GEOmedia 3 2022

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

LAND CARTOGRAPHY 

GIS 

CADASTRE 

GEOGRAPHIC INFORMATION 

PHOTOGRAMMETRY 

3D 

SURVEY TOPOGRAPHY 

CAD 

BIM 

EARTH OBSERVATION SPACE 

WEBGIS 

UAV 

URBAN PLANNING 

CONSTRUCTION 

LBS 

SMART CITY 

GNSS 

ENVIRONMENT 

NETWORKS 

LiDAR 

CULTURAL HERITAGE 

May/June 2022 year XXVI N°3 

Mobile Robotics and 

Autonomous Mapping 

DATA COLLECTION 

AND PUBLICATION 

WITH QGIS 

PLAYING WITH COLORS 

ON PANCHROMATIC 

AERIAL PHOTOGRAPHS 

MODELLING WATERSHED 

PHENOMENA WITH QGIS

Inspiration for a smarter World 

This is the motto of the Intergeo Conference 2022, where the English language edition of 

GEOmedia, usually issued as the third of the year in the summer, will be distributed on next 

October. 

The Conference will highlight current developments in surveying with following main topics: 

Digital Twins and their value creation 

4D-geodata and geospatial IoT 

Potentials of remote sensing 

Industrial surveying, measurement systems and robotics 

Smart Cities and mobility in the context of climate change and sustainability 

Mobile mapping, Web services and geoIT in disaster management 

Spatial reference and positioning 

Earth observation and Galileo 

Trend topics such as Building Information Modeling (BIM) and the diverse application 

possibilities of the Digital Twins, but also the current requirements for the Smart City and 

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

particular importance in this edition. The focus will be on their use in Building Information 

Modeling, smart planning and construction. 

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

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

and Ulisse Cavallini, about the main solutions available for data collection and seamless 

publications over the web: MerginMaps, Qfield, Lizmap, with an example form a water 

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

components for modelling watershed phenomena”, by Flavio Lupia and Giuseppe Pulighe, 

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

by a dedicated QGIS plugin (QSWAT), widening the userbase and the potential modelling 

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

Mapping: Technology for a more Sustainable Agriculture” by Eleonora Maset, Lorenzo Scalera 

and Diego Tiozzo Fasiolo, concerning the automation in geomatics for agriculture using 

robotics platforms, that must be equipped with appropriate technology. And “Geographical 

Information: the Italian Scientific Associations and... the Big Tech” from Valerio Zunino, 

observing that while the World is changing, the Italian Scientific Associations of Geographical 

Information are not. 

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

dimension, becoming increasingly important in all aspects of technology and science. 

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

on Panchromatic Aerial Photographs”, by Gianluca Cantoro from Italian National AirPhoto 

Archive (Aerofototeca Nazionale, AFN), discussing about the use of historical photographs, 

whether taken from the air or from the ground, are usually synonyms of grayscale or sepia 

prints. 

Enjoy your reading, 

Renzo Carlucci

In this 

issue... 

FOCUS 

REPORT 

COLUMNS 

From the field 

to the clouds: 

data collection and 

publication with QGIS 

By Paolo Cavallini, 

Matteo Ghetta, 

Ulisse Cavallini 

6 

24 ESA Image 

32 NEWS 

40 AEROFOTECA 

46 AGENDA 

12 

Open-source GIS 

software and components 

for modelling watershed 

phenomena 

Understanding the soil 

and water components 

under different 

management options with 

QGIS and the SWAT 

By Flavio Lupia 

and Giuseppe Pulighe 

On the cover the 

sensorized mobile 

platform developed 

at University of 

Udine, Italy. 

geomediaonline.it 

GEOmedia, published bi-monthly, is the Italian magazine for 

geomatics. Since more than 20 years publishing to open a 

worldwide window to the Italian market and vice versa. 

Themes are on latest news, developments and applications in 

the complex field of earth surface sciences. 

GEOmedia faces with all activities relating to the acquisition, 

processing, querying, analysis, presentation, dissemination, 

management and use of geo-data and geo-information. The 

magazine covers subjects such as surveying, environment, 

mapping, GNSS systems, GIS, Earth Observation, Geospatial 

Data, BIM, UAV and 3D technologies.

Mobile Robotics and 

Autonomous Mapping: 

Technology for a more 

Sustainable Agriculture 

by Eleonora Maset, 

Lorenzo Scalera, 

Diego Tiozzo Fasiolo 

16 

ADV 

Ampere 45 

Epsilon 33 

Esri Italia 23 

Geomax 31 

Gter 15 

INTERGEO 35 

Nais 39 

Planetek 48 

Stonex 47 

Strumenti Topografici 2 

Geographical 

Information: Our 

Associations and ... 

the Big Tech 

by Valerio Zunino 

20 

Teorema 46 

In the background image: 

Bonn, Germany. This Esa 

Image of the week, also featured 

on the Earth from 

Space video programme, was 

captured by the Copernicus 

Sentinel-2 mission, that with 

its high-resolution optical camera, 

can image up to 10 m 

ground resolution. 

(Credits: ESA) 

26 

Time and 

Longitude: an 

unexpected 

affinity 

by Marco Lisi 

Chief Editor 

RENZO CARLUCCI, direttore@rivistageomedia.it 

Editorial Board 

Vyron Antoniou, Fabrizio Bernardini, Caterina Balletti, 

Roberto Capua, Mattia Crespi, Fabio Crosilla, 

Donatella Dominici, Michele Fasolo, Marco Lisi, 

Flavio Lupia, Luigi Mundula, Beniamino Murgante, 

Aldo Riggio, Monica Sebillo, Attilio Selvini, Donato Tufillaro 

Managing Director 

FULVIO BERNARDINI, fbernardini@rivistageomedia.it 

Editorial Staff 

VALERIO CARLUCCI, GIANLUCA PITITTO, 

redazione@rivistageomedia.it 

Marketing Assistant 

TATIANA IASILLO, diffusione@rivistageomedia.it 

Design 

DANIELE CARLUCCI, dcarlucci@rivistageomedia.it 

MediaGEO soc. coop. 

Via Palestro, 95 00185 Roma 

Tel. 06.64871209 - Fax. 06.62209510 

info@rivistageomedia.it 

ISSN 1128-8132 

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

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Issue closed on: 28/07/2022 

una pubblicazione 

Science & Technology Communication

FOCUS 

From the field to the clouds: 

data collection and 

publication with QGIS 

By Paolo Cavallini, Matteo Ghetta, Ulisse Cavallini 

Open source GIS, and in particular QGIS, is a leading free and open 

source solution for desktop mapping since many years already. 

Its versatility, ease of use, and analytical power have made it the 

software of choice for many professionals around the world (see 

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

web publication are attracting more attention in the recent years. 

A whole suite of integrated tools is now available to implement a 

complete workflow, all centered on QGIS. 

Central to all tools is the QGIS project, designed and created using 

QGIS Desktop. Its power in creating beautiful and rich styling is 

probably unsurpassed, with expression-based styling, fusion modes, 

and a huge set of other functions. The same project can be used on a 

mobile device, and exposed through a web service (WMS, WFS, WCS, 

WPS) and a complete web interface. 

Mobile 

Over the 20+ of life of QGIS, 

a number of mobile interfaces 

have been designed, from 

the first attempt to run the 

whole of QGIS on Android, 

or a simplified interface on 

Windows Mobile, to proper 

mobile apps. Currently the 

most important and used 

solutions to use QGIS on a 

mobile are Qfield (https:// 

qfield.org/) and MerginMaps 

(https://merginmaps.com/). 

Both are generic tools, that 

can be effectively employed 

in a wide variety of contexts. 

Their flexibility stems from 

the ease with which they 

can be configured, simply 

through QGIS projects, that 

include sophisticated styling 

and simple to complex forms, 

with all expected functionalities 

such as custom menus 

(drop-down, checkbox, 

calendar etc.), relations, 

constraints, default values, 

user guiding tips, etc. While 

still relatively new entries in 

the market, they have been 

successfully employed in 

extensive surveys, from single 

users up to thousands of field 

surveyors simultaneously collecting 

data in the field. 

MerginMaps 

MerginMaps is a web service, 

written in Python with flask, 

that manages the synchronization 

process of a QGIS 

6 GEOmedia n°3-2022

FOCUS 

project, and all the related 

files. For media files, it is 

not unlike other cloud file 

management service. Unlike 

other cloud file managers, 

though, MerginMaps is able 

to manage geospatial information, 

primarily in the form 

of geopackage files. When a 

new version of a geopackage 

is uploaded, for example because 

a surveyor added some 

features and is uploading 

them back on the centralized 

server, the geodiff library is 

used to check for changes, 

merge them and solve any 

conflicts. This enables some 

flexibility in the downloading 

and uploading of new data, 

since multiple surveyors can 

add features for their area 

of interest, upload different 

versions of the modified geopackage, 

and MerginMaps 

will take care of adding every 

new feature to the centralized 

repository. 

Lutra Consulting, the firm 

developing MerginMaps, 

offers an official hosted instance, 

a reliable way to use 

MerginMaps without the 

need for configuration and 

installation on a local server. 

The official hosted instance 

offers a generous free trial 

for non-commercial usage. 

Pricing is clear and reasonable, 

with no per-user pricing, 

and the support is quick and 

responsive. 

The surveying process is effectively 

split in two. The 

first phase involves the generation 

of the QGIS project, 

the related layers, and the 

form structure, and the 

subsequent upload to the 

MerginMaps web service. Far 

from being complicated, this 

phase still requires a good understanding 

of GIS software 

and data formats. 

Once the project is uploaded 

and ready, the surveying phase 

can begin. Due to the easeof-use 

of the mobile application, 

the surveying requires 

minimal technical skill, and 

operators can be trained in a 

matter of few few days. From 

their point of view, the intricacies 

of the project are invisible: 

they just need to add or 

modify the features according 

to the form, preconfigured 

through QGIS, and click on 

the synchronization button 

once they are online. A very 

recent addition, the option 

to automatically upload new 

changes whenever an internet 

connection is detected, further 

simplifies this. 

The project folder itself is 

what is visible from the web 

interface. In order to manipulate 

the project, and the 

geospatial data, MerginMaps 

can be accessed from QGIS, 

through the official plugin, 

and through the MerginMaps 

mobile application, available 

for Android and iOS. 

The MerginMaps application 

has a special focus on simple 

UI and UX design, in order 

to be accessible by everyone, 

regardless of their GIS experience 

and in demanding field 

conditions. 

Qfield 

Qfield has been the first natively 

Android mobile application 

connected to QGIS. 

Downloaded around halfmillion 

times it is available 

for Android and now iOS. 

The idea of the usage is very 

simple: the user sets up a 

project in QGIS and thanks 

to the plugin QfieldSync it 

will be packaged in a folder. 

The folder created has to be 

copied to the device and with 

the App data can be collected 

on the field. Back to the office 

the data collected with 

the mobile device have to be 

copied back on the machine 

and re-synchronized to the 

original data source with the 

QfieldSync plugin. 

GEOmedia n°3-2022 7

FOCUS 

The App has a very simple 

design and it comes with a lot 

of features: snapping facilities, 

advanced form layout, pictures 

and interaction with the legend 

to name a few. 

The manual synchronization 

can be nowadays avoided 

thanks to QfieldCloud, a 

Django framework that is able 

to store and automatically 

synchronize the data from the 

computer to the mobile device 

and vice versa. Open Source, 

QfieldCloud is still in Beta 

version and let the user choose 

between installing the software 

on the server or register to the 

web with a free plan (limited 

space) or buy additional space. 

The main advantage of 

QfieldCloud is that the user 

can log in both on the machine 

and on the device with the 

same name and immediately 

synchronize the data between 

all the devices. The QfieldSync 

plugin in QGIS has all the 

options needed to log in, synchronize 

and also see the data 

changes difference. 


FOCUS 

As for Mergin, also Qfield 

has the possibility of using a 

server managed by OpenGIS. 

ch, the firm developing it 

without the need for configuration 

and installation on a 

local server. 

Web 

A number of different web 

interfaces have been designed 

around QGIS. For all of 

them the basic mechanism is 

the same: all user requests are 

sent to the backend (QGIS 

Server) that creates the map 

and other objects (legend, 

print layouts etc.) and send 

them back to the web app. 

The main advantages over 

other free and open source 

webGIS solutions are the ease 

to create both visually sophisticated 

maps, and complex 

print and reporting through 

QGIS Desktop layouts, without 

the need for specific 

web skills. 

The most widely used is 

Lizmap, created and maintained 

by 3Liz, a South French 

company, who also substantially 

contributes from years 

to core QGIS development. 

As for the other solutions 

described, also Lizmap can 

be used without the need for 

configuration and installation 

on a local server through a 

service managed and maintained 

by 3Liz, the firm developing 

it. 

Case study 

MerginMaps was recently 

used in a project geared towards 

the improvement of 

water resources infrastructure 

in The Gambia, financed by 

the African Development 

Bank; the project is headed 

by Hydronova, and its GIS 

section is technically managed 

by Faunalia. 

Special acknowledgment 

for this project goes for the 

continuous support to the 

Climate Smart Rural WASH 

Development Project Office 

Team and to the Department 

of Water Resources Staff, under 

the Ministry of Fisheries 

and Water Resources of the 

Gambia. 

Data collection is the first 

task upon which the whole 

project is built, since in order 

to improve resource management, 

key stakeholders need 

to know the current situation 

and distribution of the resources 

at their disposal. 

An open source solution 

is ideal in most contexts, 

even more so in a context 

where free access to data is 

paramount, and budget constraints 

are tight. 

The MerginMaps mobile 

application, backed by the 

Mergin web service, was chosen 

due to its ease of use and 

synchronization. Due to the 

possibility and ease of setting 

up a Mergin instance, all the 

data was kept in-situ at the 

relevant ministry, retaining 

control on this crucial information. 

A QGIS project with four 

layers, each with a custom 

form, was created. In order 

to have all the data fully offline, 

vector tiles were used. 

These were generated, for the 

whole country, by extracting 

OpenStreetMaps data, packaging 

it in an mbtiles file, and 


FOCUS 

by styling them with one of 

the OpenMapTiles styles. 

The resulting project was 

only 16 MB, a small fraction 

of the 160+ MB that would 

have been needed for rasterized 

tiles. 

Using the QGIS drag-n-drop 

form, extensive logic was 

introduced in the data entry 

user interface. With this 

setup, users were guided in 

choosing the three different 

administrative levels from a 

drop down, with automatic 

filtering of the available options, 

and constraints with 

appropriate description were 

implemented. For water 

sources, which are of upmost 

importance, a photo was also 

required. 

After the project was tested, 

teams of surveyors covered 

the whole country in the span 

of a few months, while the 

survey manager constantly 

analyzed data quality with 

spot crosschecks. 

Periodically, the tablets were 

brought back online, and 

the data was synchronized. 

In this process, the selective 

sync option, introduced in 

MerginMaps (at the time called 

Input) 1.0.1, was crucial. 

This feature instructs all the 

tablets to upload the pho- 

tos that were taken locally, 

without downloading all 

of the other media in the 

project, that was added by 

other surveyors. Without 

this, more than 15 GB of 

photos would have been 

downloaded into each tablet, 

severely impairing the 

synchronization process and 

requiring a stable and fast 

internet connection. 

At the completion of the 

survey, the data was checked 

and cleaned, then it 

was synchronized with a 

PostgreSQL/PostGIS database, 

using the mergin-dbsync 

tool, as described in 

the “Extensions and integrations” 

section. This procedure 

initialized the new 

database, and ensures that 

any change in the data will 

be reflected in the database 

tables. 

Using the newly initialized 

database, a second QGIS 

project based on the same 

data was created and published 

on a WebGIS based on 

QGIS server and Lizmap, 

thus reusing QGIS styling 

without the need for restyling 

and conversion. In 

this phase, the advanced 

forms could be reused, thus 

showing on the website all 

the information as entered 

by the surveyor, including 

the water source photo. 

Other layers, such as the 

administrative subdivisions, 

were added, as well as the 

localization tool, that enables 

any user to quickly find 

the current location, a village, 

or an area of interest. 

By combining the efficiency 

of PostgreSQL materialized 

view, and the flexibility 

of the QGIS print layout, 

multiple layouts were created 

and personalized for 

each administrative level, 


FOCUS 

from country aggregates to 

the village level. Graphs created 

with DataPlotly, a recent 

addition to the QGIS print 

layout, were also used. 

These layouts were then exposed 

in Lizmap with the 

AtlasPrint QGIS Server plugin. 

Extensions and integrations 

MerginMaps, being written 

in Python, with good documentation, 

has quite a few 

extensions, that enable it to 

adapt to the specific needs of 

most survey projects. 

Mergin-db-sync, first released 

in June 2020, is a crucial 

part of the MerginMaps 

offering. Also written in 

Python, it interfaces with the 

main Mergin web service and 

with a PostgreSQL/PostGIS 

database, keeping the two 

in constant sync. Whenever 

a change is detected in the 

specified geopackage, the 

changes are propagated to the 

PostgreSQL database, and 

vice versa. Strict versioning 

is still maintained, since the 

tool creates a new version of 

the MerginMaps project, just 

as a user uploading new data 

would. The tool uses two 

PostgreSQL schemas, one in 

which changes can be made 

directly, and a backup copy 

used to check for changes. 

It utilizes the geodiff library 

to check and merge changes, 

even if they happen in the 

two backends at the same 

time. Mergin-db-sync can 

also be used to expose the 

data on a WebGIS such as 

Lizmap. 

Mergin-media-sync, first 

released in December 2021, 

allows for the offloading of 

the media files, often representing 

a good chunk of the 

project size, to a local drive, 

or to the MinIO object storage. 

When new media files 

are added, the tool downloads 

them, uploads them to 

the configured service, and 

updates the relevant rows in 

the geopackage, pointing the 

media path to the new url. In 

a wide-area survey, covering 

many features and containing 

photos, this tool can effectively 

be used to avoid cluttering 

the MerginMaps project 

with hundreds of gigabytes of 

images. 

Both MerginMaps and 

QField can be used with an 

external GPS/GNSS device, 

that can be obtained at a low 

cost, enabling high precision 

location, up to a centimeter 

of accuracy. These devices, 

once highly priced, are now 

accessible and reliable. 

METAKEYS 

field survey; water resources; qgis; qfield 

ABSTRACT 

QGIS is the leading free and open source 

desktop GIS. It is also a complete ecosystem, 

that allows to build complete workflows, from 

field data collection to publication on the web. 

Central to it are QGIS projects, that define 

data sources, projections, styling and integration, 

and are reused from mobile to the web 

without a need to reconfigure them. We describe 

the main solutions available for data collection 

and seamless publication over the web: 

MerginMaps, Qfield, Lizmap, with an example 

form a water resources project in Gambia. 

AUTHOR 

Paolo Cavallini 

cavallini@faunalia.it 

Matteo Ghetta 

matteo.ghetta@faunalia.eu 

Ulisse Cavallini 

ulisse.cavallini@faunalia.it 

Faunalia 

www.faunalia.eu 

Piazza Garibaldi 4, 56025 Pontedera 

(PI), Italy 


FOCUS 

Open-source GIS software and components 

for modelling watershed phenomena 

Understanding the soil and water components under 

different management options with QGIS and the SWAT+ 

By Flavio Lupia and Giuseppe Pulighe 

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

Modelling the 

watershed balance 

Current and future climate 

change are expected to increase 

our challenges in preserving 

natural resources and ecosystem 

services. At the watershed scale, 

the processes taking places 

relate to interactions between 

soil and water and are influenced 

by land use management. 

Precipitation, infiltration, runoff, 

evapotranspiration, soil 

erosion, soil and water pollutions 

are the main components 

to be considered whenever for 

the simulation of the watershed 

system within the current and 

future conditions under changing 

drivers (i.e., human interventions 

and climate change). 

One of the most popular watershed 

modelling tools is the 

Soil and Water Assessment Tool 

(SWAT), a public domain model 

jointly developed by USDA 

Agricultural Research Service 

and Texas A&M University 

(Arnold et al., 1998). SWAT 

has been used worldwide for 

different applications (water 

quality, land use, soil erosion, 

crop yield, etc.) during the last 

four decades. As of May 2022, 

a total of 5154 articles report 

SWAT applications within 

different journals according to 

the SWAT Literature Database 

(https://www.card.iastate.edu/ 

swat_articles/). 

SWAT enables the simulation of 

watershed and river basin quantity 

and quality of surface and 

ground water under the influence 

of land use, management, 

and climate change. It can be 

used to monitor and control soil 

erosion, non-point source pollution 

and basin management. An 

entirely reconstructed version of 

SWAT, nicknamed SWAT+, was 

only launched in recent years 

to improve the capabilities of 

SWAT code maintenance and 

future development. Reservoir 

operation functions have been 

added to SWAT+ in addition 

to the new model structure to 

increase model simulation performance. 

Now, the physical 

objects (hydrologic response 

units (HRUs), aquifers, canals, 

ponds, reservoirs, point sources, 

and inlets) are built as separate 

modules. 

QSWAT: the QGIS plugin for 

the new SWAT+ model 

SWAT model was implemented 

withing the GIS environment 

with dedicated plugins both for 

commercial and open-source 

GIS platforms. The GIS imple- 


FOCUS 

mentations has allowed users to 

manage more efficiently the watershed 

modelling process in its 

natural environment, the GIS, 

where the spatial component 

of the various datasets can be 

handled straightforwardly. 

QSWAT is the most recent 

implementation as QGIS plugin, 

written in Python, of the 

new version SWAT+. As of 6th 

April 2022, QSWAT3 v1.5 for 

QGIS3 was released for 32 and 

64bit machines. SWAT+ is written 

in FORTRAN and is also 

available as command-line executable 

file that runs text file inputs 

without interface (SWAT+ 

installer 2.1.0 was released on 

31 March 2022 for Windows, 

Linux, and MacOS). QSWAT 

is increasingly gaining momentum 

thanks to the spreading 

and robustness of the opensource 

GIS platform. QGIS has 

a huge number of users and a 

solid reputation. It is utilized in 

academic and professional settings, 

and it has been translated 

into more than 48 languages. 

Moreover, the release of SWAT 

code as open source has benefitted 

the diffusion and improvement 

of the model by making 

it more robust and suitable for 

different applications thanks 

to the collaboration of several 

users with various expertise. 

Different channels are available 

for users’ collaboration such as 

QSWAT user group, SWAT+ 

Editor user group and SWAT+ 

model user group (https://swat. 

tamu.edu). Other plugins are 

also available, such as the one 

developed for the commercial 

ESRI ARCGIS (ArcSWAT). 

Beyond the functions provided 

by QSWAT for setting 

the watershed to be analysed, 

SWAT+ is complemented by 

additional software: SWAT 

Editor (a user interface for 

modifying SWAT+ inputs and 

running the model installed 

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

watershed through QSWAT. 

along with QSWAT), SWAT+ 

Toolbox (a user-friendly tool 

for SWAT+ for sensitivity 

analysis, manual and automatic 

calibration), SWATplus-CUP 

(the Calibration Uncertainty 

Program for SWAT+ requiring 

a license purchase) and 

SWATplusR (a set of tools 

taking advantage of the R environment 

for parameter sensitivity 

analysis, model calibration 

and the analysis model results). 

Moreover, the SWAT website 

provides datasets for running 

the model even if specific datasets, 

with adequate spatial and 

temporal resolution, are always 

recommended for the study 

areas to be analysed. The datasets 

available have often global 

coverage and are relative to climate, 

soil, land use and Digital 

Elevation Models (DEMs). 

Look up tables are also supplied 

with QSWAT to properly 

match to standard legends the 

land use and soil codes. 

The four-steps process and the 

minimum set of data for running 

SWAT+ 

The following spatial and tabular 

data are required for running 

SWAT+: Land use/cover, DEM, 

Soil data (hydrological group, 

clay, silt, sand), Climate data 

(temperature, precipitation, 

humidity, solar radiation, wind 

speed) and Hydrology (river 

discharge). 

QSWAT runs SWAT+ by following 

a four-step procedure: 1) 

Delineate watersheds, 2) Create 

Hydrologic Response Units 

(HRUs), 3) Edit inputs and run 

SWAT, and 4) Visualize. 

The first step deals with the 

definition of the watershed and 

its structure by extracting the 

channels and the watershed 

boundary by processing the 

DEM of the study area with 

the classical Terrain Analysis 

Using Digital Elevation Models 

(TauDEM) functions that 

divide the watershed in subbasins 

(areas with a principal 

stream channel). The second 

step involves the creation of 

the HRUs, lumped areas with 

the same combination of soil, 

topography, and land use, not 

spatially related to each other 

(Rathjens et al., 2016). The 

third step concerns the weather 

data selection and the set-up 

of model parameters. For instance, 

the latter are relative 

to the potential evapotranspiration 

method (e.g., Priestley- 


FOCUS 

Taylor, Penman-Monteith or 

Hargreaves), curve number 

method for soil moisture, land 

use management and conservation 

practices. The fourth step 

is dedicated to the visualization 

of the results at basin and subbasin 

scale and to the outputs 

exploration for a given channel 

and gauge. 

The model can simulate daily, 

monthly, yearly, and average 

outputs for different model 

components (e.g., channel, 

aquifer, reservoir, etc.), for nutrient 

balance, water balance, 

plant weather and losses from 

the basin, HRUs and landscape 

units. Results can be printed 

and exported in different formats 

such as tabular or text 

structure. 

Calibration and validation 

After the SWAT+ run and the 

production of the outputs, model 

calibration and validation 

are strongly recommended. 

The model can be calibrated 

and validated for hydrologic, 

sediment, nitrogen, and phosphorus 

components. These 

last steps guide the user on 

the fine-tuning process of the 

model parameters to produce 

results coherent with the real 

watershed processes. It requires 

the collection of data on river 

discharge that are often missing 

for several watersheds or 

available in analogic format, 

moreover water quality data 

(e.g., sediments load, dissolved 

oxygen, nitrate and phosphorous 

concentrations, etc.) can 

be used. Alternative approaches 

for hydrology calibration may 

involve the use of evapotranspiration 

data from satellite data 

to overcome the lack of river 

discharge data from the gauge 

stations at the outlets. SWAT+ 

allows several options for calibrating 

and validating the 

simulation under the different 

parametrizations defined by 

the users. A sensitivity analysis 

is quite common approach 

undertaken to pinpoint the 

main sensitive parameters and 

to reduce their redundancy during 

cal/val process. Literature 

review is always useful to start 

listing a set of common parameters 

affecting streamflow and 

sediment yield process. SWAT- 

Calibration and Uncertainty 

Programs (SWAT-CUP) is by 

Fig. 3 – Schematic representation of the hydrology outputs at watershed level through QSWAT. 

far the most known tool for 

assessing the sensitivity of parameters 

by providing several 

model evaluation techniques 

based on the relevant statistics 

(e.g., Pearson’s correlation 

coefficient, root mean square 

error (RMSE), etc.). Following 

the identification of the most 

sensitive parameters the calibration 

and validation phase 

are carried out by focussing on 

specific components (e.g., daily 

discharge). The time series of 

the available data (e.g., t0-tn) is 

divided to provide a reference 

period for the warm-up (e.g., 

t0-t5), calibration (e.g., t6-t15) 

and validation (e.g., t16-tn). 

Finally, model performances 

are assessed by using classical 

statistic measures (e.g., RMSE). 

Calibration and validation can 

be long and tedious. Therefore, 

it is always recommended to 

follow a precise work protocol 

(Abbaspour et al. 2018). 

Extending model simulation capabilities: 

land use management 

and climate change 

The impact of alternative land 

uses, and climate change are 

pressing concerns in different 

regions of the world. SWAT+ 

allows modelling diverse land 

use scenarios to meet sustainability 

goals (Pulighe et. al., 

2020) through a module where 

alternative land management 

practices can be defined (e.g., 

ploughing, seeding, tillage, irrigation, 

fertilization rates and 

crop nutrients uptake, etc.), 

by defining dates or specific 

land use classes and regions. 

Similarly, climate projections 

from climate models under different 

representative concentration 

pathways (RCPs) scenarios 

of greenhouses emissions can be 

loaded as weather data to create 

climate scenarios for the future 

decades that can be compared 

to the baseline period covering 

the historical meteorological 

data. SWAT+ can ingest these 


FOCUS 

data for simulating seasonal 

changes in precipitation and 

temperatures, hydrological extremes, 

flow regime alterations 

and river discharge, future water 

quality (i.e., nitrogen and 

phosphorus) and soil erosion 

conditions, and future biomass 

production. Estimating 

the mentioned effects on the 

hydrological regime might have 

strong impacts also on agricultural 

activities posing challenges 

to land use management and 

irrigation (Pulighe et al., 2021). 

Conclusions 

Open-source GIS (QGIS) 

and free to use models such 

as SWAT+ can be considered 

effective and strategic tools for 

monitoring and assessing water 

and soil interactions at the 

watershed level. In addition, 

the growing availability of public 

domain geospatial datasets 

can increase the applicability 

of the simulation of the watershed 

processes worldwide and 

for a wide variety of use cases. 

QSWAT will be a valuable 

tool for the SWAT scientific 

community thanks to the full 

integration with the geospatial 

functions, the new functionality 

offered by SWAT+ and 

the contribution of a wide and 

growing open-source community. 

QSWAT could be a powerful 

tool to assess the effects 

of climate change and land use 

management and the impacts 

on water quality and land degradation. 

We believe that in 

the near future, the evaluation 

of the effectiveness of policy interventions 

and the deployment 

of sustainable soil/water management 

practices will become 

an interesting arena for experimenting 

and acknowledging the 

potentiality of SWAT+. 

REFERENCES 

Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, 

J. R. (1998). Large area hydrologic modeling 

and assessment part I: model development 1. JAWRA 

Journal of the American Water Resources Association, 

34(1), 73-89. 

Abbaspour KC, Vaghefi SA, Srinivasan R. A Guideline 

for Successful Calibration and Uncertainty 

Analysis for Soil and Water Assessment: A Review of 

Papers from the 2016 International SWAT Conference. 

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

w10010006 

Pulighe, G., Lupia, F., Chen, H., & Yin, H. (2021). 

Modeling Climate Change Impacts on Water Balance 

of a Mediterranean Watershed Using SWAT+. Hydrology, 

8(4), 157. 

Pulighe G, Bonati G, Colangeli M, Traverso L, Lupia 

F, Altobelli F, Dalla Marta A, Napoli M. Predicting 

Streamflow and Nutrient Loadings in a Semi-Arid 

Mediterranean Watershed with Ephemeral Streams 

Using the SWAT Model. Agronomy. 2020; 10(1):2. 

https://doi.org/10.3390/agronomy10010002 

Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I., 

& Arnold, J. G. (2016). CMhyd user manual. Doc. 

Prep. Simulated Clim. Change Data Hydrol. Impact 

Study. 

https://www.card.iastate.edu/swat_articles/ 

https://swat.tamu.edu 

METAKEYS 

QGIS; QSWAT; watershed; river basin; SWAT+; 

climate change 

ABSTRACT 

The Soil and Water Assessment Tool (SWAT) enables 

the simulation of watershed and river basin quantity 

and quality of surface and ground water under the influence 

of land use, management, and climate change. 

It can be used to monitor and control soil erosion, 

non-point source pollution and basin management. 

The recent version (SWAT+) was implemented by 

a dedicated QGIS plugin (QSWAT) widening the 

userbase and the potential modelling application 

worldwide. QSWAT, along with additional software 

for preparing the input dataset and for performing 

the calibration/validation phase, further extends the 

watershed modelling capabilities. Such tools and the 

growing diffusion of public open geospatial datasets 

are expected to increase the range of applications especially 

with the availability climate projections datasets. 

The latter will enable users to simulate all the watersoil 

phenomena at watershed level under future conditions 

to better understand and plan suitable action for 

preserving the natural resources. 

AUTHOR 

Flavio Lupia 

flavio.lupia@crea.gov.it 

Giuseppe Pulighe 

giuseppe.pulighe@crea.gov.it 

CREA - Council for Agricultural Research 

and Economics - Roma (Italy) 


REPORT 

Mobile Robotics and Autonomous 

Mapping: Technology for a more 

Sustainable Agriculture 

by Eleonora Maset, Lorenzo Scalera, Diego Tiozzo Fasiolo 

Fig. 1 - The mobile robot traversing under the canopy in a 

maize field (Manish et al., 2022). 

Today, food systems account 

for nearly onethird 

of global greenhouse gas 

emissions, consume 

large amounts of natural 

resources and are among the 

causes of biodiversity loss. 

As part of the actions of 

the European Green 

Deal, the Farm to Fork 

Strategy (European Commission, 

2020) plays therefore a 

crucial role to reach the ambitious 

goal of making Europe a 

climate-neutral continent by 

2050. In fact, it aims to accelerate 

the transition towards a 

sustainable food system, reducing 

dependency on pesticides, 

decreasing excess fertilization 

and protecting land, soil, water, 

air, plant and animal health. All 

actors of the food chain need to 

contribute to the implementation 

of this strategy, starting 

from the transformation of 

production methods that can 

benefit from novel technological 

and digital solutions to deliver 

better environmental and climate 

results. 

In this context, we are witnessing 

an increasing demand for 

automated solutions to monitor 

and inspect crops and canopies, 

that are driving the adoption of 

autonomous and robotic systems 

with computational and 

logical capabilities. The introduction 

of robotics and automation, 

coupled with Geomatics 

techniques, could provide notable 

benefits not only in terms 

of crop production and land use 

optimization, but also to reduce 

the use of chemical pesticides, 

improving sustainability and 

climate performance through 

a more results-oriented model, 

based on the use of updated 

data and analyses. For these reasons, 

the implementation of autonomous 

and robotic solutions 

together with advanced monitoring 

techniques is becoming 

of paramount importance in 

view of a resilient and sustainable 

agriculture. 

Applications of mobile robotics 

in agriculture span from a large 

variety of tasks, as for instance, 

harvesting, monitoring, phenotyping, 

sowing, and weeding. A 

particular task in which mobile 

robots are currently employed 

at a faster pace than in previous 

years is 3D mapping, as testified 

by a flourishing literature 

on the topic (Tiozzo Fasiolo et 

al., 2022). Indeed, 3D maps of 

agricultural crops can provide 

valuable information about the 

health, stress, presence of diseases, 

as well as morphological and 

biochemical characteristics. Furthermore, 

3D surveys of plants 

and crops are fundamental in 

the computation of geometrical 

information, such as volume 

and height, to be used to reduce 

pesticide and fertilizer waste 

and water usage, and, therefore, 

improve sustainability and environmental 

impact. 

Obviously, to provide useful 

information for crop management 

and to perform the survey 

in the most automatic way possible, 

robotics platforms must 

be equipped with appropriate 

technology. In the following, we 

will therefore try to summarize 

trends and future developments 


REPORT 

in this domain. 

The first requirement of mobile 

robots in agriculture applications 

is the availability of onboard 

sensors and computational capabilities. 

Common sensors are 

2D and 3D LiDAR (Light Detection 

and Ranging), cameras 

(monocular, stereo, RGB-D, and 

time-of-flight ones), as well as 

RTK-GNSS (Real-Time Kinematics 

Global Navigation Satellite 

System) receivers, and IMUs 

(Inertial Measurement Units), 

the latter two used mainly for 

localization tasks. 

Among the robotic systems recently 

proposed in the literature 

for 3D mapping in agriculture, 

it is worth mentioning the platform 

developed in (Manish et 

al., 2021) and shown in Figure 

1. That system is capable of collaborating 

with a drone to build 

a dense point cloud of the field. 

Another interesting mobile robot 

is BoniRob (Figure 2), developed 

by Bosch Deepfield® Robotics 

(Chebrolu et al., 2017). It is an 

omnidirectional robot and carries 

a multispectral camera, able 

to register four spectral bands, 

and an RGB-D sensor to capture 

high-resolution radiometric data 

about the inspected plantation. 

Multiple LiDAR sensors and 

GNSS receivers as well as wheel 

encoders provide at the same 

time observations employed for 

localization, navigation, and 

mapping. An example of robot 

with an onboard manipulator 

is given by BrambleBee (Ohi et 

al., 2018). That robotic system 

features a custom end effector 

designed to pollinating flowers 

in a greenhouse. 

Images from standard RGB 

cameras only supply information 

about the plants in the visible 

spectrum. To investigate vegetation 

indexes related to the crop 

vigor, as for instance the NDVI 

(Normalized Difference Vegetation 

Index), multispectral and 

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

hyperspectral sensors are needed, 

which can measure the near 

infrared radiation reflected by 

the vegetation leaves. However, 

only a paucity of robotic platforms 

described in the literature 

manage to perform this task. 

The mobile lab developed at 

the Free University of Bolzano 

and shown in Figure 3 is among 

them (Bietresato et al., 2016). 

A prototype of mobile robot for 

3D mapping in agriculture is 

currently being developed at the 

University of Udine, based on 

an Agile-X Robotics Scout 2.0 

platform (Figure 4). The robot 

can navigate in harsh terrain and 

narrow passages thanks to its 

four-wheel drive and differential 

kinematics. The platform is 

equipped with a low-cost GNSS 

receiver and a 9-degree-offreedom 

(DOF) IMU as direct 

georeferencing systems. Moreover, 

it features a great computational 

capability thanks to the 

NVIDIA Jetson AGX Xavier 

board, developed to exploit artificial 

intelligence (AI) algorithms 

even in embedded systems. The 

perception of the environment 

is guaranteed by a rotating 360° 

LiDAR and an RGB-D camera. 

Finally, for phenotyping purposes 

it exploits a multispectral 

camera pointing forward to 

acquire information on the near 

infrared and the red edge portion 

of the light spectrum. 

As far as the sensorial and 

computational capabilities are 

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

and onboard sensors (Bietresato et al., 2016). 


REPORT 

Fig. 4 - The sensorized mobile platform developed at University of Udine, Italy. 

considered, from the literature 

it can be noticed that most of 

the robotic platforms operating 

in the agricultural environment 

usually employ physical devices 

to store the acquired data, whose 

can require a frequent manual 

intervention of an operator. The 

implementation on Internet 

of Things (IoT) approaches, 

together with the storage of 

data on clouds could be a great 

improvement, making data 

remotely available. Future improvements 

in this context will 

also include the integration of 

renewable energy sources, such 

as solar panels, to increase the 

autonomy of the systems, especially 

in large scale operations 

(e.g., autonomous 3D mapping 

of a whole vineyard). Moreover, 

to avoid occlusion problems 

that can occur in image-based 

phenotyping, sensors can be 

mounted on a robotic arm that 

can optimize the camera pose, 

guaranteeing the best point of 

view for data acquisition. However, 

it should also be underlined 

that eye-in-hand configurations 

for LiDAR sensors and multispectral 

cameras are not exploited 

yet. A further important 

aspect is the durability of these 

systems and sensors, that should 

be designed to operate in severe 

outdoor scenarios. 

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

To navigate autonomously in 

the surrounding environment, a 

mobile robot needs a robust localization 

method that can georeference 

the data acquired by 

means of the onboard sensors. 

Direct georeferencing methods 

are usually based on the RTK- 

GNSS, that provides position at 

low update rate, generally coupled 

with a 9-DOF IMU, which 

however is sensible to noise in 

rough terrains. Higher accuracy 

for the localization of the robot 

and the generated 3D map can 

be achieved using in addition 

Simultaneous Localization and 

Mapping (SLAM) approaches. 

As well-known also in the Geomatics 

community, SLAM problem 

consists in the estimation 

of the pose of the robot/sensor, 

while simultaneously building a 

map of the environment. Stateof-the-art 

methods are divided 

into two main groups: visual 

SLAM and LiDAR SLAM. The 

former approach relies on images 

and sequentially estimates 

the camera poses by tracking 

keypoints in the image sequence. 

The popular approach for Li- 

DAR SLAM is instead based 

on scan matching: the pose is 

retrieved by matching the newly 

acquired point cloud with the 

previously built map, which is 

constantly updated as soon as 

new observations are available. 

Although not yet fully implemented 

in mobile robots for precision 

agriculture applications, 

an optimal solution could be 

data fusion, taking the advantages 

of both visual and LiDAR 

SLAM methods. In addition, 

since external conditions can significantly 

vary among different 

application and the environment 

dictates the most advantageous 

sensor, the robot itself should be 

able to choose and use the most 

suited data source according to 

the environmental conditions. 

Many open-source SLAM algo- 


REPORT 

rithms are currently available, 

that can run in real time or in 

post-processing mode. To this 

regard, a comparison among 

the state-of-the-art SLAM algorithms 

could be interesting, 

together with a quantitative 

evaluation of the obtained 3D 

maps performed with respect 

to ground truth datasets. Conversely, 

there is a lack of methods 

to efficiently fuse spectral data 

acquired by multispectral and 

hyperspectral cameras with Li- 

DAR point clouds, fundamental 

for agriculture applications. 

Another important aspect that 

must be considered for the 

profitably application of mobile 

platforms is the autonomous 

navigation ability, guaranteed by 

path planning algorithms. Path 

planning is a mature field in 

mobile robotics, and, in the crop 

monitoring context, it generally 

consists of providing a global 

path to map an entire area. This 

approach is called coverage path 

planning and is usually coupled 

with a row-following algorithm 

to provide local velocity command 

to the robot. 

A recent trend in the coverage 

path planning is the development 

of algorithms that avoid 

repetitive paths to minimize soil 

compaction. This approach generally 

relies on prior information 

of the working area, that could 

be acquired thanks to the collaboration 

with drones, useful to 

capture an up-to-date 2D or 3D 

model of the environment. Furthermore, 

a promising solution 

could be extending the range 

of action with swarm robotics, 

that is the collaboration of several 

unmanned ground vehicles 

(UGVs). 

Nowadays, another fundamental 

aid for agricultural applications 

based on mobile robotics is given 

by artificial intelligence (AI). In 

fact, classification and segmentation 

algorithms of images and 

point clouds based on AI are increasingly 

used to enrich the 3D 

map with semantic information, 

also in real time. This is possible 

mostly thanks to the advances in 

the computational performance 

of modern embedded computers 

that can be installed onboard a 

mobile platform. 

For instance, a convolutional 

neural network (CNN) applied 

to the acquired images can provide 

bounding boxes of objects 

of interest, that can constitute 

the basis to build a topological 

map with key location estimation 

and semantic information. 

This is exploitable also to give 

the robot person following capability, 

as done in the work by 

(Masuzawa et al., 2017) (Figure 

5). Another example of machine 

learning application is given 

by the work in (Reina et al., 

2017), which employed a support 

vector machine to classify 

the terrain on which the robot 

is navigating, by means of wheel 

slip, rolling resistance, vibration 

response experienced by the 

mobile platform and visual data. 

Recent trends in this field also 

comprise the use of generative 

adversarial networks to generate 

photorealistic agricultural images 

for model training, as well as 

the recognition of diseases with 

CNN. 

In the coming years we will 

witness great progress in all the 

domains highlighted by this 

work, from sensors to mobile 

platforms, from localization algorithms 

to artificial intelligence 

methods, with the hope that 

these innovations will effectively 

contribute to the transition to 

a more sustainable, healthy and 

environmentally-friendly food 

system. 

REFERENCES 

European Commission (2020). Farm to Fork 

strategy for a fair, healthy and environmentallyfriendly 

food system. https://ec.europa.eu/food/ 

horizontal-topics/farm-fork-strategy_en 

Tiozzo Fasiolo, D., Scalera, L., Maset, E., 

Gasparetto, A. (2022). Recent Trends in Mobile 

Robotics for 3D Mapping in Agriculture. In International 

Conference on Robotics in Alpe-Adria 

Danube Region (pp. 428-435). Springer, Cham. 

Manish, R., Lin, Y. C., Ravi, R., Hasheminasab, 

S. M., Zhou, T., Habib, A. (2021). Development 

of a miniaturized mobile mapping system 

for in-row, under-canopy phenotyping. Remote 

Sensing, 13(2), 276. 

Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter, 

W., Burgard, W., Stachniss, C. (2017). 

Agricultural robot dataset for plant classification, 

localization and mapping on sugar beet 

fields. The International Journal of Robotics 

Research, 36(10), 1045-1052. 

Ohi, N., Lassak, K., Watson, R., Strader, J., Du, 

Y., Yang, C., et al. (2018). Design of an autonomous 

precision pollination robot. In 2018 IEEE/ 

RSJ international conference on intelligent robots 

and systems (IROS) (pp. 7711-7718). IEEE. 

Bietresato, M., Carabin, G., D'Auria, D., Gallo, 

R., Ristorto, G., Mazzetto, F., Vidoni, R., Gasparetto, 

A., Scalera, L. (2016). A tracked mobile 

robotic lab for monitoring the plants volume and 

health. In 2016 12th IEEE/ASME International 

Conference on Mechatronic and Embedded 

Systems and Applications (MESA). IEEE. 

Masuzawa, H., Miura, J., Oishi, S. (2017). Development 

of a mobile robot for harvest support 

in greenhouse horticulture—Person following 

and mapping. In 2017 IEEE/SICE International 

Symposium on System Integration (SII) (pp. 

541-546). IEEE. 

Reina, G., Milella, A., Galati, R. (2017). Terrain 

assessment for precision agriculture using vehicle 

dynamic modelling. Biosystems engineering, 162, 

124-139. 

KEYWORDS 

Mobile robotics; autonomous mapping; sustainable 

agriculture 

ABSTRACT 

The introduction of robotics and automation, 

coupled with Geomatics techniques, could 

provide notable benefits not only in terms of crop 

production and land use optimization, but also to 

reduce the use of chemical pesticides, improving 

sustainability and climate performance through 

a more results-oriented model, based on the use 

of updated data and analyses. For these reasons, 

the implementation of autonomous and robotic 

solutions together with advanced monitoring 

techniques is becoming of paramount importance 

in view of a resilient and sustainable agriculture. 

AUTHOR 

Eleonora Maset 

eleonora.maset@uniud.it 

Lorenzo Scalera 

lorenzo.scalera@uniud.it 

Diego Tiozzo Fasiolo 

diego.tiozzo@uniud.it 

Polytechnic Department of Engineering and 

Architecture (DPIA), University of Udine, 

Italy 


REPORT 

Geographical Information: the Italian 

Scientific Associations and... 

the Big Tech 

by Valerio Zunino 

In Italy, the Scientific 

Associations that deal with 

geographical data are simply 

not up to speed, and just a few 

of them have some sort of idea 

on how to get into it. The huge 

and mind-bending projects 

that Big Tech is dealing with in 

our same world of professional 

expertise, must be firstly 

understood -deeply-, weighted 

and brought to the table of 

our heritage of knowledge, 

culture, education, and lastly 

of consulting services that our 

Associations are required to 

offer to the national market. 

These days, we can no 

longer afford to pretend 

we can just barely 

glimpse the revolutionary contribution 

that has been made 

to the world of professionals 

around the planet by the big 

techs (in our sector), among 

others, through their generalist 

geographic portals. 

Nor can we continue to brand 

as scientific approximation their 

method in strategically tackling 

that world that we, Scientific 

Associations, together with the 

most established companies 

in the sector, believed we were 

presiding over with the exclusivity 

of knowledge and the 

most advanced technology: the 

time it takes to access an immense 

amount of geographical 

data residing on the internet 

has soon become one tenth of 

what we used to expect, and our 

locking up within our Tender 

Special Specifications will simply 

make us less credible in the 

eyes of that growing audience of 

subjects that we call users, and 

which evidently represents the 

market of the Associates that we 

have a duty to approach, starting 

with small and mediumsized 

enterprises and ending 

with the individual professional 

who is struggling at work and 

in life. If we do not do this, we 

will disappear. 

The technological framework 

and business model outlined 

by those companies that today 

- whether one accepts it or 

not - mark the times, methods 

and rules of consultation and 

processing of the vast majority 

of published geographical data, 

and that have so far often been 

seen as a kind of obstruction 

to the scientific conversation, 

it should be clearly stated as of 

now that they will have to be 

carefully studied and if possible 

brought to the table of the 

Associations, so as to first and 

foremost qualify them. It is necessary 

to at least begin to show 

a general humility on the contents, 

seek an encounter with 

the Industry and attempt to 

generate a global, adjustable and 

- even more urgently- mutual 

learning, that is for us the only 

effective and possible means of 

sharing. 

On 11 June 2001, the global 

market witnessed the release of 

Google Earth. It took none of 

us 'insiders' more than a couple 

of minutes, the time to get to 

grips with the surprise effect, 

to measure an extraordinary 

performance, that was not com- 


REPORT 

parable (for how far it was superior) 

to that of products such 

as Autodesk Mapguide or the 

direct competitors of the time, 

positioned by ESRI, Intergraph 

or Bentley, at the time leading 

tools dedicated to the web consultation 

of raster and vector geographic 

data. The very concept 

of raster was debased in just a 

few moments, almost as if it 

had been overtaken by still unknown 

words, which had been 

able to refer to and perhaps 

even describe the practicality 

and simplicity of the dynamic 

and intelligent management of 

remote sensed images, which 

populated this formidable application 

capable of occupying 

the globe in a representation 

without geographical hesitation 

and continuity. 

Earth then remained essentially 

the same, integrating some 

interesting functionalities over 

time, which, however, could not 

affect the first violent impact of 

product innovation. It is a fact 

that the other Big Techs were 

not able to respond rapidly, and 

did not want to do so in the 

years immediately following. 

Consequently, it is precisely 

from 2001 onwards that Google 

began to dig a trench that for 

a while increased in width (in 

terms of the quantity of the 

information entered) and in 

depth (in terms of its quality 

and geographical accuracy); 

then, the depth was filled by a 

number of competitors, as a result 

of which the big geographical 

data market was hit by an 

unprecedented rivalry based on 

quality: an arms race that saw 

first Apple enter the game, with 

the initial and fundamental 

support of a very strong and acclaimed 

segment brand as Tom- 

Tom still is... then Amazon, and 

closely followed by Facebook 

and Microsoft, all of which, 

although leading the growth of 

a robust proprietary mapping 

sector, are to a greater or lesser 

extent still heavily anchored, 

and we are talking about truly 

significant investments, to a 

global geocartographic system, 

probably born (Joe Morrison) 

out of a conversation between 

recent graduates in an English 

pub in 2004, and whose commercial 

value is now out of control: 

OpenStreetMap. 

Of the interesting and singular 

reasons that have at the moment 

prevented the big corporations 

from replicating OSM, 

suggesting them instead to 

invest in it by bringing in their 

own teams of editors, we will 

perhaps speak on another occasion. 

What seems more on topic 

now, however, is to report on 

the evolutionary framework of 

OSM content at the hands of 

the big brands of the IT world. 

Within the geographic areas 

(States, regions, crucial Human 

Settlements, etc.) where the 

white collar teams of the big 

names are active, the average 

incidence of editors operating 

on a voluntary basis is today less 

than 25% of the entire OSM 

geographic road/building data 

operation, whereas in 2017 this 

figure was around 70% (Jennings 

Anderson). Now, given 

that the big corporations are 

in this way impoverishing the 

ideological path from which the 


REPORT 

spirit of community that inspired 

the birth of the great free 

geographic portal had sprung, 

it remains to be seen what else 

these corporations are doing at 

the moment, each on their own 

account and more or less with 

the headlights off. 

Otherwise known as 

F.A.A.N.G. (Facebook, 

Amazon, Apple, Netflix and 

Google), sometimes with the 

inclusion of Microsoft, these 

are the Big Techs that for some 

time have been influencing our 

behavior, our choices, our purchases 

and probably even our 

attitudes. 

In the vicinity of Seattle (by the 

way, not without the support 

of the large Indian headquarters 

in Hyderabad, announced 

in September 2019 and now 

operating with about 15,000 

engineers, structured in an 

area of almost 170,000 square 

meters) they are working on at 

least two fronts (we are talking 

about mapping, of course): 

on the one hand, the Amazon 

Location Service project, born 

together with ESRI and HERE 

Technology B.V. from a rib of 

Amazon Web Service, the latter, 

today a cloud computing platform 

with a major competitive 

advantage over the analogues 

provided by Microsoft (Azure) 

and Google (GCP). The abovementioned 

partnership serves 

to fill the gap that Amazon, like 

the others, also suffers in terms 

of proprietary cartography (or 

acquired in perpetual license 

without disbursement of any 

fee for the benefit of the relevant 

suppliers): but while ESRI 

makes available to its partner 

some high-definition satellite 

databases, HERE contributes 

through the provision of its 

own geographic vectorial data 

referring in particular to road 

circulation, real-time traffic and 

address location. 

Of course, partners receive payment 

on an on-demand (click) 

basis whenever the Amazon 

Location Service user performs 

an operation on geography, processes 

a route request, performs 

a different search, etc... And it 

is also for this reason that Amazon 

is also moving on its own 

account, in order to free itself 

from such costs. As is typical for 

big brands, once the allurement 

of a market has been verified, it 

is considered a serious strategic 

mistake to wait too long before 

being present, consequently 

Amazon Location Services, like 

other platforms, simply had to 

be born, necessarily together 

with selected and established 

partners in the segment. So, 

the goal was to get into it right 

away, more or less, to steady a 

service and then innovate and 

improve it, just as it was battling 

on market share points 

against its longtime competitors. 

Before long, Amazon will 

reduce the quantitative contribution 

of its consume-based 

geographic data providers, and 

resubmit a quasi-proprietary 

version of Amazon Location 

Services on the most important 

element, the mapping system: 

you can bet on it. 

Facebook and Microsoft are also 

investing in the same endeavor 

to reduce the – as the present 

day - gross imbalance between 

third-party geographic data and 

data owned or acquired outright 

without conditions on publication; 

the platforms are called, 

for the uninitiated, Facebook 

Maps and Microsoft Azure 

Maps, respectively. But while 

Zuckenberg's creature (today 

“Meta”) does not seem to show 

any particular interest in a race 

for proprietary geographic information 

endowments - if not 

for certain categories or thematic 

classes - thereabout Redlands 

we are today witnessing an 

interesting acceleration, which 

also concerns 2D buildings, 

published and also made available 

in Opendata just in the last 

few weeks by the Bing subsidiary 

and with reference to a long 

series of countries, including 

Italy. The fullness of the data is 

good (we tested it for parts of 

our country) and the quality 

certainly more than decent. 

And while what happens in 

Mountain View is always 


REPORT 

somewhat surrounded by that, 

more or less, invisible aura of 

mystery that almost always intrigues, 

Cupertino, eventually, 

shows detail and quality with 

the famous proprietary map of 

California, representing and 

symbolizing down to individual 

plants, in some cities. Apple 

New Map is indeed the qualitative-quantitative 

manifesto, 

reporting the formidable global 

geographic wishes of the brand 

founded by Steve Jobs. From a 

strictly GIS-oriented point of 

view, it is the most ambitious 

project. 

In conclusion, will we, as Associations 

for the protection, 

dissemination, and comparison 

of geographic data in Italy, be 

able to keep up with the pace of 

a category credibility that envisages, 

without compromises, a 

greater openness of our awareness 

and a more convincing 

manifestation of our somewhat 

repressed humility? 

These are reactions that are neither 

easy nor quick, but necessary. 

To young people, who are 

entering the world of the Associations 

federated in ASITA we 

say and recommend that they 

open up, open their vision of 

the market that will one day be 

theirs alone, in the direction of 

others, Public sector, Big Tech, 

Professional world, international 

segment majors, national 

Industry, etc... Geoinformation, 

sooner or later, will have to 

become one: we better realize it 

sooner than later. 

KEYWORDS 

ASITA; Geographic information; big 

tech; GIS; AMFM; location services 

ABSTRACT 

The world is changing. The Italian 

Scientific Associations of Geographical 

Information are not. The opportunities 

are endless, but what is missing 

is humility and ideas, and often these 

two shortcomings feed off each other. 

AUTHOR 

Valerio Zunino 

valerio.zunino@studiosit.it 

Vice-President Association AMFM 

GIS Italia 


Rhine River, Germany 

The Rhine River, the longest river in Germany, is 

featured in this colourful image captured by the Copernicus 

Sentinel-2 mission. The Rhine River, visible here in black, flows from 

the Swiss Alps to the North Sea through Switzerland, Liechtenstein, Austria, 

France, Germany, and the Netherlands. In the image, the Rhine flows from 

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

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

Rhine Valley has many forested hills topped with castles and includes vineyards, quaint 

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

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

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

created by combining three separate Normalised Difference Vegetation Index (NDVI) layers from 

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

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

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

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

June and July, while blue shows August and September. Colourful squares, particularly visible 

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

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

unlike crops which are planted and harvested at set time frames. Light pink areas are 

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

water bodies. 

[Credits: contains modified Copernicus Sentinel data (2018-21), 

processed by ESA - Translation: Gianluca Pititto]

REPORT 

Time and Longitude: 

an unexpected affinity 

by Marco Lisi 

Time, the fourth dimension, is becoming increasingly important in all 

aspects of technology and science. 

The generation and distribution of an accurate reference time is a 

strategic asset on which the most disparate applications depend: from 

financial transactions to broadband communications, from satellite 

navigation systems to large laboratories for basic physical research (the 

so-called "Big Physics "). 

But time is also the dimension through which technology evolves (as, 

for example, in the case of Moore's law which describes the increase in 

complexity of integrated electronic circuits) and obsolescence spreads. 

Obsolescence will be the great challenge, often ignored or 

underestimated, that the economically and technologically advanced 

societies of the world will have to face in the years to come. The more 

technology increases its evolutionary pace, the more things that 

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

with each other and be maintained. 

Maintenance and updating of obsolete parts (the so-called “logistics”) 

are essential aspects in the operational life of a system and both have to 

do with time. 

The importance of a precise 

time reference in our society 

and economy 

The determination and the accurate 

measurement of time are 

the basis of our technological 

civilization. The major advances 

in this field have taken place 

in the last century, with the 

invention of the quartz crystal 

oscillator in 1920 and the first 

atomic clocks in the 40s. Nowadays 

time measurement is by far 

the most accurate among the 

measures of other fundamental 

physical quantities. Even the 

measurement unit for lengths, 

once based on the mythical reference 

meter, a sample of Platinum-Iridium 

preserved in Paris, 

was internationally redefined 

in 1983 as "the length of the 

path covered by light in vacuum 

during a time interval equal to 

1/299792458 of a second ". 

The second (symbol “s”) is the 

unit of measurement of the official 

time in the International 

System of Units (SI). Its name 

comes simply from the second 

division of the hour, while the 

minute is the first. The second 

was originally defined as the 

86400-th part of the mean solar 

day, i.e., the average, taken over 

a year, of the solar day, defined 

as the time interval elapsing between 

two successive passages of 

the Sun on the same meridian. 

In 1884 the Greenwich Mean 

Time (GMT) was officially 

established as the international 

standard of time, defined as the 

mean solar time at the meridian 

passing through the Royal 

Observatory in Greenwich 

(England). 


REPORT 

GMT calculates the time in each 

of the 24 zones (time zones) into 

which the earth's surface has 

been divided. The time decreases 

by one hour for each area west of 

Greenwich, and increases by one 

hour going east. GMT is also 

defined as "Z" time, or, in the 

phonetic alphabet, "Zulu" time. 

The time standard underlying 

the definition of GMT was 

maintained until astronomers 

discovered that the mean solar 

day was not constant, due to the 

slow (but continuous) slowdown 

of the Earth's rotation around 

its axis. This phenomenon is essentially 

linked to the braking 

action of the tides. It was then 

decided to refer the average solar 

day to a specific date, that of 

January 1, 1900. This solution 

was very impractical since it is 

not possible to go back in time 

and measure the duration of that 

particular day. 

In 1967 a new definition of the 

second was proposed, based on 

the motion of precession of the 

isotope 133 of Cesium. The second 

is now defined as the time 

interval equal to 9192631770 

cycles of the vibration of Cesium 

133. This definition allows scientists 

anywhere in the world 

to reconstruct the duration of 

the second with equal precision 

and the concept of International 

Atomic Time or TAI is based on 

it. 

The first atomic clock was developed 

in 1949 and was based 

on an absorption line of the 

ammonia molecule. The cesium 

clock, developed at the legendary 

NIST (National Institute 

of Standards and Technology) 

in Boulder, Colorado, can keep 

time with an accuracy better 

than one second in six million 

years. It was precisely the 

extreme accuracy of atomic 

clocks that led to the adoption 

of atomic time as an official reference 

worldwide. However, a 

Fig. 1 - UTC and critical infrastructures. 

Fig. 2 - Stonehenge, a prehistoric astronomical observatory. 

new problem was been indirectly 

generated: the discrepancy between 

the international reference 

of time, based as mentioned on 

atomic clocks, and the average 

solar time. An average solar year 

increases by about 0.8 seconds 

per century (i.e., about an hour 

every 450,000 years). Consequently, 

universal time accumulates 

a delay of approximately 1 

second every 500 days compared 

to international atomic time. 

This means that our distant 

great-grandchildren, in the 

distant future just 50,000 years 

from now, would read “noon” on 

their atomic clocks, even though 

they are actually in the middle of 

the night. To overcome this and 

many other more serious drawbacks, 

the concept of Universal 

Coordinated Time (UTC) was 

introduced in 1972, which definitively 

replaced GMT. 

In the short term, UTC essentially 

coincides with atomic time 

(called International Atomic 

Time, or TAI); when the difference 

between UTC and TAI approaches 

one second (this occurs 

approximately every 500 days), 

a fictitious second, called "leap 

second", is introduced. 

In this way, the two time-scales, 

TAI and UTC, are kept within 

a maximum discrepancy of 0.9 

seconds. 

UTC ("Universal Coordinated 

Time"), defined by the historic 

“Bureau International des Poids 

et Mesures” (BIPM) in Sevres 


REPORT 

Fig. 3 - Ancient Egyptian stone obelisks. 

(Paris), is since 1972 the legal 

basis for the measurement of 

time in the world, permanently 

replacing the old GMT. It is 

derived from TAI, from which it 

differs only by an integer number 

of seconds. TAI is in turn 

calculated by BIPM from data 

of more than 200 atomic clocks 

located in metrology institutes 

in more than 30 countries over 

the world. 

But why is it so important to 

have an accurate and unambiguous 

definition of time? 

It is a matter not only for scientists 

and experts. A universally 

recognized and very accurate 

reference time is in fact at the 

base of most infrastructures of 

our society (figure 1). 

All cellular and wireless networks, 

for example, are based on 

careful synchronization of their 

nodes and base stations (obtained 

receiving GNSS signals, 

as we will see). The same is true 

for electric power distribution 

networks. Surprisingly, even 

financial transactions, banking, 

and stock markets all depend on 

an accurate time reference, given 

the extreme volatility in equity 

and currency markets, whose 

quotations might vary within a 

few microseconds. 

Time and its measurement 

The history of the measurement 

of time is as old as the history of 

human civilization. 

In prehistoric England, the megalithic 

monument of Stonehenge 

seems to have been a sophisticated 

astronomical observatory 

to determine the length 

of the seasons and the date of 

the equinoxes (figure 2). 

Already in 3500 BC the ancient 

Egyptians invented the 

sundial and erected stone obelisks 

throughout their country 

which had the primary purpose 

of marking the movement of 

the sun with their shadow and, 

therefore, the passage of time 

(figure 3). 

In ancient Roman times and up 

until late in the Medieval Age, 

sundials, marked candles, water 

and sand hourglasses were used 

to measure time (figure 4). 

A milestone in the history of the 

measurement of time was, in 

more recent times, Galileo's discovery, 

in 1583, of the constancy 

of the pendulum swing period, 

on which all mechanical clocks 

are based (figure 5). 

In 1656 Christiaan Huygens, 

Dutch mathematician, astronomer, 

and physicist (famous 

among other things for having 

defined the principle of diffraction 

that bears his name) 

designed the first weight-wound 

pendulum clock, which deviated 

by ten minutes a day (figure 6). 

But the major impetus for the 

development of ever more accurate 

techniques for measuring 

time came from the need to 

determine one's position (particularly 

longitude) aboard a 

ship in the open sea. From then 

on, time and positioning became 

irreversibly connected. 

Fig. 4 - Roman and Medieval time measurement methods. 

“Longitude problem” and 

measurement of time 

The latitude and longitude coordinate 

system is commonly used 

to determine and describe one’s 

position on Earth’s surface and it 

was also known by astronomers 


REPORT 

and navigators since the Greek 

and Roman times. 

Determining latitude north or 

south with respect to the equator 

posed no major problems: 

it could be calculated through 

angular measurements of the sun 

and stars made with relatively 

simple instruments. 

Measuring longitude, that is, 

identifying the east-west position 

on Earth between meridians, 

lines running from pole to pole, 

was a completely different story. 

Longitude was far more difficult 

than latitude to measure by astronomical 

observation. 

Because of the Earth’s rotation, 

the difference in longitude between 

two locations is equivalent 

to the difference in their local 

times: one degree of longitude 

equals a four-minute time difference, 

and 15 degrees is equal to 

one hour (making 360 degrees, 

or 24 hours, in total). 

While a sextant with which to 

determine the height of the sun 

at noon was sufficient to determine 

one's latitude, the determination 

of longitude, due to the 

earth's rotation, required the use 

of both the sextant and a very 

precise clock. 

Several methods had been 

proposed over the centuries by 

scientists and astronomers (including 

Galileo and Newton), all 

based on the observation of specific 

astronomical events, such as 

lunar eclipses. 

All these methods turned out 

to be rather cumbersome and 

inaccurate by several hundred 

kilometers. 

Even Christopher Columbus 

made two attempts to use lunar 

eclipses to discover his longitude, 

during his voyages to the 

New World, but his results were 

affected by large errors. 

The lack of an accurate longitude 

determination method created 

innumerable problems (at 

times, real disasters) for sailors of 

Fig. 5 - Galileo Galilei discovered in 1581 the isochronism of the pendulum. 

the 15th and 16th centuries. 

At the beginning of the eighteenth 

century, with the rapid 

growth of maritime traffic, a 

sense of urgency had arisen. The 

search for longitude cast a shadow 

over the life of every man at 

sea, and the safety of every vessel 

and merchant ship. 

The exact measurement of longitude 

seemed at that time an 

impossible dream, a sort of perpetual 

motion machine. 

There was a need for an instrument 

that recorded the time (at 

the place of departure) with the 

utmost precision during long sea 

voyages, despite the movement 

of the ship and the adverse climatic 

conditions of alternating 

Fig. 6 - Christiaan Huygens and the first pendulum clock. 

hot and cold, humid and dry. 

On the other hand, seventeenthcentury 

and early eighteenthcentury 

clocks were crude devices 

that usually lost or gained up 

to a quarter of an hour a day. 

The “longitude problem” however 

became so serious that in 

1714 the British Parliament 

formed a group of well-known 

scientists to study the solution, 

the “Board of Longitude”. The 

Board offered twenty thousand 

pounds, equivalent to more than 

three million pounds today, to 

anyone who could find a way 

to determine the longitude of a 

ship on the open sea with accuracy 

within one-half of a degree 

(thirty nautical miles, about 


REPORT 

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

Fig. 8 - James Cook’s Pacific Voyages. 

Fig. 9 - Galileo Passive Hydrogen Maser (PHM) clock. 

fifty-five kilometers, at the equator). 

The approach was successful, 

despite the many (and often 

completely crazy) proposals. In 

fact, in 1761, a self-educated 

Yorkshire carpenter and amateur 

clock-maker named John Harrison 

built a special mechanical 

clock to be loaded on board 

ships, called the “marine chronometer”, 

capable of losing or 

gaining no more than one second 

per day (an incredible accuracy 

for that time) (figure 7). 

Harrison did not receive the 

prize from the Board until after 

fighting for his reward, finally 

receiving payment in 1773, after 

the intervention of the British 

parliament. 

And it was thanks to a copy of 

Harrison's H4 chronometer 

that Captain James Cook made 

his second and third legendary 

explorations of Polynesia and 

the Pacific islands on board the 

HMS Resolution (figure 8). 

A copy of the H4 chronometer 

was also used in 1787 by 

Lieutenant William Bligh, commander 

of the famous HMS 

Bounty, but it was retained by 

Fletcher Christian following his 

mutiny. It was later recovered 

in Pitcairn Island to eventually 

reach the National Maritime 

Museum in London. 

GNSS and Timing 

An extremely accurate UTC reference 

is today provided worldwide 

by satellite navigation 

systems (GNSS) such as GPS 

(Global Positioning System), 

GLONASS, Beidou, and the 

European Galileo system. They 

are systems of satellites orbiting 

around the Earth, each containing 

onboard extremely precise 

atomic clocks which are all synchronized 

to a system reference 

clock. 

GNSS technologies are intrinsically 

linked to accurate timing. 


REPORT 

This is because of the specific 

principle (trilateration) on which 

position determination is based, 

i.e., the method of measuring 

the distance of a user from each 

satellite, involving the measurement 

of the time delay experienced 

by the signal-in-space. 

The most accurate and numerous 

atomic clocks around the 

world are those belonging to 

GNSS, thus contributing substantially 

to the derivation of 

TAI and UTC (figure 9). 

UTC can be derived from 

the Galileo and GPS signals, 

through a series of corrections 

based on data provided by the 

signals themselves. The accuracy 

obtainable, even with very cheap 

commercial receivers (or in 

those integrated into our smartphones) 

is easily better than one 

microsecond. 

KEYWORDS 

GNSS; GPS; Galileo; GLONASS; Beidou; time; longitude; GMT; TAI; UTC; 

ABSTRACT 

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

and experts. A universally recognized and very accurate reference time is in fact at the 

base of most infrastructures of our society. All cellular and wireless networks, for example, 

are based on careful synchronization of their nodes and base stations (obtained receiving 

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

Surprisingly, even financial transactions and banking and stock markets all depend on 

an accurate time reference, given the extreme volatility in equity and currency markets, 

whose quotations might vary within a few microseconds. The history of the measurement 

of time is as old as the history of human civilization. But the major impetus for the 

development of ever more accurate techniques for measuring time came from the need to 

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

a self-educated Yorkshire carpenter and amateur clock-maker named John Harrison built 

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

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

that time). From then on, time and positioning became irreversibly connected. 

AUTHOR 

Marco Lisi 

ingmarcolisi@gmail.com 

Time to THE FUSION ! 

BLK2GO & X-PAD OFFICE FUSION 

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

©2022 Hexagon AB and/or its 

subsidiaries and affiliates. 

All rights reserved. 

Part of Hexagon 

GEOmedia n°3-2022 31 

Works when you do

NEWS 

in January 2021. In 

December 2020, the 

Space Development 

Agency selected L3Harris 

to build and launch four 

space vehicles to demonstrate 

the capability to 

detect and track ballistic 

and hypersonic missiles. 

L3HARRIS INFRARED SPACE 

TECHNOLOGY TO ENHANCE 

BATTLEFIELD IMAGERY AND 

MISSILE DEFENCE DETECTION 

L3Harris is providing the instrument as part of a 

wide-field-of-view satellite that also will help inform 

future space-based missile defense missions 

and architectures. The satellite will be positioned 

22,000 miles from Earth, enabling the infrared system 

to see a wide swath and patrol a large area for 

potential missile launches. 

“The L3Harris instrument can stare continuously 

at a theater of interest to provide ongoing information 

about the battlespace, which is an improvement 

over legacy systems,” said Ed Zoiss, 

President, Space & Airborne Systems, L3Harris. 

“It also provides better resolution, sensitivity and 

target discrimination at a lower cost.” 

The instrument was built for Space Systems 

Command and is integrated into a Millennium 

Space Systems satellite, scheduled to launch from 

Cape Canaveral, Florida. The payload, which is 

more than six feet tall and weighs more than 365 

pounds, was developed in Wilmington, Mass. 

L3Harris is prioritizing investments in space-based 

missile defense programs and has accelerated 

the development of resilient, end-to-end satellite 

solutions in spacecraft, payloads and ground software, 

and advanced algorithms. 

In a related effort, the Missile Defense Agency 

awarded L3Harris a missile-tracking study contract 

in 2019 and the prototype demonstration 

About L3Harris 

Technologies 

L3Harris Technologies is 

an agile global aerospace 

and defense technology 

innovator, delivering 

end-to-end solutions that 

meet customers’ missioncritical 

needs. The company 

provides advanced defense and commercial 

technologies across space, air, land, sea and cyber 

domains. L3Harris has more than $17 billion in 

annual revenue and 47,000 employees, with customers 

in more than 100 countries. L3Harris. 

com. 

Forward-Looking Statements 

This press release contains forward-looking statements 

that reflect management's current expectations, 

assumptions and estimates of future performance 

and economic conditions. Such statements 

are made in reliance upon the safe harbor provisions 

of Section 27A of the Securities Act of 1933 

and Section 21E of the Securities Exchange Act of 

1934. The company cautions investors that any 

forward-looking statements are subject to risks 

and uncertainties that may cause actual results 

and future trends to differ materially from those 

matters expressed in or implied by such forwardlooking 

statements. Statements about the value or 

expected value of orders, contracts or programs 

are forward-looking and involve risks and uncertainties. 

L3Harris disclaims any intention or obligation 

to update or revise any forward-looking 

statements, whether as a result of new information, 

future events, or otherwise. 


NEWS 

EMLID RELEASED THE PPK APP—EMLID 

STUDIO FOR MAC AND WINDOWS 

Emlid announced new PPK software—Emlid Studio. It’s a 

cross-platform desktop application designed specifically for 

post-processing GNSS data. The app is free and available 

for Windows and Mac users. 

Emlid Studio features a simple interface that makes postprocessing 

easier than ever. The app allows users to convert 

raw GNSS logs into RINEX, post-process static and kinematic 

data, geotag images from drones, including DJI, and 

extract points from the survey projects completed with the 

ReachView 3 app. 

With Emlid Studio, you can post-process data recorded 

with Emlid Reach receivers and other GNSS receivers 

or NTRIP services. For post-processing, you will need 

RINEX observation and navigation files. You can also use 

raw data in the UBX and RTCM3 format—Emlid Studio 

will automatically convert them into RINEX. 

The post-processing workflow is very straightforward. 

You can receive precise positioning of a single point or 

track depending on your positioning mode. Just add several 

RINEX files and enter the antenna height. Click the 

Process button, and Emlid Studio will do the rest. Once 

the resulting position file is ready, you will see the result 

on the plot. 

One more tool is available for the users of Reach receivers 

and the ReachView 3 app. The Stop & Go feature allows 

you to improve the coordinates of points collected in Single 

or Float modes. 

Another helpful feature is geotagging for drone mapping. 

To add geotags to the images’ EXIF data, you’ll need aerial 

photos and the POS file with the events. Emlid Studio also 

provides a chance to update your data from the RTK drone 

in case you had a float or single solution during your 

survey. You will need a set of RINEX logs from a base and 

drone, MRK file, and images from the drone. Just drag and 

drop data in the file slots and you’ll see the result in a few 

seconds. 

To start using Emlid Studio, simply download the app for 

your computer—either Windows or macOS. To learn 

more about Emlid Studio features, visit the Emlid website. 

There's life in our world 

We transform and publish data, metadata 

and services in conformance to INSPIRE 

We support Data Interoperability, 

Open Data, Hight Value Datasets, 

APIs, Location Intelligence, Data Spaces 

INSPIRE Helpdesk 

We support all INSPIRE implementers 

Epsilon Italia S.r.l. 

Viale della Concordia, 79 

87040 Mendicino (CS) 

Tel. (+39) 0984 631949 

info@epsilon-italia.it 

www.epsilon-italia.it 

www.inspire-helpdesk.eu 


NEWS 

TOPCON REPRESENTS CONSTRUCTION 

INDUSTRY IN "CAMPUSOS" 5G RESEARCH 

PROJECT 

Topcon Positioning Germany is one of 22 partners involved 

in CampusOS; a research project with the goal of developing 

a modular ecosystem for open 5G campus networks 

based on open radio technologies and interoperable 

network components. As part of the German technology 

program titled "Campus networks based on 5G communication 

technologies," innovative solutions for open 5G 

networks are being developed and tested in conjunction 

with the German Federal Ministry for Economic Affairs 

and Climate Protection. The program was launched at the 

beginning of 2022 and will run through 2025. 

The use of artificial intelligence in the operation of autonomous 

plants and construction machinery requires the 

highest level of digital sovereignty. If Construction 4.0, including 

far-reaching automation, is to become a reality in 

Germany and the rest of the world, the processes of such 

data-driven solutions must run reliably, quickly and autonomously. 

Sponsored by the Federal Ministry of Economics 

The German Federal Ministry for Economic Affairs and 

Climate Protection is providing around 18.1 million euros 

in funding for the technology program over the next three 

years, which will cost around 33 million euros in total. The 

Fraunhofer Institutes FOKUS and HHI are coordinating 

the project. 22 partners from industry and research are involved. 

They include Deutsche Telekom, Siemens, Robert 

Bosch and more. 

"To enable companies to operate their own campus networks, 

certain requirements must be met; from standardized 

technology building blocks to network structures. As the 

sole representative of the construction industry, Topcon 

will test the technologies on reference test sites and therefore, 

will help shape the solutions for the future," explains 

Ulrich Hermanski, Chief Marketing Officer of the Topcon 

Positioning Group. "We look forward to working with our 

research partners to take the digital construction site to the 

next level." 

The future of the construction industry is digital 

With this research project, construction companies will one 

day be able to operate plants and machinery autonomously 

in open campus networks. This will allow the fluid and 

uninterrupted monitoring of construction sites in real time, 

as well as the networking of all sensors and construction 

machines in use on construction sites. 

Completely autonomous from public networks, 5G technology 

guarantees seamless machine-to-machine communication 

and transmits data ten times faster than 4G. 

The campus networks required for this, based on 5G frequencies, 

are practically digital ecosystems. They operate 

with open radio technologies and dialog-enabled components. 

The campus networks are geographically limited and 

can operate on a factory floor or on a construction site. 

Hermanski explains: "We will put a lot of time and energy 

into this project, because 5G campus networks are an important 

key technology for the construction site of the future." 

Lead project CampusOS: The consortium and its partners 

In addition to Topcon Deutschland Positioning GmbH, 

the collaborative partners of the CampusOS lead project include: 

atesio GmbH, brown-iposs GmbH, BISDN GmbH, 

Robert Bosch GmbH, Deutsche Telekom AG, EANTC 

AG, Fraunhofer Institutes FOKUS and HHI (project coordinators), 

GPS Gesellschaft für Produktionssysteme 

GmbH, highstreet technologies GmbH, Kubermatic 

GmbH, MUGLER SE, Node-H GmbH, Rohde & Schwarz 

GmbH, rt-solutions. de GmbH, Siemens AG, Smart 

Mobile Labs AG, STILL GmbH, SysEleven GmbH, the 

Technical University of Berlin and the Technical University 

of Kaiserslautern. 

About Topcon Positioning Group 

Topcon Positioning Group is an industry leading designer, 

manufacturer and distributor of precision measurement 

and workflow solutions for the global construction, geospatial 

and agriculture markets. Topcon Positioning Group 

is headquartered in Livermore, California, U.S. (topconpositioning.com, 

LinkedIn, Twitter, Facebook). Its European 

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

Corporation (topcon.com), founded in 1932, is traded on 

the Tokyo Stock Exchange (7732). 


NEWS 

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NEWS 

GALILEO GNSS FOR THE ASSET MAPPING 

PLATFORM FOR EMERGING COUNTRIES 

ELECTRIFICATION 

The purpose of the AMPERE (Asset Mapping Platform for 

Emerging countRies Electrification) project is to provide 

a dedicated solution for electrical power network information 

gathering. AMPERE can actually support decision making 

actors (e. g. institutions and public/ private companies 

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In particular, the need for such a solution comes in emerging 

countries where, despite global electrification rates are 

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from being achieved in a reliable way. Indeed, the challenge 

facing such communities goes beyond the lack of infrastructure 

assets: what is needed is a mapping of already deployed 

infrastructure (sometime not well known!) in order 

to perform holistic assessment of the energy demand and 

its expected growth over time. In such a context, Galileo 

is a key enabler -especially, considering its free-of-charge 

High Accuracy Service (HAS) and its highly precise E5 

AltBOC code measurements- as a core component to map 

electric utilities, optimise decision making process about the 

network development and therefore increase time and cost 

efficiency, offering more convenient way to manage energy 

distribution. These aspects confer to the AMPERE project 

a worldwide dimension, having European industry the clear 

role to bring innovation and know-how to allow network 

intervention planning with a limited afforded financial risk 

above all for emerging non-European countries. 

AMPERE proposes a solution based on a GIS Cloud mapping 

technology, collecting on field data acquired with 

optical/thermal cameras and LIDAR installed on board a 

Remote Piloted Aircraft (RPA). In particular, an RPA will 

be able to fly over selected areas performing semi-automated 

operations to collect optical and thermal images as well 

as 3D LiDAR-based reconstruction products. Such products 

are post processed at the central cloud GIS platform 

allowing operators in planning and monitoring activities 

by means of visualization and analytics tools can resolve 

data accessibility issues and improve the decision-making 

process. On this context, EGNSS represents an essential 

technology ensuring automated operations in a reliable 

manner and guaranteeing high performance. 

AMPERE use Galileo advanced features -namely, High 

Accuracy Service (HAS) and E5 AltBOC- as a core element 

of the added-value asset mapping proposition. The nature 

of HAS is fitting very well the requirements of this application, 

especially due to the re-shaping of the once fee-based 

accuracy capability to an open, free-of-charge service delivering 

around 20 centimeter accuracy, versus the belowten-centimeters 

PPP services, in lower convergence time. 

The key of Galileo HAS stands upon the high bandwidth of 

its E6-B channel, well suited to transmit PPP information, 

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Additionally, the use of E5 AltBOC pseudo-ranges (which 

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order of 1 m) supports fast ambiguity resolution for carrier 

phase observations. The Alternative BOC, (AltBOC) 

modulation on E5, is one of the most advanced signals the 

Galileo satellites transmit. Galileo receivers capable of tracking 

this signal will benefit from unequalled performance 

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The market is responding actively and positively to multi-frequency 

enhanced capabilities provided by Galileo. 

Around 40% of receiver models on the market are now 

multi-frequency. Also, in the mass market, with the launch 

of the world’s first dual-frequency GNSS smartphone by 

Xiaomi, and u-blox, STM, Intel and Qualcomm launching 

their first dual-frequency products earlier this year, multifrequency 

is becoming a reality for user needing increased 

accuracy. 

More information on: https://h2020-ampere.eu 


NEWS 

RHETICUS® NETWORK ALERT TO ASSIST 

UNITED UTILITIES OF ENGLAND 

CHC Navigation (CHCNAV) today announced the availability 

of the i73+ Pocket GNSS receiver. The i73+ is a compact, 

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"Building on the legacy of the i73 GNSS, the new i73+ receiver 

is designed to maintain its proven compact and lightweight 

concept, but additionally adds the ability to be operated 

as either a GNSS RTK base station or a rover.” said Rachel 

Wang, Product Manager of CHC Navigation's Surveying and 

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built in the latest UHF modem technology allowing the reception 

and transmission of RTK corrections without sacrificing 

receiver size and power consumption.” 

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Best-in-class technology with 624-channels advanced tracking 

The integrated advanced 624-channel GNSS technology takes 

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data quality at all times. The 

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while maintaining centimeterlevel 

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Built-in IMU technology highly 

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With its IMU compensation ready 

in 3 seconds, the i73+ delivers 3 cm 

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able to extend their working boundary near trees, walls, and 

buildings without the use of a total station or offset measurement 

tools. 

Compact design, only 0.73kg including battery 

The i73+ is the lightest and smallest receiver in its class, weighing 

only 0.73 kg including battery. It is almost 40% lighter 

than traditional GNSS receivers and easy to carry, use and 

operate without fatigue. The i73+ is packed with advanced 

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

i73+-imu+-+rtk-gnss 

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NEWS 

LEICA GEOSYSTEMS ANNOUNCES 

MAJOR PERFORMANCE INCREASE IN 

AIRBORNE BATHYMETRIC SURVEY 

Leica Geosystems, part of Hexagon, announced today 

the introduction of Leica Chiroptera-5, the new highperformance 

airborne bathymetric LiDAR sensor for 

coastal and inland water surveys. This latest mapping 

technology increases the depth penetration, point density 

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seamless data from the seabed to land. Thanks to higher 

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increases point density by 40% compared to the previous 

generation system, collecting more data during 

every survey flight. Improved electronics and optics 

increase water depth penetration by 20% and double 

the topographic sensitivity to capture larger areas of 

submerged terrain and objects with greater detail. The 

high-performance sensor is designed to fit a stabilising 

mount, enabling more efficient area coverage which decreases 

operational costs and carbon footprint of mapping 

projects. 

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the sensor’s performance. Introducing near realtime 

data processing enables coverage analysis immediately 

after landing, allowing operators to quality control 

the data quickly before demobilising the system. 

The Leica LiDAR Survey Studio (LSS) processing suite 

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Bundled with the FAAS/EASA certified helicopter pod, 

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“The first generation Chiroptera airborne sensor was 

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“By collecting detailed data of coastal areas and inland 

waters, Chiroptera-5 provides an invaluable source of 

information that supports better decision making, especially 

for environmental monitoring and management, 

in line with Hexagon’s commitment to a more sustainable 

future.” 

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


NEWS 


AEROFOTOTECA 

POTATOES, ARTIFICIAL INTELLIGENCE AND 

L’Aerofototeca 

Nazionale 

racconta… 

OTHER AMENITIES: PLAYING WITH COLORS ON 

PANCHROMATIC AERIAL PHOTOGRAPHS 

by Gianluca Cantoro 

Towards color photography 

During the first half of 

19th century, photography 

stimulated people’s imagination 

and wonder. Despite the 

impressive quality of the first 

trials, photographs lacked the 

realism provided by natural 

colors, at times added in postproduction 

–as we would 

say today– by specialized 

painters (Coleman, 1897, p. 

56) who felt threatened by the 

emergence of photography. 

Following Rintoul, “when the 

photographer has succeeded 

in obtaining a good likeness, 

it passes into the artist’s hands, 

who, with skill and color, give 

to it a life-like and natural 

appearance” (Rintoul, 1872, p. 

XIII–XIV). 

A French physicist, Louis 

Ducos du Hauron, announced 

a method for creating color 

photographs by combining 

colored pigments instead of 

light, as suggested by Maxwell’s 

demonstration of 1861. His 

process required long exposure 

times, and this problem 

built on top of the absence 

of photographic materials 

sensitive to the whole range 

of the color spectrum. Other 

inventors and scientists tried 

to solve the challenge of color 

photographs, but all trials were 

quite expensive and needed 

specific equipment and complex 

procedures. 

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

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

(from top to bottom) Brovey, IHS and PCA. 

The first patent of color 

photograph, combining both 

screen and emulsion on the 

same glass support under 

the name Autochrome, was 

registered by Auguste and 

Louis Lumière in 1895, the 

same year of their invention 

of the Cinématographe. The 

manufacturing of autochrome 

plates was a complex process, 

starting with the sieving 

of potato starch (to isolate 

individual grains between 10– 

15 microns in diameter), whose 

grains were then dyed red, green 

and blue-violet, mixed and 

spread over a glass plate (around 

four million transparent starch 

grains on every square inch of 

it), and coated with a sticky 

varnish. Next, charcoal powder 

was spread over the plate to fill 

any gaps between the colored 

starch grains. 

Autochrome plates were simple 

to use, they required no special 

apparatus and photographers 

were able to use their existing 

cameras. Exposure times, 

however, were long –about 30 

times those of conventional 

plates. Nevertheless, by 1913, 

the Lumière factory in Lyon was 


AEROFOTOTECA 

producing 6,000 autochrome 

plates every day. This testifies 

of the appeal of color 

photographs already in those 

early times. 

Is it possible today to convert 

native black and white images 

(raster digital pictures, not 

prints or negative anymore)? 

And why should one take 

the trouble to convert 

panchromatic into color 

images after all? This paper 

presents some experiments 

to colorize historical 

photographs, in the effort 

to boost our capabilities to 

undisclose information from 

frozen moments captured 

by cameras and to –ideally– 

promote further the use of 

aerial images in various fields. 

Colors in Remote Sensing 

Some procedures in remote 

sensing are known and 

frequently applied to 

satellite images, to improve 

the resolution of a color 

image with the details of its 

panchromatic twin. This 

fusion procedure, known with 

the term pan-sharpening, 

can be applied to satellite 

imagery through numerous 

algorithms, and it produces 

a sensible increase in the 

accuracy of photo-analysis 

and derived feature extraction, 

modeling and classification 

(Yang et al., 2012). The most 

commonly used algorithms 

include IHS (Intensity, Hue 

and Saturation) (Schetselaar, 

1998), PCA (Principal 

Component Analysis) (Chavez 

et al., 1990), the Gram- 

Schmidt Spectral Sharpening 

(Laben Craig and Brower 

Bernard, 2000) and the 

Weighted Brovey transform 

(Chavez et al., 1990). 

The various pan-sharpening 

techniques have two main 

factors in common: 1) they are 

normally applied to satellite 

images, namely multispectral 

and panchromatic bands; 2) 

the two datasets to be fused 

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

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

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

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

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

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

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

10.7) edited for the proposed approach. 

should have been captured 

(almost) simultaneously. For 

these reasons it is apparently 

not possible to fuse an historical 

aerial image with a satellite 

image, which is what we are 

going to try here. Proposed 

methods are not conventional 

and may therefore attract 

comprehensible skepticism, but 

they should be intended as a 

proof-of-concept or experiments 


AEROFOTOTECA 

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

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

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

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

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

(or iColor). 

to test the capabilities of 

modern computer approach to 

obtain a realistic representation 

of past environments in natural 

colors for the benefits of photoreaders. 

For example, since our objective 

is mainly to get a colorized 

historical image, we can adjust 

reciprocal resolution of our 

vertical and satellite images of 

exactly the same area. A similar 

approach has been explored 

recently (Siok and Ewiak, 

2020) with aerial and satellite 

images of about the same 

period and without dramatic 

changes in cultivations or plot 

sizes. Indeed, the processing 

of areas that changed across 

time (i.e. between the date of 

the historical photograph and 

the date of the chosen satellite 

image in terms of time of the 

day, season or cultivations/ 

urbanization processes) may 

produce some unpleasant 

artifact (see in Fig. 1 the details 

of the trees and bushes colors 

which are larger than in the 

historical image), but in this 

case a targeted editing with 

computer graphic software 

may minimize the problem, if 

needed. 

Once such high-resolution 

color image is being generated, 

the applicability of multiple 

operations can be explored, 

such as image classification and 

feature extraction. 

Another approach, completely 

different in terms of processing 

and output, is the use of 

an operation called Color- 

Assimilation-Grid-Illusion 

(Kolås, 2019). As the name 

suggests, this approach is a mere 

visual trick and it is presented 

here mainly for dissemination 

purposes (not for an improved 

photo-interpretation) and as 

a sort of mild invasive way to 

add colors to black and white 

images. It consists in overlaying 

grids (or lines or dots) of oversaturated 

colors over black 

and white images; our brain 

essentially fills in the missing 

colors that it would anticipate, 

or expect, to be there in a full 

color image. 

The image processing is 

intended to be used on one 

single color image, that is 

converted to grayscale and 

overlaid with the original colors 

only through a grid. Instead, in 

our case, starting from the same 

inspiring principle, we take 

the historical aerial photograph 

that we want to colorize, and a 

satellite image (ideally of about 

the same season) of the same 


AEROFOTOTECA 

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 

other applications. 

area; we generate the color grid 

from the satellite image, oversaturate 

it and overlay it over the 

panchromatic picture (Fig. 2). 

Calibration of the above 

experiment may be needed 

according to the printing or 

screen zoom size, nevertheless, 

it is an easy effect to colorize 

historical photographs. 

The Machine Learning 

automatic and 

semi-automatic approach 

Machine learning has been 

explored in several fields for its 

ability to “learn” the intended 

process from A to B with 

the help of prepared dataset. 

Algorithms are available to 

convert black and white images 

into color one, based exactly on 

a learning dataset. In this sense 

it could be applied to historical 

aerial images as well, but again, 

here the intent is to generate 

an image with colors that are 

plausible, not to produce an 

accurate representation of the 

actual snapshot in time. 

Below (Fig. 3) are some 

examples of oblique 

photographs originally taken 

with digital camera in color, 

then converted to black and 

white for the sake of the 

experiment, and colorized back 

with three automatic algorithms 

for image processing: Deoldify 

(Antic, 2021) (or DeepAI), 

Algorithmia (Zhang et al., 

2016) and Automatic Colorizer 

(Larsson et al., 2017). 

The chosen algorithms, selected 

for their simplicity of use, for 

their advertised capabilities and 

for their availability as opensource 

code or online demo, 

are examples of a computer 

problem called image-to-image 

translation, whose success 

depends by the provision of 

sufficient (and compatible) 

training data (Tripathy et al., 

2018). Since the training data 

is mostly made of ground 

photographs of natural subjects, 

portraits or architectures, the 

obtained result in our case is 

mostly unsatisfactory, especially 

when looking at the original 

(our “ground truth”) but also 

if we consider the generated 

images on their own for 

photointerpretation. 

Different result is instead 

achievable with another 

algorithm of the same family, 

which has an interactive model 

that allows user to manually 

input colors on black and white 

image based on chrominance 

gamut: it is the case of 

Interactive Deep Colorization 

(or iColor) (Zhang et al., 2017) 

(Fig.4). 

By default, the first proposed 

colorized image in this 

algorithm is very much similar 

to the ones generated by similar 

algorithms (see Fig. 3), but once 

specific colors are selected, the 

result improves considerably 

reaching a good proximity to 

the original images of our test 

cases (Fig. 5). 

If from one side the Interactive 

Deep Colorization algorithm 

allows one to create images with 

plausible colors, possibly similar 

to the originally depicted 

subject, it also provides the 

option to deliberately choose 

“wrong” colors, and somehow 

create a completely unreal 

scenario (Fig. 6), which may 

make sense if they are employed 

in our case to highlight specific 

features or shadows. 

Conclusions 

In modern photointerpretation, 

crop-marks 

– as well as weed-marks, 

germination-marks and grassmarks 

– by definition are 

made of vegetational stress or 

differential growth in green 

fields. Even with soil-marks, 

shades of brown help us 

recognizing patterns in arable 

lands. 

Seeing black-and-white images 

in color has the potential to 

brings certain details to life that 

would otherwise be missed or 

hardly be visible. This sense 

of immediacy is why color 

images feel more relatable. 

Historical vertical photographs 

traditionally served (and still 

serve) immensely for the study 

of landscape changes and the 

identification of archaeological 

traces (among others) for 

the reconstruction of topic 

palimpsests. They often provide 

details that have no equals in 

color images so far and, training 

on photo-interpretation black 

and white images cannot be 

ignored or replaced in any way. 

In the paper an effort is 

presented to push the 

boundaries of consolidated 


AEROFOTOTECA 

practice in remote sensing 

and artificial intelligence, 

together with the attempt of 

presenting a visual trick for 

dissemination purposes. The 

proposed methods change 

the current paradigm with 

respect to employed algorithms 

and dataset to which the 

algorithms are applied, aiming 

at a new way of looking at 

historical aerial photographs 

and ideally unveiling a new 

dimension in past-landscape 

studies and dissemination. 

The various procedures are all 

oriented towards the artificial 

colorization of historical aerial 

photographs, natively black 

and white. These “bizarre” 

trials are intended as ways to 

promote new approaches to 

legacy data, with the ultimate 

goal to simplify or enhance 

aerial photo-interpretation 

and involve non-experts in 

the narration of the past 

made through photographic 

documents. 

Lastly, artists dealing with 

historical image colorization 

admit the intense and timeconsuming 

effort required to 

achieved a realistic result and 

a philological reconstruction, 

involving historical research, 

comparative materials and 

interviews with witnesses 

or experts. Therefore, black 

and white colorization may 

be a creative process that can 

increase focus and attention on 

what we see (or we don’t see) in 

historical aerial images. 

REFERENCES 

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

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

1.15.21). 

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

to merge multiresolution and multispectral data: LANDSAT TM and SPOT panchromatic. 

AAPG Bulletin (American Association of Petroleum Geologists); (USA) 74:6. 

Coleman, F.M., 1897. Typical Pictures of Indian Natives, Being Reproductions from 

Specially Prepared Hand-coloured Photographs with Descriptive Letterpress. “Times of 

India” office, and Thacker & Company, Limited. 

Kolås, Ø., 2019. Color Assimilation Grid Illusion [WWW Document]. Color Assimilation 

Grid Illusion. URL https://www.patreon.com/posts/color-grid-28734535 (accessed 

2.2.21). 

Laben Craig, Brower Bernard 2000: A. Laben Craig, V. Brower Bernard, Process For Enhancing 

The Spatial Resolution Of Multispectral Imagery Using Pan-sharpening - (US 

Patent: US 6011875 A), https://lens.org/135-660-046-023-136. 

Larsson, G., Maire, M., Shakhnarovich, G., 2017. Learning Representations for Automatic 

Colorization. arXiv:1603.06668 [cs]. 

Musson, C., Palmer, R., Campana, S., 2005. In volo nel passato: aerofotografia e cartografia 

archeologica, Biblioteca del Dipartimento di archeologia e storia delle arti, Sezione 

archeologica, Università di Siena. All’insegna del giglio, Florence? 

Rintoul, A.N., 1872. A guide to painting photographic portraits, draperies, backgrounds, 

&c. in water colours : with concise instructions for tinting paper, glass, & daguerreotype 

pictures and for painting photographs in oil colours and photo-chromography. 

Schetselaar, E.M., 1998. Fusion by the IHS transform: Should we use cylindrical or 

spherical coordinates? International Journal of Remote Sensing 19, 759–765. https://doi. 

org/10.1080/014311698215982 

Siok, K., Ewiak, I., 2020. The simulation approach to the interpretation of archival aerial 

photographs. Open Geosciences 12, 1–10. https://doi.org/10.1515/geo-2020-0001 

Tripathy, S., Kannala, J., Rahtu, E., 2018. Learning image-to-image translation using 

paired and unpaired training samples. arXiv:1805.03189 [cs]. 

Yang, S., Wang, M., Jiao, L., 2012. Fusion of multispectral and panchromatic images 

based on support value transform and adaptive principal component analysis. Information 

Fusion 13, 177–184. https://doi.org/10.1016/j.inffus.2010.09.003 

Zhang, R., Isola, P., Efros, A.A., 2016. Colorful Image Colorization. arXiv:1603.08511 

[cs]. 

Zhang, R., Zhu, J.-Y., Isola, P., Geng, X., Lin, A.S., Yu, T., Efros, A.A., 2017. Rea 

KEYWORDS 

Color photography; artificial intelligence; image-to-image; remote sensing; 

air-photo interpretation; 

ABSTRACT 

Historical photographs, whether taken from the air or from the ground, are usually synonyms 

of grayscale or sepia prints. From the very beginning of photography, during the first 

half of 19th century, people were amazed by this new media that could record all aspects 

of a scene with great detail. Soon though, everybody started wondering why would such 

an impressive innovation fail to record colors? A process of trials and errors then started 

(including the most successful and pioneer one, involving the use of potato starch, by Lumière 

brothers) aiming to add colors to photographs, till the consolidation of new systems 

(camera and film) capable to collect photographs directly in color. In the past, before and 

during this innovative approach, native black and white photographs were painted in the 

effort to give them life. Today, only few methods are available to convert a panchromatic 

image into a color one, and they need a number of steps and further development to work 

properly. The paper tries to present different methods to colorize native black and white 

photographs, based on available automatic or interactive Artificial Intelligence (Machine 

Learning or Deep Learning) algorithms, on revised remote sensing procedures and on 

visual tricks, aiming at exploring the possible improvement in readability and interpretation 

of photographed contests in the usual analytic process of photo-interpretation. At the 

same time, colorized historical photographs hold different appeal in the general public 

and have the potential to attract and involve non-experts in the archaeological/historical 

reconstruction phases. 

AUTHOR 

Gianluca Cantoro 

gianluca.cantoro@cnr.it 

Institute of Heritage Science (ISPC) – Italian National Research Council (CNR) 

Area della Ricerca di Roma 1, Via Salaria km 29,300 - 00010 Montelibretti (RM) 

Italian National AirPhoto Archive (Aerofototeca Nazionale, AFN) – Istituto Centrale 

per il Catalogo e la Documentazione (ICCD) 

Via di San Michele 18, 00153 Rome (RM) 


AMPERE 

a a GNSS-based integrated platform 

for for energy decision makers 

AMPERE Working Group in Santo Domingo 

AMPERE PARTNERS 

Barrio Los Tres Brazos 

Santo Domingo Este 

Asset Mapping Platform Platform for 

Emerging CountRies Electrification 

Emerging CountRies Electrification 

Despite global electrification rates are significantly progressing, the 

Despite access global to electricity electrification in emerging rates countries are significantly is still far from progressing, being achieved. the access 

Indeed, the challenge facing such communities goes beyond the lack of 

to electricity infrastructure emerging assets; what countries is needed is is still a holistic far from assessment being achieved. of the 

energy demand and its expected growth over time, based on an accurate 

Indeed, assessment the challenge of deployed facing resources such communities and their maintenance goes beyond status. the lack of 

infrastructure assets; what is needed is a holistic assessment of the energy 

demand and its expected growth over time, based on an accurate 

assessment of deployed resources and their maintenance status. 

AMPERE Consortium 

www.h2020-ampere.eu 

AMPERE project has received funding from the European GNSS Agency (grant 

agreement No 870227) under the European Union’s Horizon 2020 research and 

innovation programme.

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