br />

The folder created has to be<br />

copied to the device and with<br />

the App data can be collected<br />

on the field. Back to the office<br />

the data collected with<br />

the mobile device have to be<br />

copied back on the machine<br />

and re-synchronized to the<br />

original data source with the<br />

QfieldSync plugin.<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 7

FOCUS<br />

The App has a very simple<br />

design and it comes with a lot<br />

of features: snapping facilities,<br />

advanced form layout, pictures<br />

and interaction with the legend<br />

to name a few.<br />

The manual synchronization<br />

can be nowadays avoided<br />

thanks to QfieldCloud, a<br />

Django framework that is able<br />

to store and automatically<br />

synchronize the data from the<br />

computer to the mobile device<br />

and vice versa. Open Source,<br />

QfieldCloud is still in Beta<br />

version and let the user choose<br />

between installing the software<br />

on the server or register to the<br />

web with a free plan (limited<br />

space) or buy additional space.<br />

The main advantage of<br />

QfieldCloud is that the user<br />

can log in both on the machine<br />

and on the device with the<br />

same name and immediately<br />

synchronize the data between<br />

all the devices. The QfieldSync<br />

plugin in QGIS has all the<br />

options needed to log in, synchronize<br />

and also see the data<br />

changes difference.<br />

8 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

FOCUS<br />

As for Mergin, also Qfield<br />

has the possibility of using a<br />

server managed by OpenGIS.<br />

ch, the firm developing it<br />

without the need for configuration<br />

and installation on a<br />

local server.<br />

Web<br />

A number of different web<br />

interfaces have been designed<br />

around QGIS. For all of<br />

them the basic mechanism is<br />

the same: all user requests are<br />

sent to the backend (QGIS<br />

Server) that creates the map<br />

and other objects (legend,<br />

print layouts etc.) and send<br />

them back to the web app.<br />

The main advantages over<br />

other free and open source<br />

webGIS solutions are the ease<br />

to create both visually sophisticated<br />

maps, and complex<br />

print and reporting through<br />

QGIS Desktop layouts, without<br />

the need for specific<br />

web skills.<br />

The most widely used is<br />

Lizmap, created and maintained<br />

by 3Liz, a South French<br />

company, who also substantially<br />

contributes from years<br />

to core QGIS development.<br />

As for the other solutions<br />

described, also Lizmap can<br />

be used without the need for<br />

configuration and installation<br />

on a local server through a<br />

service managed and maintained<br />

by 3Liz, the firm developing<br />

it.<br />

Case study<br />

MerginMaps was recently<br />

used in a project geared towards<br />

the improvement of<br />

water resources infrastructure<br />

in The Gambia, financed by<br />

the African Development<br />

Bank; the project is headed<br />

by Hydronova, and its GIS<br />

section is technically managed<br />

by Faunalia.<br />

Special acknowledgment<br />

for this project goes for the<br />

continuous support to the<br />

Climate Smart Rural WASH<br />

Development Project Office<br />

Team and to the Department<br />

of Water Resources Staff, under<br />

the Ministry of Fisheries<br />

and Water Resources of the<br />

Gambia.<br />

Data collection is the first<br />

task upon which the whole<br />

project is built, since in order<br />

to improve resource management,<br />

key stakeholders need<br />

to know the current situation<br />

and distribution of the resources<br />

at their disposal.<br />

An open source solution<br />

is ideal in most contexts,<br />

even more so in a context<br />

where free access to data is<br />

paramount, and budget constraints<br />

are tight.<br />

The MerginMaps mobile<br />

application, backed by the<br />

Mergin web service, was chosen<br />

due to its ease of use and<br />

synchronization. Due to the<br />

possibility and ease of setting<br />

up a Mergin instance, all the<br />

data was kept in-situ at the<br />

relevant ministry, retaining<br />

control on this crucial information.<br />

A QGIS project with four<br />

layers, each with a custom<br />

form, was created. In order<br />

to have all the data fully offline,<br />

vector tiles were used.<br />

These were generated, for the<br />

whole country, by extracting<br />

OpenStreetMaps data, packaging<br />

it in an mbtiles file, and<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 9

FOCUS<br />

by styling them with one of<br />

the OpenMapTiles styles.<br />

The resulting project was<br />

only 16 MB, a small fraction<br />

of the 160+ MB that would<br />

have been needed for rasterized<br />

tiles.<br />

Using the QGIS drag-n-drop<br />

form, extensive logic was<br />

introduced in the data entry<br />

user interface. With this<br />

setup, users were guided in<br />

choosing the three different<br />

administrative levels from a<br />

drop down, with automatic<br />

filtering of the available options,<br />

and constraints with<br />

appropriate description were<br />

implemented. For water<br />

sources, which are of upmost<br />

importance, a photo was also<br />

required.<br />

After the project was tested,<br />

teams of surveyors covered<br />

the whole country in the span<br />

of a few months, while the<br />

survey manager constantly<br />

analyzed data quality with<br />

spot crosschecks.<br />

Periodically, the tablets were<br />

brought back online, and<br />

the data was synchronized.<br />

In this process, the selective<br />

sync option, introduced in<br />

MerginMaps (at the time called<br />

Input) 1.0.1, was crucial.<br />

This feature instructs all the<br />

tablets to upload the pho-<br />

tos that were taken locally,<br />

without downloading all<br />

of the other media in the<br />

project, that was added by<br />

other surveyors. Without<br />

this, more than 15 GB of<br />

photos would have been<br />

downloaded into each tablet,<br />

severely impairing the<br />

synchronization process and<br />

requiring a stable and fast<br />

internet connection.<br />

At the completion of the<br />

survey, the data was checked<br />

and cleaned, then it<br />

was synchronized with a<br />

PostgreSQL/PostGIS database,<br />

using the mergin-dbsync<br />

tool, as described in<br />

the “Extensions and integrations”<br />

section. This procedure<br />

initialized the new<br />

database, and ensures that<br />

any change in the data will<br />

be reflected in the database<br />

tables.<br />

Using the newly initialized<br />

database, a second QGIS<br />

project based on the same<br />

data was created and published<br />

on a WebGIS based on<br />

QGIS server and Lizmap,<br />

thus reusing QGIS styling<br />

without the need for restyling<br />

and conversion. In<br />

this phase, the advanced<br />

forms could be reused, thus<br />

showing on the website all<br />

the information as entered<br />

by the surveyor, including<br />

the water source photo.<br />

Other layers, such as the<br />

administrative subdivisions,<br />

were added, as well as the<br />

localization tool, that enables<br />

any user to quickly find<br />

the current location, a village,<br />

or an area of interest.<br />

By combining the efficiency<br />

of PostgreSQL materialized<br />

view, and the flexibility<br />

of the QGIS print layout,<br />

multiple layouts were created<br />

and personalized for<br />

each administrative level,<br />

10 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

FOCUS<br />

from country aggregates to<br />

the village level. Graphs created<br />

with DataPlotly, a recent<br />

addition to the QGIS print<br />

layout, were also used.<br />

These layouts were then exposed<br />

in Lizmap with the<br />

AtlasPrint QGIS Server plugin.<br />

Extensions and integrations<br />

MerginMaps, being written<br />

in Python, with good documentation,<br />

has quite a few<br />

extensions, that enable it to<br />

adapt to the specific needs of<br />

most survey projects.<br />

Mergin-db-sync, first released<br />

in June 2020, is a crucial<br />

part of the MerginMaps<br />

offering. Also written in<br />

Python, it interfaces with the<br />

main Mergin web service and<br />

with a PostgreSQL/PostGIS<br />

database, keeping the two<br />

in constant sync. Whenever<br />

a change is detected in the<br />

specified geopackage, the<br />

changes are propagated to the<br />

PostgreSQL database, and<br />

vice versa. Strict versioning<br />

is still maintained, since the<br />

tool creates a new version of<br />

the MerginMaps project, just<br />

as a user uploading new data<br />

would. The tool uses two<br />

PostgreSQL schemas, one in<br />

which changes can be made<br />

directly, and a backup copy<br />

used to check for changes.<br />

It utilizes the geodiff library<br />

to check and merge changes,<br />

even if they happen in the<br />

two backends at the same<br />

time. Mergin-db-sync can<br />

also be used to expose the<br />

data on a WebGIS such as<br />

Lizmap.<br />

Mergin-media-sync, first<br />

released in December 2021,<br />

allows for the offloading of<br />

the media files, often representing<br />

a good chunk of the<br />

project size, to a local drive,<br />

or to the MinIO object storage.<br />

When new media files<br />

are added, the tool downloads<br />

them, uploads them to<br />

the configured service, and<br />

updates the relevant rows in<br />

the geopackage, pointing the<br />

media path to the new url. In<br />

a wide-area survey, covering<br />

many features and containing<br />

photos, this tool can effectively<br />

be used to avoid cluttering<br />

the MerginMaps project<br />

with hundreds of gigabytes of<br />

images.<br />

Both MerginMaps and<br />

QField can be used with an<br />

external GPS/GNSS device,<br />

that can be obtained at a low<br />

cost, enabling high precision<br />

location, up to a centimeter<br />

of accuracy. These devices,<br />

once highly priced, are now<br />

accessible and reliable.<br />


field survey; water resources; qgis; qfield<br />


QGIS is the leading free and open source<br />

desktop GIS. It is also a complete ecosystem,<br />

that allows to build complete workflows, from<br />

field data collection to publication on the web.<br />

Central to it are QGIS projects, that define<br />

data sources, projections, styling and integration,<br />

and are reused from mobile to the web<br />

without a need to reconfigure them. We describe<br />

the main solutions available for data collection<br />

and seamless publication over the web:<br />

MerginMaps, Qfield, Lizmap, with an example<br />

form a water resources project in Gambia.<br />

AUTHOR<br />

Paolo Cavallini<br />

cavallini@faunalia.it<br />

Matteo Ghetta<br />

matteo.ghetta@faunalia.eu<br />

Ulisse Cavallini<br />

ulisse.cavallini@faunalia.it<br />

Faunalia<br />

www.faunalia.eu<br />

Piazza Garibaldi 4, 56025 Pontedera<br />

(PI), Italy<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 11

FOCUS<br />

Open-source GIS software and components<br />

for modelling watershed phenomena<br />

Understanding the soil and water components under<br />

different management options with QGIS and the SWAT+<br />

By Flavio Lupia and Giuseppe Pulighe<br />

Fig. 1 – Workflow with input and output components for running SWAT+ through the QSWAT plugin for QGIS.<br />

Modelling the<br />

watershed balance<br />

Current and future climate<br />

change are expected to increase<br />

our challenges in preserving<br />

natural resources and ecosystem<br />

services. At the watershed scale,<br />

the processes taking places<br />

relate to interactions between<br />

soil and water and are influenced<br />

by land use management.<br />

Precipitation, infiltration, runoff,<br />

evapotranspiration, soil<br />

erosion, soil and water pollutions<br />

are the main components<br />

to be considered whenever for<br />

the simulation of the watershed<br />

system within the current and<br />

future conditions under changing<br />

drivers (i.e., human interventions<br />

and climate change).<br />

One of the most popular watershed<br />

modelling tools is the<br />

Soil and Water Assessment Tool<br />

(SWAT), a public domain model<br />

jointly developed by USDA<br />

Agricultural Research Service<br />

and Texas A&M University<br />

(Arnold et al., 1998). SWAT<br />

has been used worldwide for<br />

different applications (water<br />

quality, land use, soil erosion,<br />

crop yield, etc.) during the last<br />

four decades. As of May <strong>2022</strong>,<br />

a total of 5154 articles report<br />

SWAT applications within<br />

different journals according to<br />

the SWAT Literature Database<br />

(https://www.card.iastate.edu/<br />

swat_articles/).<br />

SWAT enables the simulation of<br />

watershed and river basin quantity<br />

and quality of surface and<br />

ground water under the influence<br />

of land use, management,<br />

and climate change. It can be<br />

used to monitor and control soil<br />

erosion, non-point source pollution<br />

and basin management. An<br />

entirely reconstructed version of<br />

SWAT, nicknamed SWAT+, was<br />

only launched in recent years<br />

to improve the capabilities of<br />

SWAT code maintenance and<br />

future development. Reservoir<br />

operation functions have been<br />

added to SWAT+ in addition<br />

to the new model structure to<br />

increase model simulation performance.<br />

Now, the physical<br />

objects (hydrologic response<br />

units (HRUs), aquifers, canals,<br />

ponds, reservoirs, point sources,<br />

and inlets) are built as separate<br />

modules.<br />

QSWAT: the QGIS plugin for<br />

the new SWAT+ model<br />

SWAT model was implemented<br />

withing the GIS environment<br />

with dedicated plugins both for<br />

commercial and open-source<br />

GIS platforms. The GIS imple-<br />

12 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

FOCUS<br />

mentations has allowed users to<br />

manage more efficiently the watershed<br />

modelling process in its<br />

natural environment, the GIS,<br />

where the spatial component<br />

of the various datasets can be<br />

handled straightforwardly.<br />

QSWAT is the most recent<br />

implementation as QGIS plugin,<br />

written in Python, of the<br />

new version SWAT+. As of 6th<br />

April <strong>2022</strong>, QSWAT3 v1.5 for<br />

QGIS3 was released for 32 and<br />

64bit machines. SWAT+ is written<br />

in FORTRAN and is also<br />

available as command-line executable<br />

file that runs text file inputs<br />

without interface (SWAT+<br />

installer 2.1.0 was released on<br />

31 March <strong>2022</strong> for Windows,<br />

Linux, and MacOS). QSWAT<br />

is increasingly gaining momentum<br />

thanks to the spreading<br />

and robustness of the opensource<br />

GIS platform. QGIS has<br />

a huge number of users and a<br />

solid reputation. It is utilized in<br />

academic and professional settings,<br />

and it has been translated<br />

into more than 48 languages.<br />

Moreover, the release of SWAT<br />

code as open source has benefitted<br />

the diffusion and improvement<br />

of the model by making<br />

it more robust and suitable for<br />

different applications thanks<br />

to the collaboration of several<br />

users with various expertise.<br />

Different channels are available<br />

for users’ collaboration such as<br />

QSWAT user group, SWAT+<br />

Editor user group and SWAT+<br />

model user group (https://swat.<br />

tamu.edu). Other plugins are<br />

also available, such as the one<br />

developed for the commercial<br />


Beyond the functions provided<br />

by QSWAT for setting<br />

the watershed to be analysed,<br />

SWAT+ is complemented by<br />

additional software: SWAT<br />

Editor (a user interface for<br />

modifying SWAT+ inputs and<br />

running the model installed<br />

Fig. 2 – Spatial distribution of annual means of the actual evapotranspiration from the soil at subbasin scale for a<br />

watershed through QSWAT.<br />

along with QSWAT), SWAT+<br />

Toolbox (a user-friendly tool<br />

for SWAT+ for sensitivity<br />

analysis, manual and automatic<br />

calibration), SWATplus-CUP<br />

(the Calibration Uncertainty<br />

Program for SWAT+ requiring<br />

a license purchase) and<br />

SWATplusR (a set of tools<br />

taking advantage of the R environment<br />

for parameter sensitivity<br />

analysis, model calibration<br />

and the analysis model results).<br />

Moreover, the SWAT website<br />

provides datasets for running<br />

the model even if specific datasets,<br />

with adequate spatial and<br />

temporal resolution, are always<br />

recommended for the study<br />

areas to be analysed. The datasets<br />

available have often global<br />

coverage and are relative to climate,<br />

soil, land use and Digital<br />

Elevation Models (DEMs).<br />

Look up tables are also supplied<br />

with QSWAT to properly<br />

match to standard legends the<br />

land use and soil codes.<br />

The four-steps process and the<br />

minimum set of data for running<br />

SWAT+<br />

The following spatial and tabular<br />

data are required for running<br />

SWAT+: Land use/cover, DEM,<br />

Soil data (hydrological group,<br />

clay, silt, sand), Climate data<br />

(temperature, precipitation,<br />

humidity, solar radiation, wind<br />

speed) and Hydrology (river<br />

discharge).<br />

QSWAT runs SWAT+ by following<br />

a four-step procedure: 1)<br />

Delineate watersheds, 2) Create<br />

Hydrologic Response Units<br />

(HRUs), 3) Edit inputs and run<br />

SWAT, and 4) Visualize.<br />

The first step deals with the<br />

definition of the watershed and<br />

its structure by extracting the<br />

channels and the watershed<br />

boundary by processing the<br />

DEM of the study area with<br />

the classical Terrain Analysis<br />

Using Digital Elevation Models<br />

(TauDEM) functions that<br />

divide the watershed in subbasins<br />

(areas with a principal<br />

stream channel). The second<br />

step involves the creation of<br />

the HRUs, lumped areas with<br />

the same combination of soil,<br />

topography, and land use, not<br />

spatially related to each other<br />

(Rathjens et al., 2016). The<br />

third step concerns the weather<br />

data selection and the set-up<br />

of model parameters. For instance,<br />

the latter are relative<br />

to the potential evapotranspiration<br />

method (e.g., Priestley-<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 13

FOCUS<br />

Taylor, Penman-Monteith or<br />

Hargreaves), curve number<br />

method for soil moisture, land<br />

use management and conservation<br />

practices. The fourth step<br />

is dedicated to the visualization<br />

of the results at basin and subbasin<br />

scale and to the outputs<br />

exploration for a given channel<br />

and gauge.<br />

The model can simulate daily,<br />

monthly, yearly, and average<br />

outputs for different model<br />

components (e.g., channel,<br />

aquifer, reservoir, etc.), for nutrient<br />

balance, water balance,<br />

plant weather and losses from<br />

the basin, HRUs and landscape<br />

units. Results can be printed<br />

and exported in different formats<br />

such as tabular or text<br />

structure.<br />

Calibration and validation<br />

After the SWAT+ run and the<br />

production of the outputs, model<br />

calibration and validation<br />

are strongly recommended.<br />

The model can be calibrated<br />

and validated for hydrologic,<br />

sediment, nitrogen, and phosphorus<br />

components. These<br />

last steps guide the user on<br />

the fine-tuning process of the<br />

model parameters to produce<br />

results coherent with the real<br />

watershed processes. It requires<br />

the collection of data on river<br />

discharge that are often missing<br />

for several watersheds or<br />

available in analogic format,<br />

moreover water quality data<br />

(e.g., sediments load, dissolved<br />

oxygen, nitrate and phosphorous<br />

concentrations, etc.) can<br />

be used. Alternative approaches<br />

for hydrology calibration may<br />

involve the use of evapotranspiration<br />

data from satellite data<br />

to overcome the lack of river<br />

discharge data from the gauge<br />

stations at the outlets. SWAT+<br />

allows several options for calibrating<br />

and validating the<br />

simulation under the different<br />

parametrizations defined by<br />

the users. A sensitivity analysis<br />

is quite common approach<br />

undertaken to pinpoint the<br />

main sensitive parameters and<br />

to reduce their redundancy during<br />

cal/val process. Literature<br />

review is always useful to start<br />

listing a set of common parameters<br />

affecting streamflow and<br />

sediment yield process. SWAT-<br />

Calibration and Uncertainty<br />

Programs (SWAT-CUP) is by<br />

Fig. 3 – Schematic representation of the hydrology outputs at watershed level through QSWAT.<br />

far the most known tool for<br />

assessing the sensitivity of parameters<br />

by providing several<br />

model evaluation techniques<br />

based on the relevant statistics<br />

(e.g., Pearson’s correlation<br />

coefficient, root mean square<br />

error (RMSE), etc.). Following<br />

the identification of the most<br />

sensitive parameters the calibration<br />

and validation phase<br />

are carried out by focussing on<br />

specific components (e.g., daily<br />

discharge). The time series of<br />

the available data (e.g., t0-tn) is<br />

divided to provide a reference<br />

period for the warm-up (e.g.,<br />

t0-t5), calibration (e.g., t6-t15)<br />

and validation (e.g., t16-tn).<br />

Finally, model performances<br />

are assessed by using classical<br />

statistic measures (e.g., RMSE).<br />

Calibration and validation can<br />

be long and tedious. Therefore,<br />

it is always recommended to<br />

follow a precise work protocol<br />

(Abbaspour et al. 2018).<br />

Extending model simulation capabilities:<br />

land use management<br />

and climate change<br />

The impact of alternative land<br />

uses, and climate change are<br />

pressing concerns in different<br />

regions of the world. SWAT+<br />

allows modelling diverse land<br />

use scenarios to meet sustainability<br />

goals (Pulighe et. al.,<br />

2020) through a module where<br />

alternative land management<br />

practices can be defined (e.g.,<br />

ploughing, seeding, tillage, irrigation,<br />

fertilization rates and<br />

crop nutrients uptake, etc.),<br />

by defining dates or specific<br />

land use classes and regions.<br />

Similarly, climate projections<br />

from climate models under different<br />

representative concentration<br />

pathways (RCPs) scenarios<br />

of greenhouses emissions can be<br />

loaded as weather data to create<br />

climate scenarios for the future<br />

decades that can be compared<br />

to the baseline period covering<br />

the historical meteorological<br />

data. SWAT+ can ingest these<br />

14 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

FOCUS<br />

data for simulating seasonal<br />

changes in precipitation and<br />

temperatures, hydrological extremes,<br />

flow regime alterations<br />

and river discharge, future water<br />

quality (i.e., nitrogen and<br />

phosphorus) and soil erosion<br />

conditions, and future biomass<br />

production. Estimating<br />

the mentioned effects on the<br />

hydrological regime might have<br />

strong impacts also on agricultural<br />

activities posing challenges<br />

to land use management and<br />

irrigation (Pulighe et al., 2021).<br />

Conclusions<br />

Open-source GIS (QGIS)<br />

and free to use models such<br />

as SWAT+ can be considered<br />

effective and strategic tools for<br />

monitoring and assessing water<br />

and soil interactions at the<br />

watershed level. In addition,<br />

the growing availability of public<br />

domain geospatial datasets<br />

can increase the applicability<br />

of the simulation of the watershed<br />

processes worldwide and<br />

for a wide variety of use cases.<br />

QSWAT will be a valuable<br />

tool for the SWAT scientific<br />

community thanks to the full<br />

integration with the geospatial<br />

functions, the new functionality<br />

offered by SWAT+ and<br />

the contribution of a wide and<br />

growing open-source community.<br />

QSWAT could be a powerful<br />

tool to assess the effects<br />

of climate change and land use<br />

management and the impacts<br />

on water quality and land degradation.<br />

We believe that in<br />

the near future, the evaluation<br />

of the effectiveness of policy interventions<br />

and the deployment<br />

of sustainable soil/water management<br />

practices will become<br />

an interesting arena for experimenting<br />

and acknowledging the<br />

potentiality of SWAT+.<br />


Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams,<br />

J. R. (1998). Large area hydrologic modeling<br />

and assessment part I: model development 1. JAWRA<br />

Journal of the American Water Resources Association,<br />

34(1), 73-89.<br />

Abbaspour KC, Vaghefi SA, Srinivasan R. A Guideline<br />

for Successful Calibration and Uncertainty<br />

Analysis for Soil and Water Assessment: A Review of<br />

Papers from the 2016 International SWAT Conference.<br />

Water. 2018; 10(1):6. https://doi.org/10.3390/<br />

w10010006<br />

Pulighe, G., Lupia, F., Chen, H., & Yin, H. (2021).<br />

Modeling Climate Change Impacts on Water Balance<br />

of a Mediterranean Watershed Using SWAT+. Hydrology,<br />

8(4), 157.<br />

Pulighe G, Bonati G, Colangeli M, Traverso L, Lupia<br />

F, Altobelli F, Dalla Marta A, Napoli M. Predicting<br />

Streamflow and Nutrient Loadings in a Semi-Arid<br />

Mediterranean Watershed with Ephemeral Streams<br />

Using the SWAT Model. Agronomy. 2020; 10(1):2.<br />

https://doi.org/10.3390/agronomy10010002<br />

Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I.,<br />

& Arnold, J. G. (2016). CMhyd user manual. Doc.<br />

Prep. Simulated Clim. Change Data Hydrol. Impact<br />

Study.<br />

https://www.card.iastate.edu/swat_articles/<br />

https://swat.tamu.edu<br />


QGIS; QSWAT; watershed; river basin; SWAT+;<br />

climate change<br />


The Soil and Water Assessment Tool (SWAT) enables<br />

the simulation of watershed and river basin quantity<br />

and quality of surface and ground water under the influence<br />

of land use, management, and climate change.<br />

It can be used to monitor and control soil erosion,<br />

non-point source pollution and basin management.<br />

The recent version (SWAT+) was implemented by<br />

a dedicated QGIS plugin (QSWAT) widening the<br />

userbase and the potential modelling application<br />

worldwide. QSWAT, along with additional software<br />

for preparing the input dataset and for performing<br />

the calibration/validation phase, further extends the<br />

watershed modelling capabilities. Such tools and the<br />

growing diffusion of public open geospatial datasets<br />

are expected to increase the range of applications especially<br />

with the availability climate projections datasets.<br />

The latter will enable users to simulate all the watersoil<br />

phenomena at watershed level under future conditions<br />

to better understand and plan suitable action for<br />

preserving the natural resources.<br />

AUTHOR<br />

Flavio Lupia<br />

flavio.lupia@crea.gov.it<br />

Giuseppe Pulighe<br />

giuseppe.pulighe@crea.gov.it<br />

CREA - Council for Agricultural Research<br />

and Economics - Roma (Italy)<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 15

REPORT<br />

Mobile Robotics and Autonomous<br />

Mapping: Technology for a more<br />

Sustainable Agriculture<br />

by Eleonora Maset, Lorenzo Scalera, Diego Tiozzo Fasiolo<br />

Fig. 1 - The mobile robot traversing under the canopy in a<br />

maize field (Manish et al., <strong>2022</strong>).<br />

Today, food systems account<br />

for nearly onethird<br />

of global greenhouse gas<br />

emissions, consume<br />

large amounts of natural<br />

resources and are among the<br />

causes of biodiversity loss.<br />

As part of the actions of<br />

the European Green<br />

Deal, the Farm to Fork<br />

Strategy (European Commission,<br />

2020) plays therefore a<br />

crucial role to reach the ambitious<br />

goal of making Europe a<br />

climate-neutral continent by<br />

2050. In fact, it aims to accelerate<br />

the transition towards a<br />

sustainable food system, reducing<br />

dependency on pesticides,<br />

decreasing excess fertilization<br />

and protecting land, soil, water,<br />

air, plant and animal health. All<br />

actors of the food chain need to<br />

contribute to the implementation<br />

of this strategy, starting<br />

from the transformation of<br />

production methods that can<br />

benefit from novel technological<br />

and digital solutions to deliver<br />

better environmental and climate<br />

results.<br />

In this context, we are witnessing<br />

an increasing demand for<br />

automated solutions to monitor<br />

and inspect crops and canopies,<br />

that are driving the adoption of<br />

autonomous and robotic systems<br />

with computational and<br />

logical capabilities. The introduction<br />

of robotics and automation,<br />

coupled with Geomatics<br />

techniques, could provide notable<br />

benefits not only in terms<br />

of crop production and land use<br />

optimization, but also to reduce<br />

the use of chemical pesticides,<br />

improving sustainability and<br />

climate performance through<br />

a more results-oriented model,<br />

based on the use of updated<br />

data and analyses. For these reasons,<br />

the implementation of autonomous<br />

and robotic solutions<br />

together with advanced monitoring<br />

techniques is becoming<br />

of paramount importance in<br />

view of a resilient and sustainable<br />

agriculture.<br />

Applications of mobile robotics<br />

in agriculture span from a large<br />

variety of tasks, as for instance,<br />

harvesting, monitoring, phenotyping,<br />

sowing, and weeding. A<br />

particular task in which mobile<br />

robots are currently employed<br />

at a faster pace than in previous<br />

years is 3D mapping, as testified<br />

by a flourishing literature<br />

on the topic (Tiozzo Fasiolo et<br />

al., <strong>2022</strong>). Indeed, 3D maps of<br />

agricultural crops can provide<br />

valuable information about the<br />

health, stress, presence of diseases,<br />

as well as morphological and<br />

biochemical characteristics. Furthermore,<br />

3D surveys of plants<br />

and crops are fundamental in<br />

the computation of geometrical<br />

information, such as volume<br />

and height, to be used to reduce<br />

pesticide and fertilizer waste<br />

and water usage, and, therefore,<br />

improve sustainability and environmental<br />

impact.<br />

Obviously, to provide useful<br />

information for crop management<br />

and to perform the survey<br />

in the most automatic way possible,<br />

robotics platforms must<br />

be equipped with appropriate<br />

technology. In the following, we<br />

will therefore try to summarize<br />

trends and future developments<br />

16 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

REPORT<br />

in this domain.<br />

The first requirement of mobile<br />

robots in agriculture applications<br />

is the availability of onboard<br />

sensors and computational capabilities.<br />

Common sensors are<br />

2D and 3D LiDAR (Light Detection<br />

and Ranging), cameras<br />

(monocular, stereo, RGB-D, and<br />

time-of-flight ones), as well as<br />

RTK-GNSS (Real-Time Kinematics<br />

Global Navigation Satellite<br />

System) receivers, and IMUs<br />

(Inertial Measurement Units),<br />

the latter two used mainly for<br />

localization tasks.<br />

Among the robotic systems recently<br />

proposed in the literature<br />

for 3D mapping in agriculture,<br />

it is worth mentioning the platform<br />

developed in (Manish et<br />

al., 2021) and shown in Figure<br />

1. That system is capable of collaborating<br />

with a drone to build<br />

a dense point cloud of the field.<br />

Another interesting mobile robot<br />

is BoniRob (Figure 2), developed<br />

by Bosch Deepfield® Robotics<br />

(Chebrolu et al., 2017). It is an<br />

omnidirectional robot and carries<br />

a multispectral camera, able<br />

to register four spectral bands,<br />

and an RGB-D sensor to capture<br />

high-resolution radiometric data<br />

about the inspected plantation.<br />

Multiple LiDAR sensors and<br />

GNSS receivers as well as wheel<br />

encoders provide at the same<br />

time observations employed for<br />

localization, navigation, and<br />

mapping. An example of robot<br />

with an onboard manipulator<br />

is given by BrambleBee (Ohi et<br />

al., 2018). That robotic system<br />

features a custom end effector<br />

designed to pollinating flowers<br />

in a greenhouse.<br />

Images from standard RGB<br />

cameras only supply information<br />

about the plants in the visible<br />

spectrum. To investigate vegetation<br />

indexes related to the crop<br />

vigor, as for instance the NDVI<br />

(Normalized Difference Vegetation<br />

Index), multispectral and<br />

Fig. 2 - Agricultural field robot BoniRob with onboard sensors (Chebrolu et al., 2017).<br />

hyperspectral sensors are needed,<br />

which can measure the near<br />

infrared radiation reflected by<br />

the vegetation leaves. However,<br />

only a paucity of robotic platforms<br />

described in the literature<br />

manage to perform this task.<br />

The mobile lab developed at<br />

the Free University of Bolzano<br />

and shown in Figure 3 is among<br />

them (Bietresato et al., 2016).<br />

A prototype of mobile robot for<br />

3D mapping in agriculture is<br />

currently being developed at the<br />

University of Udine, based on<br />

an Agile-X Robotics Scout 2.0<br />

platform (Figure 4). The robot<br />

can navigate in harsh terrain and<br />

narrow passages thanks to its<br />

four-wheel drive and differential<br />

kinematics. The platform is<br />

equipped with a low-cost GNSS<br />

receiver and a 9-degree-offreedom<br />

(DOF) IMU as direct<br />

georeferencing systems. Moreover,<br />

it features a great computational<br />

capability thanks to the<br />

NVIDIA Jetson AGX Xavier<br />

board, developed to exploit artificial<br />

intelligence (AI) algorithms<br />

even in embedded systems. The<br />

perception of the environment<br />

is guaranteed by a rotating 360°<br />

LiDAR and an RGB-D camera.<br />

Finally, for phenotyping purposes<br />

it exploits a multispectral<br />

camera pointing forward to<br />

acquire information on the near<br />

infrared and the red edge portion<br />

of the light spectrum.<br />

As far as the sensorial and<br />

computational capabilities are<br />

Fig. 3 - Agricultural robot developed at the Free University of Bolzano, Italy: robot in a orchard,<br />

and onboard sensors (Bietresato et al., 2016).<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 17

REPORT<br />

Fig. 4 - The sensorized mobile platform developed at University of Udine, Italy.<br />

considered, from the literature<br />

it can be noticed that most of<br />

the robotic platforms operating<br />

in the agricultural environment<br />

usually employ physical devices<br />

to store the acquired data, whose<br />

can require a frequent manual<br />

intervention of an operator. The<br />

implementation on Internet<br />

of Things (IoT) approaches,<br />

together with the storage of<br />

data on clouds could be a great<br />

improvement, making data<br />

remotely available. Future improvements<br />

in this context will<br />

also include the integration of<br />

renewable energy sources, such<br />

as solar panels, to increase the<br />

autonomy of the systems, especially<br />

in large scale operations<br />

(e.g., autonomous 3D mapping<br />

of a whole vineyard). Moreover,<br />

to avoid occlusion problems<br />

that can occur in image-based<br />

phenotyping, sensors can be<br />

mounted on a robotic arm that<br />

can optimize the camera pose,<br />

guaranteeing the best point of<br />

view for data acquisition. However,<br />

it should also be underlined<br />

that eye-in-hand configurations<br />

for LiDAR sensors and multispectral<br />

cameras are not exploited<br />

yet. A further important<br />

aspect is the durability of these<br />

systems and sensors, that should<br />

be designed to operate in severe<br />

outdoor scenarios.<br />

Fig. 5 - Person following with YOLO object detection (Masuzawa et al., 2017).<br />

To navigate autonomously in<br />

the surrounding environment, a<br />

mobile robot needs a robust localization<br />

method that can georeference<br />

the data acquired by<br />

means of the onboard sensors.<br />

Direct georeferencing methods<br />

are usually based on the RTK-<br />

GNSS, that provides position at<br />

low update rate, generally coupled<br />

with a 9-DOF IMU, which<br />

however is sensible to noise in<br />

rough terrains. Higher accuracy<br />

for the localization of the robot<br />

and the generated 3D map can<br />

be achieved using in addition<br />

Simultaneous Localization and<br />

Mapping (SLAM) approaches.<br />

As well-known also in the Geomatics<br />

community, SLAM problem<br />

consists in the estimation<br />

of the pose of the robot/sensor,<br />

while simultaneously building a<br />

map of the environment. Stateof-the-art<br />

methods are divided<br />

into two main groups: visual<br />

SLAM and LiDAR SLAM. The<br />

former approach relies on images<br />

and sequentially estimates<br />

the camera poses by tracking<br />

keypoints in the image sequence.<br />

The popular approach for Li-<br />

DAR SLAM is instead based<br />

on scan matching: the pose is<br />

retrieved by matching the newly<br />

acquired point cloud with the<br />

previously built map, which is<br />

constantly updated as soon as<br />

new observations are available.<br />

Although not yet fully implemented<br />

in mobile robots for precision<br />

agriculture applications,<br />

an optimal solution could be<br />

data fusion, taking the advantages<br />

of both visual and LiDAR<br />

SLAM methods. In addition,<br />

since external conditions can significantly<br />

vary among different<br />

application and the environment<br />

dictates the most advantageous<br />

sensor, the robot itself should be<br />

able to choose and use the most<br />

suited data source according to<br />

the environmental conditions.<br />

Many open-source SLAM algo-<br />

18 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

REPORT<br />

rithms are currently available,<br />

that can run in real time or in<br />

post-processing mode. To this<br />

regard, a comparison among<br />

the state-of-the-art SLAM algorithms<br />

could be interesting,<br />

together with a quantitative<br />

evaluation of the obtained 3D<br />

maps performed with respect<br />

to ground truth datasets. Conversely,<br />

there is a lack of methods<br />

to efficiently fuse spectral data<br />

acquired by multispectral and<br />

hyperspectral cameras with Li-<br />

DAR point clouds, fundamental<br />

for agriculture applications.<br />

Another important aspect that<br />

must be considered for the<br />

profitably application of mobile<br />

platforms is the autonomous<br />

navigation ability, guaranteed by<br />

path planning algorithms. Path<br />

planning is a mature field in<br />

mobile robotics, and, in the crop<br />

monitoring context, it generally<br />

consists of providing a global<br />

path to map an entire area. This<br />

approach is called coverage path<br />

planning and is usually coupled<br />

with a row-following algorithm<br />

to provide local velocity command<br />

to the robot.<br />

A recent trend in the coverage<br />

path planning is the development<br />

of algorithms that avoid<br />

repetitive paths to minimize soil<br />

compaction. This approach generally<br />

relies on prior information<br />

of the working area, that could<br />

be acquired thanks to the collaboration<br />

with drones, useful to<br />

capture an up-to-date 2D or 3D<br />

model of the environment. Furthermore,<br />

a promising solution<br />

could be extending the range<br />

of action with swarm robotics,<br />

that is the collaboration of several<br />

unmanned ground vehicles<br />

(UGVs).<br />

Nowadays, another fundamental<br />

aid for agricultural applications<br />

based on mobile robotics is given<br />

by artificial intelligence (AI). In<br />

fact, classification and segmentation<br />

algorithms of images and<br />

point clouds based on AI are increasingly<br />

used to enrich the 3D<br />

map with semantic information,<br />

also in real time. This is possible<br />

mostly thanks to the advances in<br />

the computational performance<br />

of modern embedded computers<br />

that can be installed onboard a<br />

mobile platform.<br />

For instance, a convolutional<br />

neural network (CNN) applied<br />

to the acquired images can provide<br />

bounding boxes of objects<br />

of interest, that can constitute<br />

the basis to build a topological<br />

map with key location estimation<br />

and semantic information.<br />

This is exploitable also to give<br />

the robot person following capability,<br />

as done in the work by<br />

(Masuzawa et al., 2017) (Figure<br />

5). Another example of machine<br />

learning application is given<br />

by the work in (Reina et al.,<br />

2017), which employed a support<br />

vector machine to classify<br />

the terrain on which the robot<br />

is navigating, by means of wheel<br />

slip, rolling resistance, vibration<br />

response experienced by the<br />

mobile platform and visual data.<br />

Recent trends in this field also<br />

comprise the use of generative<br />

adversarial networks to generate<br />

photorealistic agricultural images<br />

for model training, as well as<br />

the recognition of diseases with<br />

CNN.<br />

In the coming years we will<br />

witness great progress in all the<br />

domains highlighted by this<br />

work, from sensors to mobile<br />

platforms, from localization algorithms<br />

to artificial intelligence<br />

methods, with the hope that<br />

these innovations will effectively<br />

contribute to the transition to<br />

a more sustainable, healthy and<br />

environmentally-friendly food<br />

system.<br />


European Commission (2020). Farm to Fork<br />

strategy for a fair, healthy and environmentallyfriendly<br />

food system. https://ec.europa.eu/food/<br />

horizontal-topics/farm-fork-strategy_en<br />

Tiozzo Fasiolo, D., Scalera, L., Maset, E.,<br />

Gasparetto, A. (<strong>2022</strong>). Recent Trends in Mobile<br />

Robotics for 3D Mapping in Agriculture. In International<br />

Conference on Robotics in Alpe-Adria<br />

Danube Region (pp. 428-435). Springer, Cham.<br />

Manish, R., Lin, Y. C., Ravi, R., Hasheminasab,<br />

S. M., Zhou, T., Habib, A. (2021). Development<br />

of a miniaturized mobile mapping system<br />

for in-row, under-canopy phenotyping. Remote<br />

Sensing, 13(2), 276.<br />

Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter,<br />

W., Burgard, W., Stachniss, C. (2017).<br />

Agricultural robot dataset for plant classification,<br />

localization and mapping on sugar beet<br />

fields. The International Journal of Robotics<br />

Research, 36(10), 1045-1052.<br />

Ohi, N., Lassak, K., Watson, R., Strader, J., Du,<br />

Y., Yang, C., et al. (2018). Design of an autonomous<br />

precision pollination robot. In 2018 IEEE/<br />

RSJ international conference on intelligent robots<br />

and systems (IROS) (pp. 7711-7718). IEEE.<br />

Bietresato, M., Carabin, G., D'Auria, D., Gallo,<br />

R., Ristorto, G., Mazzetto, F., Vidoni, R., Gasparetto,<br />

A., Scalera, L. (2016). A tracked mobile<br />

robotic lab for monitoring the plants volume and<br />

health. In 2016 12th IEEE/ASME International<br />

Conference on Mechatronic and Embedded<br />

Systems and Applications (MESA). IEEE.<br />

Masuzawa, H., Miura, J., Oishi, S. (2017). Development<br />

of a mobile robot for harvest support<br />

in greenhouse horticulture—Person following<br />

and mapping. In 2017 IEEE/SICE International<br />

Symposium on System Integration (SII) (pp.<br />

541-546). IEEE.<br />

Reina, G., Milella, A., Galati, R. (2017). Terrain<br />

assessment for precision agriculture using vehicle<br />

dynamic modelling. Biosystems engineering, 162,<br />

124-139.<br />


Mobile robotics; autonomous mapping; sustainable<br />

agriculture<br />


The introduction of robotics and automation,<br />

coupled with Geomatics techniques, could<br />

provide notable benefits not only in terms of crop<br />

production and land use optimization, but also to<br />

reduce the use of chemical pesticides, improving<br />

sustainability and climate performance through<br />

a more results-oriented model, based on the use<br />

of updated data and analyses. For these reasons,<br />

the implementation of autonomous and robotic<br />

solutions together with advanced monitoring<br />

techniques is becoming of paramount importance<br />

in view of a resilient and sustainable agriculture.<br />

AUTHOR<br />

Eleonora Maset<br />

eleonora.maset@uniud.it<br />

Lorenzo Scalera<br />

lorenzo.scalera@uniud.it<br />

Diego Tiozzo Fasiolo<br />

diego.tiozzo@uniud.it<br />

Polytechnic Department of Engineering and<br />

Architecture (DPIA), University of Udine,<br />

Italy<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 19

REPORT<br />

Geographical Information: the Italian<br />

Scientific Associations and...<br />

the Big Tech<br />

by Valerio Zunino<br />

In Italy, the Scientific<br />

Associations that deal with<br />

geographical data are simply<br />

not up to speed, and just a few<br />

of them have some sort of idea<br />

on how to get into it. The huge<br />

and mind-bending projects<br />

that Big Tech is dealing with in<br />

our same world of professional<br />

expertise, must be firstly<br />

understood -deeply-, weighted<br />

and brought to the table of<br />

our heritage of knowledge,<br />

culture, education, and lastly<br />

of consulting services that our<br />

Associations are required to<br />

offer to the national market.<br />

These days, we can no<br />

longer afford to pretend<br />

we can just barely<br />

glimpse the revolutionary contribution<br />

that has been made<br />

to the world of professionals<br />

around the planet by the big<br />

techs (in our sector), among<br />

others, through their generalist<br />

geographic portals.<br />

Nor can we continue to brand<br />

as scientific approximation their<br />

method in strategically tackling<br />

that world that we, Scientific<br />

Associations, together with the<br />

most established companies<br />

in the sector, believed we were<br />

presiding over with the exclusivity<br />

of knowledge and the<br />

most advanced technology: the<br />

time it takes to access an immense<br />

amount of geographical<br />

data residing on the internet<br />

has soon become one tenth of<br />

what we used to expect, and our<br />

locking up within our Tender<br />

Special Specifications will simply<br />

make us less credible in the<br />

eyes of that growing audience of<br />

subjects that we call users, and<br />

which evidently represents the<br />

market of the Associates that we<br />

have a duty to approach, starting<br />

with small and mediumsized<br />

enterprises and ending<br />

with the individual professional<br />

who is struggling at work and<br />

in life. If we do not do this, we<br />

will disappear.<br />

The technological framework<br />

and business model outlined<br />

by those companies that today<br />

- whether one accepts it or<br />

not - mark the times, methods<br />

and rules of consultation and<br />

processing of the vast majority<br />

of published geographical data,<br />

and that have so far often been<br />

seen as a kind of obstruction<br />

to the scientific conversation,<br />

it should be clearly stated as of<br />

now that they will have to be<br />

carefully studied and if possible<br />

brought to the table of the<br />

Associations, so as to first and<br />

foremost qualify them. It is necessary<br />

to at least begin to show<br />

a general humility on the contents,<br />

seek an encounter with<br />

the Industry and attempt to<br />

generate a global, adjustable and<br />

- even more urgently- mutual<br />

learning, that is for us the only<br />

effective and possible means of<br />

sharing.<br />

On 11 June 2001, the global<br />

market witnessed the release of<br />

Google Earth. It took none of<br />

us 'insiders' more than a couple<br />

of minutes, the time to get to<br />

grips with the surprise effect,<br />

to measure an extraordinary<br />

performance, that was not com-<br />

20 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

REPORT<br />

parable (for how far it was superior)<br />

to that of products such<br />

as Autodesk Mapguide or the<br />

direct competitors of the time,<br />

positioned by ESRI, Intergraph<br />

or Bentley, at the time leading<br />

tools dedicated to the web consultation<br />

of raster and vector geographic<br />

data. The very concept<br />

of raster was debased in just a<br />

few moments, almost as if it<br />

had been overtaken by still unknown<br />

words, which had been<br />

able to refer to and perhaps<br />

even describe the practicality<br />

and simplicity of the dynamic<br />

and intelligent management of<br />

remote sensed images, which<br />

populated this formidable application<br />

capable of occupying<br />

the globe in a representation<br />

without geographical hesitation<br />

and continuity.<br />

Earth then remained essentially<br />

the same, integrating some<br />

interesting functionalities over<br />

time, which, however, could not<br />

affect the first violent impact of<br />

product innovation. It is a fact<br />

that the other Big Techs were<br />

not able to respond rapidly, and<br />

did not want to do so in the<br />

years immediately following.<br />

Consequently, it is precisely<br />

from 2001 onwards that Google<br />

began to dig a trench that for<br />

a while increased in width (in<br />

terms of the quantity of the<br />

information entered) and in<br />

depth (in terms of its quality<br />

and geographical accuracy);<br />

then, the depth was filled by a<br />

number of competitors, as a result<br />

of which the big geographical<br />

data market was hit by an<br />

unprecedented rivalry based on<br />

quality: an arms race that saw<br />

first Apple enter the game, with<br />

the initial and fundamental<br />

support of a very strong and acclaimed<br />

segment brand as Tom-<br />

Tom still is... then Amazon, and<br />

closely followed by Facebook<br />

and Microsoft, all of which,<br />

although leading the growth of<br />

a robust proprietary mapping<br />

sector, are to a greater or lesser<br />

extent still heavily anchored,<br />

and we are talking about truly<br />

significant investments, to a<br />

global geocartographic system,<br />

probably born (Joe Morrison)<br />

out of a conversation between<br />

recent graduates in an English<br />

pub in 2004, and whose commercial<br />

value is now out of control:<br />

OpenStreetMap.<br />

Of the interesting and singular<br />

reasons that have at the moment<br />

prevented the big corporations<br />

from replicating OSM,<br />

suggesting them instead to<br />

invest in it by bringing in their<br />

own teams of editors, we will<br />

perhaps speak on another occasion.<br />

What seems more on topic<br />

now, however, is to report on<br />

the evolutionary framework of<br />

OSM content at the hands of<br />

the big brands of the IT world.<br />

Within the geographic areas<br />

(States, regions, crucial Human<br />

Settlements, etc.) where the<br />

white collar teams of the big<br />

names are active, the average<br />

incidence of editors operating<br />

on a voluntary basis is today less<br />

than 25% of the entire OSM<br />

geographic road/building data<br />

operation, whereas in 2017 this<br />

figure was around 70% (Jennings<br />

Anderson). Now, given<br />

that the big corporations are<br />

in this way impoverishing the<br />

ideological path from which the<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 21

REPORT<br />

spirit of community that inspired<br />

the birth of the great free<br />

geographic portal had sprung,<br />

it remains to be seen what else<br />

these corporations are doing at<br />

the moment, each on their own<br />

account and more or less with<br />

the headlights off.<br />

Otherwise known as<br />

F.A.A.N.G. (Facebook,<br />

Amazon, Apple, Netflix and<br />

Google), sometimes with the<br />

inclusion of Microsoft, these<br />

are the Big Techs that for some<br />

time have been influencing our<br />

behavior, our choices, our purchases<br />

and probably even our<br />

attitudes.<br />

In the vicinity of Seattle (by the<br />

way, not without the support<br />

of the large Indian headquarters<br />

in Hyderabad, announced<br />

in September 2019 and now<br />

operating with about 15,000<br />

engineers, structured in an<br />

area of almost 170,000 square<br />

meters) they are working on at<br />

least two fronts (we are talking<br />

about mapping, of course):<br />

on the one hand, the Amazon<br />

Location Service project, born<br />

together with ESRI and HERE<br />

Technology B.V. from a rib of<br />

Amazon Web Service, the latter,<br />

today a cloud computing platform<br />

with a major competitive<br />

advantage over the analogues<br />

provided by Microsoft (Azure)<br />

and Google (GCP). The abovementioned<br />

partnership serves<br />

to fill the gap that Amazon, like<br />

the others, also suffers in terms<br />

of proprietary cartography (or<br />

acquired in perpetual license<br />

without disbursement of any<br />

fee for the benefit of the relevant<br />

suppliers): but while ESRI<br />

makes available to its partner<br />

some high-definition satellite<br />

databases, HERE contributes<br />

through the provision of its<br />

own geographic vectorial data<br />

referring in particular to road<br />

circulation, real-time traffic and<br />

address location.<br />

Of course, partners receive payment<br />

on an on-demand (click)<br />

basis whenever the Amazon<br />

Location Service user performs<br />

an operation on geography, processes<br />

a route request, performs<br />

a different search, etc... And it<br />

is also for this reason that Amazon<br />

is also moving on its own<br />

account, in order to free itself<br />

from such costs. As is typical for<br />

big brands, once the allurement<br />

of a market has been verified, it<br />

is considered a serious strategic<br />

mistake to wait too long before<br />

being present, consequently<br />

Amazon Location Services, like<br />

other platforms, simply had to<br />

be born, necessarily together<br />

with selected and established<br />

partners in the segment. So,<br />

the goal was to get into it right<br />

away, more or less, to steady a<br />

service and then innovate and<br />

improve it, just as it was battling<br />

on market share points<br />

against its longtime competitors.<br />

Before long, Amazon will<br />

reduce the quantitative contribution<br />

of its consume-based<br />

geographic data providers, and<br />

resubmit a quasi-proprietary<br />

version of Amazon Location<br />

Services on the most important<br />

element, the mapping system:<br />

you can bet on it.<br />

Facebook and Microsoft are also<br />

investing in the same endeavor<br />

to reduce the – as the present<br />

day - gross imbalance between<br />

third-party geographic data and<br />

data owned or acquired outright<br />

without conditions on publication;<br />

the platforms are called,<br />

for the uninitiated, Facebook<br />

Maps and Microsoft Azure<br />

Maps, respectively. But while<br />

Zuckenberg's creature (today<br />

“Meta”) does not seem to show<br />

any particular interest in a race<br />

for proprietary geographic information<br />

endowments - if not<br />

for certain categories or thematic<br />

classes - thereabout Redlands<br />

we are today witnessing an<br />

interesting acceleration, which<br />

also concerns 2D buildings,<br />

published and also made available<br />

in Opendata just in the last<br />

few weeks by the Bing subsidiary<br />

and with reference to a long<br />

series of countries, including<br />

Italy. The fullness of the data is<br />

good (we tested it for parts of<br />

our country) and the quality<br />

certainly more than decent.<br />

And while what happens in<br />

Mountain View is always<br />

22 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

REPORT<br />

somewhat surrounded by that,<br />

more or less, invisible aura of<br />

mystery that almost always intrigues,<br />

Cupertino, eventually,<br />

shows detail and quality with<br />

the famous proprietary map of<br />

California, representing and<br />

symbolizing down to individual<br />

plants, in some cities. Apple<br />

New Map is indeed the qualitative-quantitative<br />

manifesto,<br />

reporting the formidable global<br />

geographic wishes of the brand<br />

founded by Steve Jobs. From a<br />

strictly GIS-oriented point of<br />

view, it is the most ambitious<br />

project.<br />

In conclusion, will we, as Associations<br />

for the protection,<br />

dissemination, and comparison<br />

of geographic data in Italy, be<br />

able to keep up with the pace of<br />

a category credibility that envisages,<br />

without compromises, a<br />

greater openness of our awareness<br />

and a more convincing<br />

manifestation of our somewhat<br />

repressed humility?<br />

These are reactions that are neither<br />

easy nor quick, but necessary.<br />

To young people, who are<br />

entering the world of the Associations<br />

federated in ASITA we<br />

say and recommend that they<br />

open up, open their vision of<br />

the market that will one day be<br />

theirs alone, in the direction of<br />

others, Public sector, Big Tech,<br />

Professional world, international<br />

segment majors, national<br />

Industry, etc... Geoinformation,<br />

sooner or later, will have to<br />

become one: we better realize it<br />

sooner than later.<br />


ASITA; Geographic information; big<br />

tech; GIS; AMFM; location services<br />


The world is changing. The Italian<br />

Scientific Associations of Geographical<br />

Information are not. The opportunities<br />

are endless, but what is missing<br />

is humility and ideas, and often these<br />

two shortcomings feed off each other.<br />

AUTHOR<br />

Valerio Zunino<br />

valerio.zunino@studiosit.it<br />

Vice-President Association AMFM<br />

GIS Italia<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 23

Rhine River, Germany<br />

The Rhine River, the longest river in Germany, is<br />

featured in this colourful image captured by the Copernicus<br />

Sentinel-2 mission. The Rhine River, visible here in black, flows from<br />

the Swiss Alps to the North Sea through Switzerland, Liechtenstein, Austria,<br />

France, Germany, and the Netherlands. In the image, the Rhine flows from<br />

bottom-right to top-left. The river is an important waterway with an abundance of<br />

shipping traffic, with import and export goods from all over the world. The picturesque<br />

Rhine Valley has many forested hills topped with castles and includes vineyards, quaint<br />

towns and villages along the route of the river. One particular stretch that extends from Bingen<br />

in the south to Koblenz, known as the Rhine Gorge, has been declared a UNESCO World<br />

Heritage Site (not visible). Cologne is visible at the top of the image. This composite image was<br />

created by combining three separate Normalised Difference Vegetation Index (NDVI) layers from<br />

the Copernicus Sentinel-2 mission. The Normalised Difference Vegetation Index is widely used in<br />

remote sensing as it gives scientists an accurate measure of health and status of plant growth.Each<br />

colour in this week’s image represents the average NDVI value of an entire season between 2018<br />

and 2021. Shades of red depict peak vegetation growth in April and May, green shows changes in<br />

June and July, while blue shows August and September. Colourful squares, particularly visible<br />

in the left of the image, show different crop types. The nearby white areas are forested areas<br />

and appear white as they retain high NDVI values through most of the growing season,<br />

unlike crops which are planted and harvested at set time frames. Light pink areas are<br />

grasslands, while the dark areas (which have a low NDVI) are built-up areas and<br />

water bodies.<br />

[Credits: contains modified Copernicus Sentinel data (2018-21),<br />

processed by ESA - Translation: Gianluca Pititto]

REPORT<br />

Time and Longitude:<br />

an unexpected affinity<br />

by Marco Lisi<br />

Time, the fourth dimension, is becoming increasingly important in all<br />

aspects of technology and science.<br />

The generation and distribution of an accurate reference time is a<br />

strategic asset on which the most disparate applications depend: from<br />

financial transactions to broadband communications, from satellite<br />

navigation systems to large laboratories for basic physical research (the<br />

so-called "Big Physics ").<br />

But time is also the dimension through which technology evolves (as,<br />

for example, in the case of Moore's law which describes the increase in<br />

complexity of integrated electronic circuits) and obsolescence spreads.<br />

Obsolescence will be the great challenge, often ignored or<br />

underestimated, that the economically and technologically advanced<br />

societies of the world will have to face in the years to come. The more<br />

technology increases its evolutionary pace, the more things that<br />

surround us quickly become “old”, as they are no longer able to interface<br />

with each other and be maintained.<br />

Maintenance and updating of obsolete parts (the so-called “logistics”)<br />

are essential aspects in the operational life of a system and both have to<br />

do with time.<br />

The importance of a precise<br />

time reference in our society<br />

and economy<br />

The determination and the accurate<br />

measurement of time are<br />

the basis of our technological<br />

civilization. The major advances<br />

in this field have taken place<br />

in the last century, with the<br />

invention of the quartz crystal<br />

oscillator in 1920 and the first<br />

atomic clocks in the 40s. Nowadays<br />

time measurement is by far<br />

the most accurate among the<br />

measures of other fundamental<br />

physical quantities. Even the<br />

measurement unit for lengths,<br />

once based on the mythical reference<br />

meter, a sample of Platinum-Iridium<br />

preserved in Paris,<br />

was internationally redefined<br />

in 1983 as "the length of the<br />

path covered by light in vacuum<br />

during a time interval equal to<br />

1/299792458 of a second ".<br />

The second (symbol “s”) is the<br />

unit of measurement of the official<br />

time in the International<br />

System of Units (SI). Its name<br />

comes simply from the second<br />

division of the hour, while the<br />

minute is the first. The second<br />

was originally defined as the<br />

86400-th part of the mean solar<br />

day, i.e., the average, taken over<br />

a year, of the solar day, defined<br />

as the time interval elapsing between<br />

two successive passages of<br />

the Sun on the same meridian.<br />

In 1884 the Greenwich Mean<br />

Time (GMT) was officially<br />

established as the international<br />

standard of time, defined as the<br />

mean solar time at the meridian<br />

passing through the Royal<br />

Observatory in Greenwich<br />

(England).<br />

26 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

REPORT<br />

GMT calculates the time in each<br />

of the 24 zones (time zones) into<br />

which the earth's surface has<br />

been divided. The time decreases<br />

by one hour for each area west of<br />

Greenwich, and increases by one<br />

hour going east. GMT is also<br />

defined as "Z" time, or, in the<br />

phonetic alphabet, "Zulu" time.<br />

The time standard underlying<br />

the definition of GMT was<br />

maintained until astronomers<br />

discovered that the mean solar<br />

day was not constant, due to the<br />

slow (but continuous) slowdown<br />

of the Earth's rotation around<br />

its axis. This phenomenon is essentially<br />

linked to the braking<br />

action of the tides. It was then<br />

decided to refer the average solar<br />

day to a specific date, that of<br />

January 1, 1900. This solution<br />

was very impractical since it is<br />

not possible to go back in time<br />

and measure the duration of that<br />

particular day.<br />

In 1967 a new definition of the<br />

second was proposed, based on<br />

the motion of precession of the<br />

isotope 133 of Cesium. The second<br />

is now defined as the time<br />

interval equal to 9192631770<br />

cycles of the vibration of Cesium<br />

133. This definition allows scientists<br />

anywhere in the world<br />

to reconstruct the duration of<br />

the second with equal precision<br />

and the concept of International<br />

Atomic Time or TAI is based on<br />

it.<br />

The first atomic clock was developed<br />

in 1949 and was based<br />

on an absorption line of the<br />

ammonia molecule. The cesium<br />

clock, developed at the legendary<br />

NIST (National Institute<br />

of Standards and Technology)<br />

in Boulder, Colorado, can keep<br />

time with an accuracy better<br />

than one second in six million<br />

years. It was precisely the<br />

extreme accuracy of atomic<br />

clocks that led to the adoption<br />

of atomic time as an official reference<br />

worldwide. However, a<br />

Fig. 1 - UTC and critical infrastructures.<br />

Fig. 2 - Stonehenge, a prehistoric astronomical observatory.<br />

new problem was been indirectly<br />

generated: the discrepancy between<br />

the international reference<br />

of time, based as mentioned on<br />

atomic clocks, and the average<br />

solar time. An average solar year<br />

increases by about 0.8 seconds<br />

per century (i.e., about an hour<br />

every 450,000 years). Consequently,<br />

universal time accumulates<br />

a delay of approximately 1<br />

second every 500 days compared<br />

to international atomic time.<br />

This means that our distant<br />

great-grandchildren, in the<br />

distant future just 50,000 years<br />

from now, would read “noon” on<br />

their atomic clocks, even though<br />

they are actually in the middle of<br />

the night. To overcome this and<br />

many other more serious drawbacks,<br />

the concept of Universal<br />

Coordinated Time (UTC) was<br />

introduced in 1972, which definitively<br />

replaced GMT.<br />

In the short term, UTC essentially<br />

coincides with atomic time<br />

(called International Atomic<br />

Time, or TAI); when the difference<br />

between UTC and TAI approaches<br />

one second (this occurs<br />

approximately every 500 days),<br />

a fictitious second, called "leap<br />

second", is introduced.<br />

In this way, the two time-scales,<br />

TAI and UTC, are kept within<br />

a maximum discrepancy of 0.9<br />

seconds.<br />

UTC ("Universal Coordinated<br />

Time"), defined by the historic<br />

“Bureau International des Poids<br />

et Mesures” (BIPM) in Sevres<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 27

REPORT<br />

Fig. 3 - Ancient Egyptian stone obelisks.<br />

(Paris), is since 1972 the legal<br />

basis for the measurement of<br />

time in the world, permanently<br />

replacing the old GMT. It is<br />

derived from TAI, from which it<br />

differs only by an integer number<br />

of seconds. TAI is in turn<br />

calculated by BIPM from data<br />

of more than 200 atomic clocks<br />

located in metrology institutes<br />

in more than 30 countries over<br />

the world.<br />

But why is it so important to<br />

have an accurate and unambiguous<br />

definition of time?<br />

It is a matter not only for scientists<br />

and experts. A universally<br />

recognized and very accurate<br />

reference time is in fact at the<br />

base of most infrastructures of<br />

our society (figure 1).<br />

All cellular and wireless networks,<br />

for example, are based on<br />

careful synchronization of their<br />

nodes and base stations (obtained<br />

receiving GNSS signals,<br />

as we will see). The same is true<br />

for electric power distribution<br />

networks. Surprisingly, even<br />

financial transactions, banking,<br />

and stock markets all depend on<br />

an accurate time reference, given<br />

the extreme volatility in equity<br />

and currency markets, whose<br />

quotations might vary within a<br />

few microseconds.<br />

Time and its measurement<br />

The history of the measurement<br />

of time is as old as the history of<br />

human civilization.<br />

In prehistoric England, the megalithic<br />

monument of Stonehenge<br />

seems to have been a sophisticated<br />

astronomical observatory<br />

to determine the length<br />

of the seasons and the date of<br />

the equinoxes (figure 2).<br />

Already in 3500 BC the ancient<br />

Egyptians invented the<br />

sundial and erected stone obelisks<br />

throughout their country<br />

which had the primary purpose<br />

of marking the movement of<br />

the sun with their shadow and,<br />

therefore, the passage of time<br />

(figure 3).<br />

In ancient Roman times and up<br />

until late in the Medieval Age,<br />

sundials, marked candles, water<br />

and sand hourglasses were used<br />

to measure time (figure 4).<br />

A milestone in the history of the<br />

measurement of time was, in<br />

more recent times, Galileo's discovery,<br />

in 1583, of the constancy<br />

of the pendulum swing period,<br />

on which all mechanical clocks<br />

are based (figure 5).<br />

In 1656 Christiaan Huygens,<br />

Dutch mathematician, astronomer,<br />

and physicist (famous<br />

among other things for having<br />

defined the principle of diffraction<br />

that bears his name)<br />

designed the first weight-wound<br />

pendulum clock, which deviated<br />

by ten minutes a day (figure 6).<br />

But the major impetus for the<br />

development of ever more accurate<br />

techniques for measuring<br />

time came from the need to<br />

determine one's position (particularly<br />

longitude) aboard a<br />

ship in the open sea. From then<br />

on, time and positioning became<br />

irreversibly connected.<br />

Fig. 4 - Roman and Medieval time measurement methods.<br />

“Longitude problem” and<br />

measurement of time<br />

The latitude and longitude coordinate<br />

system is commonly used<br />

to determine and describe one’s<br />

position on Earth’s surface and it<br />

was also known by astronomers<br />

28 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

REPORT<br />

and navigators since the Greek<br />

and Roman times.<br />

Determining latitude north or<br />

south with respect to the equator<br />

posed no major problems:<br />

it could be calculated through<br />

angular measurements of the sun<br />

and stars made with relatively<br />

simple instruments.<br />

Measuring longitude, that is,<br />

identifying the east-west position<br />

on Earth between meridians,<br />

lines running from pole to pole,<br />

was a completely different story.<br />

Longitude was far more difficult<br />

than latitude to measure by astronomical<br />

observation.<br />

Because of the Earth’s rotation,<br />

the difference in longitude between<br />

two locations is equivalent<br />

to the difference in their local<br />

times: one degree of longitude<br />

equals a four-minute time difference,<br />

and 15 degrees is equal to<br />

one hour (making 360 degrees,<br />

or 24 hours, in total).<br />

While a sextant with which to<br />

determine the height of the sun<br />

at noon was sufficient to determine<br />

one's latitude, the determination<br />

of longitude, due to the<br />

earth's rotation, required the use<br />

of both the sextant and a very<br />

precise clock.<br />

Several methods had been<br />

proposed over the centuries by<br />

scientists and astronomers (including<br />

Galileo and Newton), all<br />

based on the observation of specific<br />

astronomical events, such as<br />

lunar eclipses.<br />

All these methods turned out<br />

to be rather cumbersome and<br />

inaccurate by several hundred<br />

kilometers.<br />

Even Christopher Columbus<br />

made two attempts to use lunar<br />

eclipses to discover his longitude,<br />

during his voyages to the<br />

New World, but his results were<br />

affected by large errors.<br />

The lack of an accurate longitude<br />

determination method created<br />

innumerable problems (at<br />

times, real disasters) for sailors of<br />

Fig. 5 - Galileo Galilei discovered in 1581 the isochronism of the pendulum.<br />

the 15th and 16th centuries.<br />

At the beginning of the eighteenth<br />

century, with the rapid<br />

growth of maritime traffic, a<br />

sense of urgency had arisen. The<br />

search for longitude cast a shadow<br />

over the life of every man at<br />

sea, and the safety of every vessel<br />

and merchant ship.<br />

The exact measurement of longitude<br />

seemed at that time an<br />

impossible dream, a sort of perpetual<br />

motion machine.<br />

There was a need for an instrument<br />

that recorded the time (at<br />

the place of departure) with the<br />

utmost precision during long sea<br />

voyages, despite the movement<br />

of the ship and the adverse climatic<br />

conditions of alternating<br />

Fig. 6 - Christiaan Huygens and the first pendulum clock.<br />

hot and cold, humid and dry.<br />

On the other hand, seventeenthcentury<br />

and early eighteenthcentury<br />

clocks were crude devices<br />

that usually lost or gained up<br />

to a quarter of an hour a day.<br />

The “longitude problem” however<br />

became so serious that in<br />

1714 the British Parliament<br />

formed a group of well-known<br />

scientists to study the solution,<br />

the “Board of Longitude”. The<br />

Board offered twenty thousand<br />

pounds, equivalent to more than<br />

three million pounds today, to<br />

anyone who could find a way<br />

to determine the longitude of a<br />

ship on the open sea with accuracy<br />

within one-half of a degree<br />

(thirty nautical miles, about<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 29

REPORT<br />

Figure 7: Right: John Harrison – Left (clockwise): H1 thru H4 Harrison’s chronometers<br />

Fig. 8 - James Cook’s Pacific Voyages.<br />

Fig. 9 - Galileo Passive Hydrogen Maser (PHM) clock.<br />

fifty-five kilometers, at the equator).<br />

The approach was successful,<br />

despite the many (and often<br />

completely crazy) proposals. In<br />

fact, in 1761, a self-educated<br />

Yorkshire carpenter and amateur<br />

clock-maker named John Harrison<br />

built a special mechanical<br />

clock to be loaded on board<br />

ships, called the “marine chronometer”,<br />

capable of losing or<br />

gaining no more than one second<br />

per day (an incredible accuracy<br />

for that time) (figure 7).<br />

Harrison did not receive the<br />

prize from the Board until after<br />

fighting for his reward, finally<br />

receiving payment in 1773, after<br />

the intervention of the British<br />

parliament.<br />

And it was thanks to a copy of<br />

Harrison's H4 chronometer<br />

that Captain James Cook made<br />

his second and third legendary<br />

explorations of Polynesia and<br />

the Pacific islands on board the<br />

HMS Resolution (figure 8).<br />

A copy of the H4 chronometer<br />

was also used in 1787 by<br />

Lieutenant William Bligh, commander<br />

of the famous HMS<br />

Bounty, but it was retained by<br />

Fletcher Christian following his<br />

mutiny. It was later recovered<br />

in Pitcairn Island to eventually<br />

reach the National Maritime<br />

Museum in London.<br />

GNSS and Timing<br />

An extremely accurate UTC reference<br />

is today provided worldwide<br />

by satellite navigation<br />

systems (GNSS) such as GPS<br />

(Global Positioning System),<br />

GLONASS, Beidou, and the<br />

European Galileo system. They<br />

are systems of satellites orbiting<br />

around the Earth, each containing<br />

onboard extremely precise<br />

atomic clocks which are all synchronized<br />

to a system reference<br />

clock.<br />

GNSS technologies are intrinsically<br />

linked to accurate timing.<br />

30 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

REPORT<br />

This is because of the specific<br />

principle (trilateration) on which<br />

position determination is based,<br />

i.e., the method of measuring<br />

the distance of a user from each<br />

satellite, involving the measurement<br />

of the time delay experienced<br />

by the signal-in-space.<br />

The most accurate and numerous<br />

atomic clocks around the<br />

world are those belonging to<br />

GNSS, thus contributing substantially<br />

to the derivation of<br />

TAI and UTC (figure 9).<br />

UTC can be derived from<br />

the Galileo and GPS signals,<br />

through a series of corrections<br />

based on data provided by the<br />

signals themselves. The accuracy<br />

obtainable, even with very cheap<br />

commercial receivers (or in<br />

those integrated into our smartphones)<br />

is easily better than one<br />

microsecond.<br />


GNSS; GPS; Galileo; GLONASS; Beidou; time; longitude; GMT; TAI; UTC;<br />


To have an accurate and unambiguous definition of time is a matter not only for scientists<br />

and experts. A universally recognized and very accurate reference time is in fact at the<br />

base of most infrastructures of our society. All cellular and wireless networks, for example,<br />

are based on careful synchronization of their nodes and base stations (obtained receiving<br />

GNSS signals, as we will see). The same is true for electric power distribution networks.<br />

Surprisingly, even financial transactions and banking and stock markets all depend on<br />

an accurate time reference, given the extreme volatility in equity and currency markets,<br />

whose quotations might vary within a few microseconds. The history of the measurement<br />

of time is as old as the history of human civilization. But the major impetus for the<br />

development of ever more accurate techniques for measuring time came from the need to<br />

determine one's position (particularly longitude) aboard a ship in the open sea. In 1761,<br />

a self-educated Yorkshire carpenter and amateur clock-maker named John Harrison built<br />

a special mechanical clock to be loaded on board ships, called the “marine chronometer”,<br />

capable of losing or gaining no more than one second per day (an incredible accuracy for<br />

that time). From then on, time and positioning became irreversibly connected.<br />

AUTHOR<br />

Marco Lisi<br />

ingmarcolisi@gmail.com<br />

Time to THE FUSION !<br />


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GeoMax provides the solution to increase your efficiency and accuracy.<br />

BLK2GO, a handheld 3D imaging scanner, captures models and point clouds and<br />

X-PAD OFFICE FUSION, GeoMax geodata office software, processes them in a few clicks.<br />

©<strong>2022</strong> Hexagon AB and/or its<br />

subsidiaries and affiliates.<br />

All rights reserved.<br />

Part of Hexagon<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 31<br />

Works when you do

NEWS<br />

in January 2021. In<br />

December 2020, the<br />

Space Development<br />

Agency selected L3Harris<br />

to build and launch four<br />

space vehicles to demonstrate<br />

the capability to<br />

detect and track ballistic<br />

and hypersonic missiles.<br />





L3Harris is providing the instrument as part of a<br />

wide-field-of-view satellite that also will help inform<br />

future space-based missile defense missions<br />

and architectures. The satellite will be positioned<br />

22,000 miles from Earth, enabling the infrared system<br />

to see a wide swath and patrol a large area for<br />

potential missile launches.<br />

“The L3Harris instrument can stare continuously<br />

at a theater of interest to provide ongoing information<br />

about the battlespace, which is an improvement<br />

over legacy systems,” said Ed Zoiss,<br />

President, Space & Airborne Systems, L3Harris.<br />

“It also provides better resolution, sensitivity and<br />

target discrimination at a lower cost.”<br />

The instrument was built for Space Systems<br />

Command and is integrated into a Millennium<br />

Space Systems satellite, scheduled to launch from<br />

Cape Canaveral, Florida. The payload, which is<br />

more than six feet tall and weighs more than 365<br />

pounds, was developed in Wilmington, Mass.<br />

L3Harris is prioritizing investments in space-based<br />

missile defense programs and has accelerated<br />

the development of resilient, end-to-end satellite<br />

solutions in spacecraft, payloads and ground software,<br />

and advanced algorithms.<br />

In a related effort, the Missile Defense Agency<br />

awarded L3Harris a missile-tracking study contract<br />

in 2019 and the prototype demonstration<br />

About L3Harris<br />

Technologies<br />

L3Harris Technologies is<br />

an agile global aerospace<br />

and defense technology<br />

innovator, delivering<br />

end-to-end solutions that<br />

meet customers’ missioncritical<br />

needs. The company<br />

provides advanced defense and commercial<br />

technologies across space, air, land, sea and cyber<br />

domains. L3Harris has more than $17 billion in<br />

annual revenue and 47,000 employees, with customers<br />

in more than 100 countries. L3Harris.<br />

com.<br />

Forward-Looking Statements<br />

This press release contains forward-looking statements<br />

that reflect management's current expectations,<br />

assumptions and estimates of future performance<br />

and economic conditions. Such statements<br />

are made in reliance upon the safe harbor provisions<br />

of Section 27A of the Securities Act of 1933<br />

and Section 21E of the Securities Exchange Act of<br />

1934. The company cautions investors that any<br />

forward-looking statements are subject to risks<br />

and uncertainties that may cause actual results<br />

and future trends to differ materially from those<br />

matters expressed in or implied by such forwardlooking<br />

statements. Statements about the value or<br />

expected value of orders, contracts or programs<br />

are forward-looking and involve risks and uncertainties.<br />

L3Harris disclaims any intention or obligation<br />

to update or revise any forward-looking<br />

statements, whether as a result of new information,<br />

future events, or otherwise.<br />

32 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

NEWS<br />



Emlid announced new PPK software—Emlid Studio. It’s a<br />

cross-platform desktop application designed specifically for<br />

post-processing GNSS data. The app is free and available<br />

for Windows and Mac users.<br />

Emlid Studio features a simple interface that makes postprocessing<br />

easier than ever. The app allows users to convert<br />

raw GNSS logs into RINEX, post-process static and kinematic<br />

data, geotag images from drones, including DJI, and<br />

extract points from the survey projects completed with the<br />

ReachView 3 app.<br />

With Emlid Studio, you can post-process data recorded<br />

with Emlid Reach receivers and other GNSS receivers<br />

or NTRIP services. For post-processing, you will need<br />

RINEX observation and navigation files. You can also use<br />

raw data in the UBX and RTCM3 format—Emlid Studio<br />

will automatically convert them into RINEX.<br />

The post-processing workflow is very straightforward.<br />

You can receive precise positioning of a single point or<br />

track depending on your positioning mode. Just add several<br />

RINEX files and enter the antenna height. Click the<br />

Process button, and Emlid Studio will do the rest. Once<br />

the resulting position file is ready, you will see the result<br />

on the plot.<br />

One more tool is available for the users of Reach receivers<br />

and the ReachView 3 app. The Stop & Go feature allows<br />

you to improve the coordinates of points collected in Single<br />

or Float modes.<br />

Another helpful feature is geotagging for drone mapping.<br />

To add geotags to the images’ EXIF data, you’ll need aerial<br />

photos and the POS file with the events. Emlid Studio also<br />

provides a chance to update your data from the RTK drone<br />

in case you had a float or single solution during your<br />

survey. You will need a set of RINEX logs from a base and<br />

drone, MRK file, and images from the drone. Just drag and<br />

drop data in the file slots and you’ll see the result in a few<br />

seconds.<br />

To start using Emlid Studio, simply download the app for<br />

your computer—either Windows or macOS. To learn<br />

more about Emlid Studio features, visit the Emlid website.<br />

There's life in our world<br />

We transform and publish data, metadata<br />

and services in conformance to INSPIRE<br />

We support Data Interoperability,<br />

Open Data, Hight Value Datasets,<br />

APIs, Location Intelligence, Data Spaces<br />

INSPIRE Helpdesk<br />

We support all INSPIRE implementers<br />

Epsilon Italia S.r.l.<br />

Viale della Concordia, 79<br />

87040 Mendicino (CS)<br />

Tel. (+39) 0984 631949<br />

info@epsilon-italia.it<br />

www.epsilon-italia.it<br />

www.inspire-helpdesk.eu<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 33

NEWS<br />




Topcon Positioning Germany is one of 22 partners involved<br />

in CampusOS; a research project with the goal of developing<br />

a modular ecosystem for open 5G campus networks<br />

based on open radio technologies and interoperable<br />

network components. As part of the German technology<br />

program titled "Campus networks based on 5G communication<br />

technologies," innovative solutions for open 5G<br />

networks are being developed and tested in conjunction<br />

with the German Federal Ministry for Economic Affairs<br />

and Climate Protection. The program was launched at the<br />

beginning of <strong>2022</strong> and will run through 2025.<br />

The use of artificial intelligence in the operation of autonomous<br />

plants and construction machinery requires the<br />

highest level of digital sovereignty. If Construction 4.0, including<br />

far-reaching automation, is to become a reality in<br />

Germany and the rest of the world, the processes of such<br />

data-driven solutions must run reliably, quickly and autonomously.<br />

Sponsored by the Federal Ministry of Economics<br />

The German Federal Ministry for Economic Affairs and<br />

Climate Protection is providing around 18.1 million euros<br />

in funding for the technology program over the next three<br />

years, which will cost around 33 million euros in total. The<br />

Fraunhofer Institutes FOKUS and HHI are coordinating<br />

the project. 22 partners from industry and research are involved.<br />

They include Deutsche Telekom, Siemens, Robert<br />

Bosch and more.<br />

"To enable companies to operate their own campus networks,<br />

certain requirements must be met; from standardized<br />

technology building blocks to network structures. As the<br />

sole representative of the construction industry, Topcon<br />

will test the technologies on reference test sites and therefore,<br />

will help shape the solutions for the future," explains<br />

Ulrich Hermanski, Chief Marketing Officer of the Topcon<br />

Positioning Group. "We look forward to working with our<br />

research partners to take the digital construction site to the<br />

next level."<br />

The future of the construction industry is digital<br />

With this research project, construction companies will one<br />

day be able to operate plants and machinery autonomously<br />

in open campus networks. This will allow the fluid and<br />

uninterrupted monitoring of construction sites in real time,<br />

as well as the networking of all sensors and construction<br />

machines in use on construction sites.<br />

Completely autonomous from public networks, 5G technology<br />

guarantees seamless machine-to-machine communication<br />

and transmits data ten times faster than 4G.<br />

The campus networks required for this, based on 5G frequencies,<br />

are practically digital ecosystems. They operate<br />

with open radio technologies and dialog-enabled components.<br />

The campus networks are geographically limited and<br />

can operate on a factory floor or on a construction site.<br />

Hermanski explains: "We will put a lot of time and energy<br />

into this project, because 5G campus networks are an important<br />

key technology for the construction site of the future."<br />

Lead project CampusOS: The consortium and its partners<br />

In addition to Topcon Deutschland Positioning GmbH,<br />

the collaborative partners of the CampusOS lead project include:<br />

atesio GmbH, brown-iposs GmbH, BISDN GmbH,<br />

Robert Bosch GmbH, Deutsche Telekom AG, EANTC<br />

AG, Fraunhofer Institutes FOKUS and HHI (project coordinators),<br />

GPS Gesellschaft für Produktionssysteme<br />

GmbH, highstreet technologies GmbH, Kubermatic<br />

GmbH, MUGLER SE, Node-H GmbH, Rohde & Schwarz<br />

GmbH, rt-solutions. de GmbH, Siemens AG, Smart<br />

Mobile Labs AG, STILL GmbH, SysEleven GmbH, the<br />

Technical University of Berlin and the Technical University<br />

of Kaiserslautern.<br />

About Topcon Positioning Group<br />

Topcon Positioning Group is an industry leading designer,<br />

manufacturer and distributor of precision measurement<br />

and workflow solutions for the global construction, geospatial<br />

and agriculture markets. Topcon Positioning Group<br />

is headquartered in Livermore, California, U.S. (topconpositioning.com,<br />

LinkedIn, Twitter, Facebook). Its European<br />

head office is in Capelle a/d IJssel, the Netherlands. Topcon<br />

Corporation (topcon.com), founded in 1932, is traded on<br />

the Tokyo Stock Exchange (7732).<br />

34 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

NEWS<br />



WORLD<br />




Veranstalter / Host: DVW e.V.<br />

Ausrichter Conference / Conference organiser: DVW GmbH<br />

Ausrichter Expo / Expo organiser: HINTE GmbH<br />


<strong>GEOmedia</strong> n°3-<strong>2022</strong> 35

NEWS<br />




The purpose of the AMPERE (Asset Mapping Platform for<br />

Emerging countRies Electrification) project is to provide<br />

a dedicated solution for electrical power network information<br />

gathering. AMPERE can actually support decision making<br />

actors (e. g. institutions and public/ private companies<br />

in charge to manage electrical network) to collect all needed<br />

info to plan electrical network maintenance and upgrade.<br />

In particular, the need for such a solution comes in emerging<br />

countries where, despite global electrification rates are<br />

significantly progressing, the access to electricity is still far<br />

from being achieved in a reliable way. Indeed, the challenge<br />

facing such communities goes beyond the lack of infrastructure<br />

assets: what is needed is a mapping of already deployed<br />

infrastructure (sometime not well known!) in order<br />

to perform holistic assessment of the energy demand and<br />

its expected growth over time. In such a context, Galileo<br />

is a key enabler -especially, considering its free-of-charge<br />

High Accuracy Service (HAS) and its highly precise E5<br />

AltBOC code measurements- as a core component to map<br />

electric utilities, optimise decision making process about the<br />

network development and therefore increase time and cost<br />

efficiency, offering more convenient way to manage energy<br />

distribution. These aspects confer to the AMPERE project<br />

a worldwide dimension, having European industry the clear<br />

role to bring innovation and know-how to allow network<br />

intervention planning with a limited afforded financial risk<br />

above all for emerging non-European countries.<br />

AMPERE proposes a solution based on a GIS Cloud mapping<br />

technology, collecting on field data acquired with<br />

optical/thermal cameras and LIDAR installed on board a<br />

Remote Piloted Aircraft (RPA). In particular, an RPA will<br />

be able to fly over selected areas performing semi-automated<br />

operations to collect optical and thermal images as well<br />

as 3D LiDAR-based reconstruction products. Such products<br />

are post processed at the central cloud GIS platform<br />

allowing operators in planning and monitoring activities<br />

by means of visualization and analytics tools can resolve<br />

data accessibility issues and improve the decision-making<br />

process. On this context, EGNSS represents an essential<br />

technology ensuring automated operations in a reliable<br />

manner and guaranteeing high performance.<br />

AMPERE use Galileo advanced features -namely, High<br />

Accuracy Service (HAS) and E5 AltBOC- as a core element<br />

of the added-value asset mapping proposition. The nature<br />

of HAS is fitting very well the requirements of this application,<br />

especially due to the re-shaping of the once fee-based<br />

accuracy capability to an open, free-of-charge service delivering<br />

around 20 centimeter accuracy, versus the belowten-centimeters<br />

PPP services, in lower convergence time.<br />

The key of Galileo HAS stands upon the high bandwidth of<br />

its E6-B channel, well suited to transmit PPP information,<br />

especially relevant for satellite clock corrections, which are<br />

not as stable in the medium and long term as the orbits.<br />

Additionally, the use of E5 AltBOC pseudo-ranges (which<br />

are cm-level precise with maximum multipath effects in the<br />

order of 1 m) supports fast ambiguity resolution for carrier<br />

phase observations. The Alternative BOC, (AltBOC)<br />

modulation on E5, is one of the most advanced signals the<br />

Galileo satellites transmit. Galileo receivers capable of tracking<br />

this signal will benefit from unequalled performance<br />

in terms of measurement accuracy and multipath suppression.<br />

The market is responding actively and positively to multi-frequency<br />

enhanced capabilities provided by Galileo.<br />

Around 40% of receiver models on the market are now<br />

multi-frequency. Also, in the mass market, with the launch<br />

of the world’s first dual-frequency GNSS smartphone by<br />

Xiaomi, and u-blox, STM, Intel and Qualcomm launching<br />

their first dual-frequency products earlier this year, multifrequency<br />

is becoming a reality for user needing increased<br />

accuracy.<br />

More information on: https://h2020-ampere.eu<br />

36 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

NEWS<br />



CHC Navigation (CHCNAV) today announced the availability<br />

of the i73+ Pocket GNSS receiver. The i73+ is a compact,<br />

powerful and versatile GNSS receiver with an integrated UHF<br />

modem which can be used indifferently as a base station or<br />

rover. Powered by 624 full GNSS channels and the latest iStar<br />

technology, it delivers survey-grade accuracy in all job site configurations.<br />

"Building on the legacy of the i73 GNSS, the new i73+ receiver<br />

is designed to maintain its proven compact and lightweight<br />

concept, but additionally adds the ability to be operated<br />

as either a GNSS RTK base station or a rover.” said Rachel<br />

Wang, Product Manager of CHC Navigation's Surveying and<br />

Engineering Division. “To enable this extra feature, we have<br />

built in the latest UHF modem technology allowing the reception<br />

and transmission of RTK corrections without sacrificing<br />

receiver size and power consumption.”<br />

Integrated Tx/Rx UHF modem extends the i73+ capacity<br />

The i73+ has a built-in transceiver radio module compatible<br />

with major radio protocols, making it a perfect portable builtin<br />

UHF base and rover kit with fewer accessories. The i73+ is<br />

a highly productive NTRIP rover when used with a handheld<br />

controller or tablet and connected to a GNSS RTK network<br />

via CHCNAV LandStar field software.<br />

Best-in-class technology with 624-channels advanced tracking<br />

The integrated advanced 624-channel GNSS technology takes<br />

advantage of GPS, Glonass, Galileo<br />

and BeiDou, in particular the latest<br />

BeiDou III signal, and provides robust<br />

data quality at all times. The<br />

i73+ extends GNSS surveying capabilities<br />

while maintaining centimeterlevel<br />

survey-grade accuracy.<br />

Built-in IMU technology highly<br />

enhances surveyors’ work efficiency<br />

With its IMU compensation ready<br />

in 3 seconds, the i73+ delivers 3 cm<br />

accuracy at up to 30 degrees pole tilt, increasing point measurement<br />

efficiency by 20% and stakeout by 30%. Surveyors are<br />

able to extend their working boundary near trees, walls, and<br />

buildings without the use of a total station or offset measurement<br />

tools.<br />

Compact design, only 0.73kg including battery<br />

The i73+ is the lightest and smallest receiver in its class, weighing<br />

only 0.73 kg including battery. It is almost 40% lighter<br />

than traditional GNSS receivers and easy to carry, use and<br />

operate without fatigue. The i73+ is packed with advanced<br />

technology, fits in hands and offers maximum productivity for<br />

GNSS surveys.<br />

Learn more about i73+: https://chcnav.com/product-detail/<br />

i73+-imu+-+rtk-gnss<br />





METAGEO is an easy to use map (GIS)<br />

platform that brings imagery, maps,<br />

Digital Twins, and sensor data into one<br />

3D universe, and then streams to any<br />

internet enabled device, or metaverse<br />

platform. The new GIS platform aim to<br />

enable organizations of all sizes to host,<br />

analyze, find and share 3D map datasets<br />

between any internet-capable device.<br />

The platform processes any locationbased<br />

map or sensor data from the real<br />

world, combines it into a single 3D virtual<br />

environment and streams it to any<br />

device or Metaverse platform.Today’s<br />

traditional GIS platforms are expensive,<br />

primarily offer 2D mapping features, are<br />

highly complicated and often require an<br />

advanced degree to master. 3D map and<br />

scan datasets are large, expensive and<br />

often hidden. Furthermore, these large<br />

files are often unsuitable for viewing<br />

on mobile devices or rendering in AR/<br />

VR environments. METAGEO addresses<br />

these issues with an affordable and<br />

easy-to-use platform that can load data<br />

from multiple sources. These sources include<br />

satellites, drones, mobile devices,<br />

public and crowdsourced repositories,<br />

IoT sensor data, 3D models and topographic<br />

maps. The data is then processed<br />

by the METAGEO platform into a<br />

3D world and streamed to any internetconnected<br />

device, enabling live collaboration<br />

between the office and field via<br />

mobile or AR device. Key innovations<br />

in the METAGEO 3D map platform<br />

include:Fast and intuitive multi-user interface<br />

for easy data sharing and collaborationAggregation<br />

of map and locationbased<br />

data from a multitude of sources<br />

on a global scaleSeamlessly import and<br />

sync data from multiple different systems<br />

into a single platformEasily host<br />

and stream large datasets between internet-connected<br />

devicesProvide ability to<br />

find open source and private dataPlugin<br />

SDK will allow for 3rd party tools to<br />

scale and fit any user needsMETAGEO<br />

has been designed for a wide range of applications<br />

in academia, architecture, engineering,<br />

construction, energy, natural<br />

resource management, environmental<br />

monitoring, utilities, and public safety,<br />

among others. The platform uses include<br />

planning and managing construction<br />

sites, organizing the layouts of events,<br />

maps for disaster management, public<br />

safety, visualizing inspection imagery<br />

from drones and mobile devices, and<br />

much more.“After working with 3D<br />

map data for several years, it became apparent<br />

that there was no easy way to share<br />

big datasets with those who need the<br />

information most, those with the boots<br />

on the ground,”said Paul Spaur, Founder<br />

of METAGEO. “Now with the rapid<br />

advancement of mobile hardware, and<br />

using advanced processing techniques,<br />

we can now leverage this data in real life,<br />

and in the metaverse.” METAGEO will<br />

be offered in several affordable subscription<br />

tiers, including Free Single User,<br />

Free Educational, Standard, Commercial<br />

and Enterprise. Each tier provides added<br />

features and<br />

benefits, enabling organizations to scale.<br />

METAGEO is available to a limited<br />

number of beta subscribers.Interested<br />

parties can get started today at www.metageo.iowww.geoforall.it/kcax<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 37

NEWS<br />




Leica Geosystems, part of Hexagon, announced today<br />

the introduction of Leica Chiroptera-5, the new highperformance<br />

airborne bathymetric LiDAR sensor for<br />

coastal and inland water surveys. This latest mapping<br />

technology increases the depth penetration, point density<br />

and topographic sensitivity of the sensor compared<br />

to previous generations. The new system delivers highresolution<br />

LiDAR data supporting numerous applications<br />

such as nautical charting, coastal infrastructure<br />

planning, environmental monitoring as well as landslide<br />

and erosion risk assessments.<br />

Higher sensor performance enables<br />

more cost-effective surveys<br />

Chiroptera-5 combines airborne bathymetric and topographic<br />

LiDAR sensors with a 4-band camera to collect<br />

seamless data from the seabed to land. Thanks to higher<br />

pulse repetition frequency (PRF), the new technology<br />

increases point density by 40% compared to the previous<br />

generation system, collecting more data during<br />

every survey flight. Improved electronics and optics<br />

increase water depth penetration by 20% and double<br />

the topographic sensitivity to capture larger areas of<br />

submerged terrain and objects with greater detail. The<br />

high-performance sensor is designed to fit a stabilising<br />

mount, enabling more efficient area coverage which decreases<br />

operational costs and carbon footprint of mapping<br />

projects.<br />

Leica Geosystems’ signature bathymetric workflow supports<br />

the sensor’s performance. Introducing near realtime<br />

data processing enables coverage analysis immediately<br />

after landing, allowing operators to quality control<br />

the data quickly before demobilising the system.<br />

The Leica LiDAR Survey Studio (LSS) processing suite<br />

provides full waveform analysis and offers automatic calibration,<br />

refraction correction and data classification,<br />

as well as advanced turbid water enhancement.<br />

Expanding bathymetric application portfolio to support<br />

environmental research<br />

Combining superior resolution, depth penetration and<br />

topographic sensitivity, Chiroptera-5 provides substantial<br />

benefits for various environmental applications like<br />

shoreline erosion monitoring, flood simulation and<br />

prevention and benthic habitat classification.<br />

Bundled with the FAAS/EASA certified helicopter pod,<br />

the system enables advanced terrain-following flying<br />

paths for efficient river mapping and complex coastlines<br />

surveys. Owners of previous generation systems are<br />

offered an easy upgrade path to Chiroptera-5 to add<br />

capabilities to their existing sensor and leverage their<br />

initial investment.<br />

“The first generation Chiroptera airborne sensor was<br />

flown in 2012. During its ten years of operation, the<br />

system has seen constant evolution that continuously<br />

improved the productivity and efficiency of the entire<br />

bathymetric surveying industry,” says Anders Ekelund,<br />

Vice President of Airborne Bathymetry at Hexagon.<br />

“By collecting detailed data of coastal areas and inland<br />

waters, Chiroptera-5 provides an invaluable source of<br />

information that supports better decision making, especially<br />

for environmental monitoring and management,<br />

in line with Hexagon’s commitment to a more sustainable<br />

future.”<br />

For more information please visit: http://leica-geosystems.com/chiroptera-5<br />

38 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

NEWS<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 39



L’Aerofototeca<br />

Nazionale<br />

racconta…<br />



by Gianluca Cantoro<br />

Towards color photography<br />

During the first half of<br />

19th century, photography<br />

stimulated people’s imagination<br />

and wonder. Despite the<br />

impressive quality of the first<br />

trials, photographs lacked the<br />

realism provided by natural<br />

colors, at times added in postproduction<br />

–as we would<br />

say today– by specialized<br />

painters (Coleman, 1897, p.<br />

56) who felt threatened by the<br />

emergence of photography.<br />

Following Rintoul, “when the<br />

photographer has succeeded<br />

in obtaining a good likeness,<br />

it passes into the artist’s hands,<br />

who, with skill and color, give<br />

to it a life-like and natural<br />

appearance” (Rintoul, 1872, p.<br />

XIII–XIV).<br />

A French physicist, Louis<br />

Ducos du Hauron, announced<br />

a method for creating color<br />

photographs by combining<br />

colored pigments instead of<br />

light, as suggested by Maxwell’s<br />

demonstration of 1861. His<br />

process required long exposure<br />

times, and this problem<br />

built on top of the absence<br />

of photographic materials<br />

sensitive to the whole range<br />

of the color spectrum. Other<br />

inventors and scientists tried<br />

to solve the challenge of color<br />

photographs, but all trials were<br />

quite expensive and needed<br />

specific equipment and complex<br />

procedures.<br />

Fig. 1 - Example of pan-sharpening between a satellite image and an historical panchromatic<br />

photograph (top and bottom left). In the column to the right, three different algorithms, respectively<br />

(from top to bottom) Brovey, IHS and PCA.<br />

The first patent of color<br />

photograph, combining both<br />

screen and emulsion on the<br />

same glass support under<br />

the name Autochrome, was<br />

registered by Auguste and<br />

Louis Lumière in 1895, the<br />

same year of their invention<br />

of the Cinématographe. The<br />

manufacturing of autochrome<br />

plates was a complex process,<br />

starting with the sieving<br />

of potato starch (to isolate<br />

individual grains between 10–<br />

15 microns in diameter), whose<br />

grains were then dyed red, green<br />

and blue-violet, mixed and<br />

spread over a glass plate (around<br />

four million transparent starch<br />

grains on every square inch of<br />

it), and coated with a sticky<br />

varnish. Next, charcoal powder<br />

was spread over the plate to fill<br />

any gaps between the colored<br />

starch grains.<br />

Autochrome plates were simple<br />

to use, they required no special<br />

apparatus and photographers<br />

were able to use their existing<br />

cameras. Exposure times,<br />

however, were long –about 30<br />

times those of conventional<br />

plates. Nevertheless, by 1913,<br />

the Lumière factory in Lyon was<br />

40 <strong>GEOmedia</strong> n°3-<strong>2022</strong>


producing 6,000 autochrome<br />

plates every day. This testifies<br />

of the appeal of color<br />

photographs already in those<br />

early times.<br />

Is it possible today to convert<br />

native black and white images<br />

(raster digital pictures, not<br />

prints or negative anymore)?<br />

And why should one take<br />

the trouble to convert<br />

panchromatic into color<br />

images after all? This paper<br />

presents some experiments<br />

to colorize historical<br />

photographs, in the effort<br />

to boost our capabilities to<br />

undisclose information from<br />

frozen moments captured<br />

by cameras and to –ideally–<br />

promote further the use of<br />

aerial images in various fields.<br />

Colors in Remote Sensing<br />

Some procedures in remote<br />

sensing are known and<br />

frequently applied to<br />

satellite images, to improve<br />

the resolution of a color<br />

image with the details of its<br />

panchromatic twin. This<br />

fusion procedure, known with<br />

the term pan-sharpening,<br />

can be applied to satellite<br />

imagery through numerous<br />

algorithms, and it produces<br />

a sensible increase in the<br />

accuracy of photo-analysis<br />

and derived feature extraction,<br />

modeling and classification<br />

(Yang et al., 2012). The most<br />

commonly used algorithms<br />

include IHS (Intensity, Hue<br />

and Saturation) (Schetselaar,<br />

1998), PCA (Principal<br />

Component Analysis) (Chavez<br />

et al., 1990), the Gram-<br />

Schmidt Spectral Sharpening<br />

(Laben Craig and Brower<br />

Bernard, 2000) and the<br />

Weighted Brovey transform<br />

(Chavez et al., 1990).<br />

The various pan-sharpening<br />

techniques have two main<br />

factors in common: 1) they are<br />

normally applied to satellite<br />

images, namely multispectral<br />

and panchromatic bands; 2)<br />

the two datasets to be fused<br />

Fig. 2 - Example of visual trick for image colorization inspired by the Color-Assimilation-Grid-Illusion. Top-<br />

Left: historical vertical image of Ostia of 1985 precisely georeferenced over the bottom satellite image. Bottomleft:<br />

Landsat/Copernicus satellite image of the same area of 2019. Top-Right: historical panchromatic with<br />

over-saturated color grid extracted from the available satellite image. Bottom-Right: Detail of the image above<br />

to show a close-up look at the colored grid and the black and white background.<br />

Fig. 3 - Application of automatic colorization algorithms (Deoldify, Algorithmia and Automatic Colorizer) to<br />

three oblique photographs (Original Image). Photographs by Otto Braasch (Musson et al., 2005, fig. 10.8, 10.9,<br />

10.7) edited for the proposed approach.<br />

should have been captured<br />

(almost) simultaneously. For<br />

these reasons it is apparently<br />

not possible to fuse an historical<br />

aerial image with a satellite<br />

image, which is what we are<br />

going to try here. Proposed<br />

methods are not conventional<br />

and may therefore attract<br />

comprehensible skepticism, but<br />

they should be intended as a<br />

proof-of-concept or experiments<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 41


Fig. 4 - Interactive Deep Colorization User Interface. After clicking on a specific point on the<br />

black and white image (see colored spots on left image in the interface), the user can assign a<br />

color from the “ab Color Gamut”, the “Suggested colors” or the “Recently used colors”. Results<br />

are presented in real time to the right of the UI and can be saved at any time.<br />

Fig. 5 - Comparison of processing of the same pictures as for Fig. 3 with Interactive Deep Colorization<br />

(or iColor).<br />

to test the capabilities of<br />

modern computer approach to<br />

obtain a realistic representation<br />

of past environments in natural<br />

colors for the benefits of photoreaders.<br />

For example, since our objective<br />

is mainly to get a colorized<br />

historical image, we can adjust<br />

reciprocal resolution of our<br />

vertical and satellite images of<br />

exactly the same area. A similar<br />

approach has been explored<br />

recently (Siok and Ewiak,<br />

2020) with aerial and satellite<br />

images of about the same<br />

period and without dramatic<br />

changes in cultivations or plot<br />

sizes. Indeed, the processing<br />

of areas that changed across<br />

time (i.e. between the date of<br />

the historical photograph and<br />

the date of the chosen satellite<br />

image in terms of time of the<br />

day, season or cultivations/<br />

urbanization processes) may<br />

produce some unpleasant<br />

artifact (see in Fig. 1 the details<br />

of the trees and bushes colors<br />

which are larger than in the<br />

historical image), but in this<br />

case a targeted editing with<br />

computer graphic software<br />

may minimize the problem, if<br />

needed.<br />

Once such high-resolution<br />

color image is being generated,<br />

the applicability of multiple<br />

operations can be explored,<br />

such as image classification and<br />

feature extraction.<br />

Another approach, completely<br />

different in terms of processing<br />

and output, is the use of<br />

an operation called Color-<br />

Assimilation-Grid-Illusion<br />

(Kolås, 2019). As the name<br />

suggests, this approach is a mere<br />

visual trick and it is presented<br />

here mainly for dissemination<br />

purposes (not for an improved<br />

photo-interpretation) and as<br />

a sort of mild invasive way to<br />

add colors to black and white<br />

images. It consists in overlaying<br />

grids (or lines or dots) of oversaturated<br />

colors over black<br />

and white images; our brain<br />

essentially fills in the missing<br />

colors that it would anticipate,<br />

or expect, to be there in a full<br />

color image.<br />

The image processing is<br />

intended to be used on one<br />

single color image, that is<br />

converted to grayscale and<br />

overlaid with the original colors<br />

only through a grid. Instead, in<br />

our case, starting from the same<br />

inspiring principle, we take<br />

the historical aerial photograph<br />

that we want to colorize, and a<br />

satellite image (ideally of about<br />

the same season) of the same<br />

42 <strong>GEOmedia</strong> n°3-<strong>2022</strong>


Fig. 6 - Possible variants generated with iColor of the same image with deliberate selection of false colors to make specific features more visible or for<br />

other applications.<br />

area; we generate the color grid<br />

from the satellite image, oversaturate<br />

it and overlay it over the<br />

panchromatic picture (Fig. 2).<br />

Calibration of the above<br />

experiment may be needed<br />

according to the printing or<br />

screen zoom size, nevertheless,<br />

it is an easy effect to colorize<br />

historical photographs.<br />

The Machine Learning<br />

automatic and<br />

semi-automatic approach<br />

Machine learning has been<br />

explored in several fields for its<br />

ability to “learn” the intended<br />

process from A to B with<br />

the help of prepared dataset.<br />

Algorithms are available to<br />

convert black and white images<br />

into color one, based exactly on<br />

a learning dataset. In this sense<br />

it could be applied to historical<br />

aerial images as well, but again,<br />

here the intent is to generate<br />

an image with colors that are<br />

plausible, not to produce an<br />

accurate representation of the<br />

actual snapshot in time.<br />

Below (Fig. 3) are some<br />

examples of oblique<br />

photographs originally taken<br />

with digital camera in color,<br />

then converted to black and<br />

white for the sake of the<br />

experiment, and colorized back<br />

with three automatic algorithms<br />

for image processing: Deoldify<br />

(Antic, 2021) (or DeepAI),<br />

Algorithmia (Zhang et al.,<br />

2016) and Automatic Colorizer<br />

(Larsson et al., 2017).<br />

The chosen algorithms, selected<br />

for their simplicity of use, for<br />

their advertised capabilities and<br />

for their availability as opensource<br />

code or online demo,<br />

are examples of a computer<br />

problem called image-to-image<br />

translation, whose success<br />

depends by the provision of<br />

sufficient (and compatible)<br />

training data (Tripathy et al.,<br />

2018). Since the training data<br />

is mostly made of ground<br />

photographs of natural subjects,<br />

portraits or architectures, the<br />

obtained result in our case is<br />

mostly unsatisfactory, especially<br />

when looking at the original<br />

(our “ground truth”) but also<br />

if we consider the generated<br />

images on their own for<br />

photointerpretation.<br />

Different result is instead<br />

achievable with another<br />

algorithm of the same family,<br />

which has an interactive model<br />

that allows user to manually<br />

input colors on black and white<br />

image based on chrominance<br />

gamut: it is the case of<br />

Interactive Deep Colorization<br />

(or iColor) (Zhang et al., 2017)<br />

(Fig.4).<br />

By default, the first proposed<br />

colorized image in this<br />

algorithm is very much similar<br />

to the ones generated by similar<br />

algorithms (see Fig. 3), but once<br />

specific colors are selected, the<br />

result improves considerably<br />

reaching a good proximity to<br />

the original images of our test<br />

cases (Fig. 5).<br />

If from one side the Interactive<br />

Deep Colorization algorithm<br />

allows one to create images with<br />

plausible colors, possibly similar<br />

to the originally depicted<br />

subject, it also provides the<br />

option to deliberately choose<br />

“wrong” colors, and somehow<br />

create a completely unreal<br />

scenario (Fig. 6), which may<br />

make sense if they are employed<br />

in our case to highlight specific<br />

features or shadows.<br />

Conclusions<br />

In modern photointerpretation,<br />

crop-marks<br />

– as well as weed-marks,<br />

germination-marks and grassmarks<br />

– by definition are<br />

made of vegetational stress or<br />

differential growth in green<br />

fields. Even with soil-marks,<br />

shades of brown help us<br />

recognizing patterns in arable<br />

lands.<br />

Seeing black-and-white images<br />

in color has the potential to<br />

brings certain details to life that<br />

would otherwise be missed or<br />

hardly be visible. This sense<br />

of immediacy is why color<br />

images feel more relatable.<br />

Historical vertical photographs<br />

traditionally served (and still<br />

serve) immensely for the study<br />

of landscape changes and the<br />

identification of archaeological<br />

traces (among others) for<br />

the reconstruction of topic<br />

palimpsests. They often provide<br />

details that have no equals in<br />

color images so far and, training<br />

on photo-interpretation black<br />

and white images cannot be<br />

ignored or replaced in any way.<br />

In the paper an effort is<br />

presented to push the<br />

boundaries of consolidated<br />

<strong>GEOmedia</strong> n°3-<strong>2022</strong> 43


practice in remote sensing<br />

and artificial intelligence,<br />

together with the attempt of<br />

presenting a visual trick for<br />

dissemination purposes. The<br />

proposed methods change<br />

the current paradigm with<br />

respect to employed algorithms<br />

and dataset to which the<br />

algorithms are applied, aiming<br />

at a new way of looking at<br />

historical aerial photographs<br />

and ideally unveiling a new<br />

dimension in past-landscape<br />

studies and dissemination.<br />

The various procedures are all<br />

oriented towards the artificial<br />

colorization of historical aerial<br />

photographs, natively black<br />

and white. These “bizarre”<br />

trials are intended as ways to<br />

promote new approaches to<br />

legacy data, with the ultimate<br />

goal to simplify or enhance<br />

aerial photo-interpretation<br />

and involve non-experts in<br />

the narration of the past<br />

made through photographic<br />

documents.<br />

Lastly, artists dealing with<br />

historical image colorization<br />

admit the intense and timeconsuming<br />

effort required to<br />

achieved a realistic result and<br />

a philological reconstruction,<br />

involving historical research,<br />

comparative materials and<br />

interviews with witnesses<br />

or experts. Therefore, black<br />

and white colorization may<br />

be a creative process that can<br />

increase focus and attention on<br />

what we see (or we don’t see) in<br />

historical aerial images.<br />


Antic, J., 2021. DeOldify [WWW Document]. Deoldify, a deep learning based project<br />

for coloring and restoring old images. URL https://github.com/jantic/DeOldify (accessed<br />

1.15.21).<br />

Chavez, P.S., Jr, Sides, S.C., Anderson, J.A., 1990. Comparison of three different methods<br />

to merge multiresolution and multispectral data: LANDSAT TM and SPOT panchromatic.<br />

AAPG Bulletin (American Association of Petroleum Geologists); (USA) 74:6.<br />

Coleman, F.M., 1897. Typical Pictures of Indian Natives, Being Reproductions from<br />

Specially Prepared Hand-coloured Photographs with Descriptive Letterpress. “Times of<br />

India” office, and Thacker & Company, Limited.<br />

Kolås, Ø., 2019. Color Assimilation Grid Illusion [WWW Document]. Color Assimilation<br />

Grid Illusion. URL https://www.patreon.com/posts/color-grid-28734535 (accessed<br />

2.2.21).<br />

Laben Craig, Brower Bernard 2000: A. Laben Craig, V. Brower Bernard, Process For Enhancing<br />

The Spatial Resolution Of Multispectral Imagery Using Pan-sharpening - (US<br />

Patent: US 6011875 A), https://lens.org/135-660-046-023-136.<br />

Larsson, G., Maire, M., Shakhnarovich, G., 2017. Learning Representations for Automatic<br />

Colorization. arXiv:1603.06668 [cs].<br />

Musson, C., Palmer, R., Campana, S., 2005. In volo nel passato: aerofotografia e cartografia<br />

archeologica, Biblioteca del Dipartimento di archeologia e storia delle arti, Sezione<br />

archeologica, Università di Siena. All’insegna del giglio, Florence?<br />

Rintoul, A.N., 1872. A guide to painting photographic portraits, draperies, backgrounds,<br />

&c. in water colours : with concise instructions for tinting paper, glass, & daguerreotype<br />

pictures and for painting photographs in oil colours and photo-chromography.<br />

Schetselaar, E.M., 1998. Fusion by the IHS transform: Should we use cylindrical or<br />

spherical coordinates? International Journal of Remote Sensing 19, 759–765. https://doi.<br />

org/10.1080/014311698215982<br />

Siok, K., Ewiak, I., 2020. The simulation approach to the interpretation of archival aerial<br />

photographs. Open Geosciences 12, 1–10. https://doi.org/10.1515/geo-2020-0001<br />

Tripathy, S., Kannala, J., Rahtu, E., 2018. Learning image-to-image translation using<br />

paired and unpaired training samples. arXiv:1805.03189 [cs].<br />

Yang, S., Wang, M., Jiao, L., 2012. Fusion of multispectral and panchromatic images<br />

based on support value transform and adaptive principal component analysis. Information<br />

Fusion 13, 177–184. https://doi.org/10.1016/j.inffus.2010.09.003<br />

Zhang, R., Isola, P., Efros, A.A., 2016. Colorful Image Colorization. arXiv:1603.08511<br />

[cs].<br />

Zhang, R., Zhu, J.-Y., Isola, P., Geng, X., Lin, A.S., Yu, T., Efros, A.A., 2017. Rea<br />


Color photography; artificial intelligence; image-to-image; remote sensing;<br />

air-photo interpretation;<br />


Historical photographs, whether taken from the air or from the ground, are usually synonyms<br />

of grayscale or sepia prints. From the very beginning of photography, during the first<br />

half of 19th century, people were amazed by this new media that could record all aspects<br />

of a scene with great detail. Soon though, everybody started wondering why would such<br />

an impressive innovation fail to record colors? A process of trials and errors then started<br />

(including the most successful and pioneer one, involving the use of potato starch, by Lumière<br />

brothers) aiming to add colors to photographs, till the consolidation of new systems<br />

(camera and film) capable to collect photographs directly in color. In the past, before and<br />

during this innovative approach, native black and white photographs were painted in the<br />

effort to give them life. Today, only few methods are available to convert a panchromatic<br />

image into a color one, and they need a number of steps and further development to work<br />

properly. The paper tries to present different methods to colorize native black and white<br />

photographs, based on available automatic or interactive Artificial Intelligence (Machine<br />

Learning or Deep Learning) algorithms, on revised remote sensing procedures and on<br />

visual tricks, aiming at exploring the possible improvement in readability and interpretation<br />

of photographed contests in the usual analytic process of photo-interpretation. At the<br />

same time, colorized historical photographs hold different appeal in the general public<br />

and have the potential to attract and involve non-experts in the archaeological/historical<br />

reconstruction phases.<br />

AUTHOR<br />

Gianluca Cantoro<br />

gianluca.cantoro@cnr.it<br />

Institute of Heritage Science (ISPC) – Italian National Research Council (CNR)<br />

Area della Ricerca di Roma 1, Via Salaria km 29,300 - 00010 Montelibretti (RM)<br />

Italian National AirPhoto Archive (Aerofototeca Nazionale, AFN) – Istituto Centrale<br />

per il Catalogo e la Documentazione (ICCD)<br />

Via di San Michele 18, 00153 Rome (RM)<br />

44 <strong>GEOmedia</strong> n°3-<strong>2022</strong>

AMPERE<br />

a a GNSS-based integrated platform<br />

for for energy decision makers<br />

AMPERE Working Group in Santo Domingo<br />


Barrio Los Tres Brazos<br />

Santo Domingo Este<br />

Asset Mapping Platform Platform for<br />

Emerging CountRies Electrification<br />

Emerging CountRies Electrification<br />

Despite global electrification rates are significantly progressing, the<br />

Despite access global to electricity electrification in emerging rates countries are significantly is still far from progressing, being achieved. the access<br />

Indeed, the challenge facing such communities goes beyond the lack of<br />

to electricity infrastructure emerging assets; what countries is needed is is still a holistic far from assessment being achieved. of the<br />

energy demand and its expected growth over time, based on an accurate<br />

Indeed, assessment the challenge of deployed facing resources such communities and their maintenance goes beyond status. the lack of<br />

infrastructure assets; what is needed is a holistic assessment of the energy<br />

demand and its expected growth over time, based on an accurate<br />

assessment of deployed resources and their maintenance status.<br />

AMPERE Consortium<br />

www.h2020-ampere.eu<br />

AMPERE project has received funding from the European GNSS Agency (grant<br />

agreement No 870227) under the European Union’s Horizon 2020 research and<br />

innovation programme.

EVENTS<br />

22-28 Agosto <strong>2022</strong><br />

FOSS4G <strong>2022</strong> Academic<br />

Track<br />

Firenze (Italy)<br />

http://www.geoforall.it/<br />

kcp3h<br />

11-15 September <strong>2022</strong><br />

FIG Congress <strong>2022</strong><br />

Warsaw (Poland)<br />

http://www.geoforall.it/<br />

kc3kd<br />

17-20 October <strong>2022</strong><br />

IAG International<br />

Symposium on Reference<br />

Frames for Applications in<br />

Geosciences (REFAG <strong>2022</strong>)<br />

Thessaloniki (Greece)<br />

http://www.geoforall.it/<br />

kc3kr<br />

18-20 October <strong>2022</strong><br />

INTERGEO <strong>2022</strong><br />

Essen (Germany)<br />

http://www.geoforall.it/<br />

kc3kh<br />

20-21 October <strong>2022</strong><br />

SAR Analytics Symposium<br />

Berlin (Germany)<br />

http://www.geoforall.it/<br />

kc3f8<br />

Cartographic Conference<br />

Firenze www.geoforall.it/<br />

kyk8k<br />

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