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NSE and AI: The New Revolution Changing

the World and Human Life Within It

We are living a new age of a phantastic technological development. The capability to increase

our knowledge or to imagine and develop new solutions enabled by satellites technologies

namely Earth Observation and satellite navigation both in Downstream and Upstream

domains appear infinite.

The evolution we are living is so rapid and significant that we needed to precisely define this

as the age of the “New Space Economy (NSE)”

The New Space Economy is the age of commercialization and democratization of Space

Exploration. Because also of the incredible support of Artificial Intelligence the access to

Space is as cheap as never before. I was reading in a paper published on a previous issue of

GEOMEDIA that in the recent years the cost to send one kg payload into a LEO orbit

decreased of 95% (from 65.000,00 $ to 1.500 $). It means that the capability to send

satellites into orbit is no more limited to a small club of rich and powerful Countries. Space

is now accessible to many other Countries and moreover also to commercial companies.

Many new stakeholders, in particular private stakeholders, can promote a new Economy

enabled by the full exploitation of space assets and technologies by developing, for example

in the downstream sector, new products and services which are becoming more important

and pervasive in our normal life. Just one example over all: Starlink telecommunication

network implemented and manged by SpaceX (Space Exploration Technologies Corp.)

the company owned by Elon Musk. This company, fully exploiting the cost reduction

was able to send into orbit a Mega Constellation of thousands of LEO satellites which

support data transmission and Internet connection at low cost also in areas without ground

infrastructures.

The Policy strategy of many Governments is pushing and stimulating the involvement

of privates in accessing Space both in the Upstream as well in the Downstream. The new

missions aiming at the Solar System colonization, first our Moon and then Mars, are

opening a new age full of hope and enthusiasm.

Nevertheless I am some time confused and frightened by the news reports that testify to the

all-too-often destructive use of the technologies we are developing. When more the 40 years

ago I started to work in this domain, in a period in which Satellite Navigation and EO were

still under development, the term GEOMATICS or the acronym UAV had not yet been

coined and Artificial Intelligence or the colonization of Mars were topics for science fiction

movies, international cooperation and knowledge sharing were the best practices instead of

competition and conflicts and I believe we have to go back to that spirit.

In the same paper on the same issue of GEOMEDIA that I mentioned before the conclusion

was: “NSE and AI will change our life. our society and the geopolitical scenario on the

Earth and in Space, but as every instruments it is under our responsibility to use them

wisely and keep them under control”. Dear Friends and Colleagues NSE and AI are neutral

but their use is not and it depends also from us their safe use to create a world more just

and supportive.

Enjoy your reading,

Marco Fermi

In this

issue...

FOCUS

GUEST PAPER

REPORT

FOCUS

Large-scale seafloor

mapping of the Italian

coasts using multi-sensor

surveying to characterise

Posidonia oceanica and

seafloor morphology in

shallow waters

by S. F. Rende et al.

6

COLUMNS

32 IMMAGINE ESA

54 THE ITALIAN

AIRPHOTO ARCHIVE

a random aerial photograph:

dating a historical image by

cross-referencing sourcesd

58 MARKET

62 DIARY

14

Open SAR Data

Analysis

Techniques

vs. Intelligence

by Planetek Italia

20

Continental-

Scale Assessment

of Urban Sprawl

in Africa

(2016-2024)

by Johnny Muhindo

Bahavira et al.

An image of the

ongoing survey

with Fugro vessels

at Basiluzzo Rock,

part of the island

of Panarea, in the

Aeolian Islands

archipelago.

28 From Heat

Islands to

Green Spaces

by A. Perello

4 GEOmedia n°3-2025

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.

34

Why you need

to use ground

control points

(GCP) for

drone mapping

by E. Van Rees

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44 Harnessing

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From Analyst to

AI Orchestrator:

Evolving Roles

in the Age

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55

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Chief Editor

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Editorial Board

Vyron Antoniou, Fabrizio Bernardini, Caterina Balletti,

Roberto Capua, Mattia Crespi, Fabio Crosilla, Donatella

Dominici, Michele Fasolo, Marco Fermi, Marco Lisi,

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Issue closed on: 12/09/2025

published by

Science & Technology Communication

Science & Technology Communication

FOCUS

Large-scale seafloor mapping of

the Italian coasts using multi-sensor

surveying to characterise Posidonia

oceanica and seafloor morphology

in shallow waters

by Sante Francesco Rende et al.

Seagrass meadows are among the

most valuable coastal ecosystems

on the planet. They provide a wide

range of ecosystem services, with

carbon storage standing out as one

of the most important. Beyond their

remarkable capacity to capture and

retain carbon, seagrass

meadows enhance marine biodiversity,

stabilise sediments, and reduce wave

energy, offering natural protection for

coastlines against storms.

In the Mediterranean Sea, meadows of Posidonia oceanica

— an endemic species — have been recognised

as a priority habitat under the European Union’s

Habitats Directive (Habitat Type 1120: Posidonion oceanicae).

It is estimated that Posidonia oceanica alone has

sequestered between 11% and 42% of the region’s carbon

dioxide emissions since the onset of the Industrial

Revolution (Pergent et al., 2014).

Despite their ecological importance, Posidonia oceanica

and other seagrass meadows are increasingly under threat

from human activities such as coastal development, bottom

trawling, anchoring, pollution, and declining water

quality. Climate change further accelerates their decline

through rising sea-surface temperatures and sea-level rise

(Boudouresque et al., 2009). Alarmingly, research sug-


FOCUS

gests that Posidonia oceanica

meadows in the Mediterranean

have shrunk by about 34%

over the past 50 years (Telesca

et al., 2015).

To counteract this degradation,

the Italian government has

established the Piano Nazionale

di Ripresa e Resilienza (PNRR)

Marine Ecosystem Restoration

(MER) project, implemented

by the Italian Institute for

Environmental Protection

and Research (ISPRA). The

activities are carried out

within the framework of the

NextGenerationEU investment

projects - Mission 2: Green

Revolution and Ecological

Transition, Component

4: Protection of Land and

Water Resources, Measure

3: Safeguard air quality and

biodiversity through the protection

of green areas, soil, and

marine areas, Investment 3.5

has been planned: Restoration

and Protection of Seabeds and

Marine Habitats.

The MER project aims to

restore the marine habitats

and fortify the national system

for observing marine

and coastal ecosystems. The

first crucial component of

the MER project involves the

mapping of Posidonia oceanica

and Cymodocea nodosa seagrass

meadows across Italian waters.

Secondly, it aims to provide

high-resolution bathymetric

coverage and morphological

mapping with continuity from

the subaerial to the submerged

portions down to 50 metre

depth, in order to provide high-resolution

digital elevation

models (DEMs) useful for:

maritime navigation; coastal

risk indicators to which people

and infrastructure are exposed;

monitoring coastal infrastructure

and assets in relation to

climate change; geomorphological

analysis of the seabed in

relation to coastal geo-hazards;

support for the management

of coastal areas by the State

Property Agency; support for

the management of archaeological

assets, scenarios of relative

sea level rise and more.

Only by fully understanding

the status of seagrass meadows

along the Italian coast can appropriate

steps be taken to protect

and restore this vital marine

ecosystem. An integrated

approach using multiple data

acquisition methods ensures

the accurate and high-resolution

mapping of the abundance

and distribution of seagrass

meadows with different spatial

configurations (Rende et al.,

2020).

The seagrass mapping initiative

under the MER project

is performed by Fugro

and Compagnia Generale

Ripreseaeree (CGR), in partnership

with EOMAP- a Fugro

company, and PlanBlue. The

project started in March 2024

and will last until June 2026.

The project includes mapping

the entire Italian coastline,

covering 12,600 km² using

topographic and bathymetric

LiDAR aerial RBB-NIR imagery,

aerial gravimetry and

satellite sensors, 3,798.2 km²

using high-resolution multi-

Figure 1: Different data

collected by sensors for

high-resolution seafloor

mapping. From bottom

right to top left: RGB

satellite imagery, combined

ALB and multibeam

bathymetry and backscatter.

Seagrass meadows

are visible in the RGB

imagery as darker-toned

areas, indicating denser

vegetation. In the intensity

data as seagrass appears

as regions of generally

lower values with greater

inhomogeneity due to

stronger variation in

reflectance and backscatter

intensity. Posidonia

oceanica matte on bathymetric

data (DEM) are

associated with morphologically

detected features

with a specific roughness.

The dotted black line

separates the multibeam

backscatter from LiDAR

intensity data.

GEOmedia n°3-2025 7

FOCUS

Sensor / Platform Type Data Derivatives Resolution

Satellite EO

WorldView-2,

WorldView-3

RGB, Satellite-derived

bathymetry (SDB), Subsurface

reflectance (SSR)

2 m

Topographic LiDAR

(ALT)

Bathymetric LiDAR

(ALB)

MBES

AUV SeaCat

Gravimetry

Leica TerrainMapper

CityMapper 2/3

Fugro RAMMS 2.0

Kongsberg EM2040, EM

710-712

Camera PlanBlue, MBES,

SSS

Strapdown Airborne

Gravimeter, Land Relative

Gravimeter

Table 1: Overview of employed sensors and their data derivatives

Orthophotos, DSM, DTM

Bathy DTM, Topo DTM,

Bathy Intensity, RGB-NIR

DEM, Backscatter

RGB imagery, Orthophoto,

Point Clouds, DEM

Free-Air Gravity Anomalies

along track, Complete

Spherical Bouguer

Anomalies along track,

Gridded Free-Air and

Complete Spherical Bouguer

Anomalies

10 cm

1 m

0.2 m – 0.5 m

Sub-centimetre

Variable, ranging

from 1.5 to 3.0 km

beam technology from vessels

and 4,000 km using autonomous

underwater vehicles

(AUVs).

The unique feature of this

project is the extensive spatial

coverage combined with the

high number of sensor platforms,

instrument types and

data derivatives employed.

Data is collected from space,

air, water surface and below

water – each of these data sets

with its own characteristics and

advantages. The strength lies in

the integration of these different

data sets and types, which

enables the creation of a comprehensive

data basis for a thorough

and complete analysis of

the seafloor morphologies.

Table 1 provides an overview

of the equipment and data

used in the MER project.

Typically, a specific data type

is acquired through multiple

sensors. For instance, bathymetric

and intensity data are

collected via satellites (satellitederived

bathymetry, or SDB),

by airborne LiDAR bathymetry

(ALB), as well as by shipborne

high-resolution multibeam

echo sounder (MBES).

Whereas SDB and ALB are

limited in terms of penetration

depth, MBES completes these

data sets by covering deeper

water areas. In contrast, space-

and airborne systems are

particularly effective in capturing

data in very shallow and

onshore areas, enabling a seamless

and comprehensive data

compilation across the entire

survey domain ranging from

the land to water depth of approximately

50 metres.

Figure 1 shows examples of the

different data (LiDAR bathymetry

and intensity, multibeam

bathymetry and backscatter

and RGB-NIR satellite imagery),

covering a large seagrass

meadow.

Figure 2: Virgeo® user interface of western Sicily (Egadi Islands – Italy)

Challenges in large-scale

multi-sensor mapping MER

project

The MER project, which aims

to conduct large-scale mapping

of the Italian coastline using


FOCUS

a wide variety of sensors and

datasets, presents significant

technological and methodological

challenges. These include

the integration of geophysical,

optical, and multispectral data;

the generation of high-resolution

digital land–sea models;

and their homogenisation

within a national reference system

and official datum. Some

of the key considerations are

outlined below.

Planning data acquisition

When multiple sensors complement

each other in terms of

spatial coverage and depth ranging,

such as ALB and MBES

for bathymetry and intensity/

backscatter data, careful acquisition

planning is essential to

ensure sufficient data overlap.

ALB covers shallow water areas.

It can penetrate up to three

Secchi depth, which is about

20 metre depth (depending on

the environmental characteristics).

MBES, on the other

hand, is used in deeper waters

beyond ALB’s range, extending

to about 50 metre depth. This

depth marks the natural limit

for seagrass growth due to its

reliance on photosynthesis.

An effective acquisition planning

must account for various

factors:

4Seafloor morphology: MBES

is typically acquired parallel

to the slope (i.e., parallel to

the isobaths and shoreline),

while ALB acquisition patterns

may differ as subaerial

terrain features such as

mountains and coastal infrastructure

must be considered

when planning flight lines

and the turns in between.

4Environmental conditions:

weather plays a significant

role, particular for ALB.

Water turbidity, which

negatively affects ALB measurement

range, varies not

only by location but is also

influenced by recent weather

events. Rainfall, for instance,

can increase the water

turbidity (i.e., flash flood),

sea condition, etc.

4Operational constraints:

local restrictions on flight

times or airspace usage may

also impact the scheduling

and execution of airborne

data acquisition. As for vessel

- and AUV-based operations,

they can be affected

by local survey permit regulations

as well as tourism

during the summer period.

Furthermore, emergencies

such as summer fires and

volcanic activity (Stromboli

and Etna) may also have an

impact.

Positioning, datum a

nd reference system

All multibeam bathymetric

data, bathymetric and topographic

LiDAR datasets,

ortho-mosaics, and derived

cartographic products were

integrated within a standard

Reference System and Datum

(ETRF2000 RDN2008).

Figure 3: Mapping process for seafloor classification. A) Aerial RGB photo; B) highresolution

multibeam and LiDAR bathymetry; C) LiDAR intensity (preliminary not

processed) and MBES backscatter; D) preliminary seafloor classification results deriving

from their integrated analysis.


FOCUS

National GNSS CORS geodetic

networks (Leica SmartNet,

Trimble Spectra, and Topcon)

were employed for all kinematic

positioning systems.

The maximum baseline distances

used for post-processing

kinematic (PPK) positioning

between aircraft/ship platforms

(rovers) and the CORS reference

stations did not exceed

20 km. This ensured a planoaltimetric

accuracy of only a

few centimetres, and in any

case less than one decimetre.

Data acquisition was carried

out using latest-generation,

multi-frequency and multiconstellation

GNSS receivers

in combination with IMUs.

Post-processing was performed

using precise ephemerides,

allowing the generation of

DEMs in both orthometric

height (H – ITALGEO2005)

and ellipsoidal height. Finally,

the data were also referenced to

the local mean sea level.

Monitoring project

progress and data status

Managing large and heterogeneous

data sets requires a robust

system to ensure effective

oversight of project progress

and data processing status. For

the MER project, Fugro adopted

Virgeo® – a cloud-hosted

platform specifically designed

to streamline data management.

Field, vessel, and office teams

can upload data sets into

Virgeo®, making them accessible

in real-time to all project

stakeholders. This centralised

access not only enhances decision-making

but also significantly

improves operational

efficiency.

Figure 4: MBES backscatter mosaic (0.2 metres) with seagrass areas. The inset map shows a

more detailed view of the high-resolution AUV data.

As illustrated in Figure 2, the

Virgeo® interface allows users

to visualise the real-time positions

of vessels and aircraft,

alongside all datasets collected.

In addition to displaying spatial

data layers, the platform

provides live asset tracking and

colour-coded indicators that

reflect the temporal status of

the MER project.

Merging MBES and ALB data

The integration of MBES and

ALBdatasets is essential for

generating unified information

layers, which can subsequently

be used for advanced analyses

such as automated seafloor

classification. When applying

machine learning and AI techniques,

it is particularly important

to ensure that the datasets

are free of artefacts and that

differences in data properties,

such as resolution, are properly

addressed.

One of the main challenges in

this process is the integration

of MBES and ALB intensity

data. MBES backscatter values

are typically expressed in decibels,

whereas ALB intensity

values are derived from laser

reflectance and depend on

both post-processing steps and

the specific data format of the

derivative product.

To obtain consistent and artefact-free

results, inter-sensor

normalisation is required. For

MBES backscatter data, normalisation

is carried out using

the Kongsberg system (the

equipment employed in this

project) for each sector and acquisition

mode, ensuring internal

consistency within datasets

collected by the same system.

The subsequent step involves

the normalisation of datasets

acquired from different vessels

and flight missions, thereby

homogenising the data and ensuring

high-quality, comparable

outputs across all sources.


FOCUS

Automated seagrass

classification

Once all data sets have been

processed, automated classification

techniques using machine

learning are applied to identify

and assess seagrass coverage and

other morphologies as rock and

mobile sediment. The success

of this classification is strongly

influenced by the diversity and

quality of input data. Especially

data derivatives such as slope,

aspect, backscatter, and intensity

can improve the classification

results.

The classification process in

this project is carried out using

Trimble eCognition software

(Rende et al., 2020; Rende

et al., 2022; Tomasello et al.,

2022). The process starts with

a segmentation, where the data

is divided into regions based on

shared properties across different

data sets. These segments

are then grouped and assigned

to thematic classes such as rock,

sediment, or seagrass, using

high-resolution orthophotos

as ground truth, to validate information

retrieved from other

sensors and training machine

learning classification. Figure

3 shows a preliminary result of

the automated seafloor classification

(Figure D, the bottom

right), alongside some of the

data layers used in the process.

The orthophotos used for

ground-truthing are captured

by an AUV, operating two to

three metres above the seafloor

at speeds of up to three knots.

These images provide detailed

visual information that not

only confirms seagrass presence

but also enables assessments of

its health.

Figure 4 and figure 5 illustrate

the high amount of detail

provided from the AUV imagery

(orthophoto, resolution

0.2 cm, Planblue). This AUV

survey was carried out during

seagrass winter dormancy in

Secche di Vada (Tuscany).

Figure 4 shows AUV imagery

overlaid on MBES backscatter

data (resolution of 0.2 metres)

in an area of seagrass coverage

at a water depth of approximately

35 metres.

Conclusion and remarks

The MER project represents

the first national-scale, highresolution

mapping initiative

dedicated to the study of

Posidonia oceanica meadows

and seabed morphology. It

addresses significant technological

and methodological

challenges, with the capacity to

generate DEMs and accurate

maps through the integration

of ground, marine (surface

and deep), aerial, and satellite

sensors, all referenced within a

unified official system.

This multi-sensor approach ensures

full coverage of Posidonia

oceanica habitats, from the

coastline down to depths of

about 50 metres. By combining

different acquisition methods,

it provides reliable mapping of

seagrass meadows and seabed

classification, supporting both

restoration planning and longterm

monitoring. Moreover, it

offers the most cost-effective

solution for establishing ecological

baselines and conducting

large-scale, repeatable assessments.

The integration of

multiple high-resolution data

sources reduces the uncertainties

of single-sensor analyses

and enables investigation down

to the lower limits of meadow

distribution.

Figure 5: Left: High-resolution orthophoto collected during night test (low environmental

visibility) in east Pianosa Island, Tuscan Archipelago, Italy. Right: windows showing altitude, depth and motion parameters

for quality checks. Credit: PlanBlue


FOCUS

Within this framework, the

project directly contributes to

the implementation of major

European policies and regulations:

4The Habitats Directive

(92/43/EEC), which recognises

Posidonia oceanica

meadows as a priority habitat

requiring protection.

4The Marine Strategy

Framework Directive

(2008/56/EC), which requires

Member States to

achieve good environmental

status of marine ecosystems.

4The recent Nature

Restoration Law, which

sets legally binding targets

for the restoration of degraded

habitats, including

seagrass meadows.

The knowledge generated

will support policymakers in

developing targeted strategies

to mitigate pressures, preserve

existing meadows, and guide

effective restoration actions, in

line with European regulatory

commitments. In this way, the

MER project makes a significant

contribution to the longterm

protection of one of the

Mediterranean’s most valuable

marine ecosystems.

Want to hear more about this

project?

Then check

out this

podcast:

Multibeam

Echosounder

(MBES) survey

conducted

near Tavolara

Island, Sardinia


FOCUS

REFERENCES

1. Pergent, G. et al. (2014). Climate change and Mediterranean seagrass meadows: a synopsis for environmental managers. Mediterranean

Marine Science, 15(2), 462–473. https://doi.org/10.12681/mms.621

2. Boudouresque, C et al. (2009). Regression of Mediterranean seagrasses caused by natural processes and anthropogenic disturbances

and stress: A critical review. Botanica Marina 52. DOI:10.1515/BOT.2009.057

3. Telesca, L., Belluscio, A., Criscoli, A. et al. (2015). Seagrass meadows (Posidonia oceanica) distribution and trajectories of change. Sci

Rep 5, 12505 https://doi.org/10.1038/srep12505

4. Rende, S.F. et al. (2020). Ultra-High-Resolution Mapping of Posidonia oceanica (L.) Delile Meadows through Acoustic, Optical

Data and Object-based Image Classification. J. Mar. Sci. Eng. 2020, 8, 647; doi:10.3390/jmse8090647

5. Rende S.F. et al. (2022). Assessing Seagrass Restoration Actions through a Micro-Bathymetry Survey Approach (Italy, Mediterranean

Sea). Water 2022, 14, 1285. https://doi.org/10.3390/w14081285.

6. Tomasello et al. (2022). 3D-Reconstruction of a Giant Posidonia oceanica Beach Wrack (Banquette): Sizing Biomass, Carbon and

Nutrient Stocks by Combining Field Data With High-Resolution UAV Photogrammetry. Front. Mar. Sci., https://doi.org/10.3389/

fmars.2022.903138

KEYWORDS

Marine Ecosystem Restoration, habitat and seafloor mapping, multi-sensor surveying, seagrass monitoring,

machine learning classification

ABSTRACT

The Italian Institute for Environmental Protection and Research (ISPRA) is leading a nationwide initiative to map and restore seagrass

meadows under the Marine Ecosystem Restoration (MER) project. This effort addresses the alarming decline of Posidonia oceanica and

Cymodocea nodosa habitats, which are critical for carbon sequestration, biodiversity, and coastal resilience. The MER project’s mapping

component, executed by Fugro and Compagnia Generale Ripreseaeree (CGR), in partnership with EOMAP – a Fugro company, and

PlanBlue, employed a multi-sensor approach, combining satellite, airborne, vessel-based (high-resolution multibeam), and autonomous

underwater vehicle (AUV) technologies. The integration of bathymetric LiDAR, multibeam, optical and multispectral data allowed

continuous bathymetric coverage from the coastline to 50 metre depth. The Virgeo® platform, specifically developed by Fugro, facilitated

real-time monitoring of acquisitions and data collected by ships and aircraft engaged in the surveys. This integrated approach

provided a robust baseline for restoration planning and long-term monitoring, offering a scalable, cost-effective solution for national

marine habitat assessments. The Piano Nazionale di Ripresa e Resilienza (PNRR) MER project was funded by MASE, coordinated by

ISPRA and scientifically supported by Italian research institutes and universities (CNR-IGAG, IIM, Sapienza, INGV, PoliMi, UniPd,

UniGe).

AUTHOR

Sante Francesco Rende 1 , Alessandro Bosman 2 , 1 , Nunziante Langellotto 3 , Viviana Belvisi 1 , Valerio Vitale 1 , Luca Olivetta

1 , Saverio Romeo 1 , Gianluigi Di Paola 1 , Agostino Tommasello 4 , Monica Montefalcone 5 , Alberto Guarnieri 6 , Giorgio

De Donno 7 , Valerio Baiocchi 7 , Daniela Carrion 7 , Filippo Muccini 8 , Riccardo Barzaghi 9 , Tanja Dufek 10 , Paula Garcia

Rodriguez 10 , Marco Filippone 10 , Benoit Cajelot 10 , Dhira Adhiwijna 10 , Federico Bartali 10 , Hugh Parker 10 , Michelle

Wagner 10 , Nick Rackebrandt 10 , Leonardo Tamborrino 11 , Knut Hartmann 12 , Constantin Sandu 12 , Andreas Müller 12 ,

Simone Ceresini 13 , Michele della Malva 13 , Giordano Giorgi 1

1 Italian Institute for Environmental Protection and Research (ISPRA, Italy)

2 National Research Council, Institute of Environmental Geology and Geoengineering (Rome, Italy)

3 Italian Navy, Hydrographic Institute (Genova, Italy)

4 Università degli Studi di Palermo (Italy)

5 DISTAV, Department of Earth, Environment and Life Sciences (DiSTAV), University of Genoa, Italy; NBFC (National

Biodiversity Future Centre), Palermo, Italy

6 Università degli Studi di Padova, Interdepartmental Research Center of Geomatics, CIRGEO (Italy)

7 Sapienza Università di Roma, DICEA (Rome, Italy)

8 Istituto Nazionale di Geofisica e Vulcanologia (La Spezia, Italy)

9 Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale (Milano, Italy)

10 Fugro

11 PlanBlue

12 EOMAP – a Fugro company

13 Compagnia Generale Ripreseaeree (CGR)

Corresponding Author:

dr. Sante Francesco Rende - francesco.rende@isprambiente.it

Section for Development of Technology and Support for Monitoring and Applied Research in the Deep Sea

Environment"ISPRA – Istituto Superiore per la Protezione e la Ricerca Ambientale

Via Vitaliano Brancati, 60 I-00144 ROMA - Italy


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Open SAR Data Analysis

Techniques vs. Intelligence

Can the wide availability of free satellite data, particularly

in the SAR domain, truly support the research and analysis

of intelligence-related information?

by Planetek Italia

Fig. 1 – Geographical Overview of the Yulin Naval Base

The Earth observation missions from space carried out

by the Copernicus constellation of the European Space

Agency (ESA) have made a wide variety of data more

readily available across the entire electromagnetic

spectrum, with a notably high revisit frequency. In

particular, the Sentinel-1 (SAR) constellation has been

providing its products consistently and effectively since

2014, to the point where one may wonder whether it is

possible to capitalize on such a highly reliable resource.

The Earth observation

missions from space

carried out by the

Copernicus constellation of the

European Space Agency (ESA)

have made a wide variety of data

more readily available across

the entire electromagnetic spectrum,

with a notably high revisit

frequency. In particular, the

Sentinel-1 (SAR) constellation

has been providing its products

consistently and effectively

since 2014, to the point where

one may wonder whether it is


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possible to capitalize on such a

highly reliable resource.

Imagery Intelligence (IMINT)

has traditionally relied on

high-resolution optical data,

but the advancement of satellite

radar sensors like Sentinel-1

has significantly expanded surveillance

capabilities, especially

under adverse weather conditions

or at night. This article illustrates

the use of a Sentinel-1

SAR time series to monitor naval

activities at the Yulin naval

base in the South China Sea,

from January 2024 to June

2025, using the Copernicus

Data Space Ecosystem.

Yulin Naval Base –

Geographical Context and

Strategic Significance

• Overview

The Yulin naval base, located

on the southern coast of

Hainan Island (China), is one

of the most strategic naval

hubs of the People's Liberation

Army Navy (PLAN). The base

faces the South China Sea,

near the Qiongzhou Strait,

southeast of Sanya, and is composed

of two distinct sections:

• Yulin Ovest (Longpo)

– intended for the surface

fleet ( Shandong aircraft

carrier);

• Yulin Est – Headquartes

for destroyers and frigate as

well as a semi-underground

base reserved for nuclear

submarine in particular the

Type 094 (SSBN)and Type

093A (SSN).

• Strategic Significance

Its geographical location and

military assets make it a site of

significant strategic interest,

enabling it to fulfill roles in

protection, surveillance, and

power projection across all three

domains: air, surface, and

undersea warfare.

• Submarine nuclear deterrence:

the eastern section

of the base hosts Type 094

SSBNs, equipped with JL-2

and future JL-3 ballistic missiles,

representing the maritime

cornerstone of China’s nuclear

triad.

• Naval power projection:

Yulin West hosts the aircraft

carrier Shandong, Type

055-class destroyers, and amphibious

units, enabling longrange

naval and air operations.

• Control of the South

China Sea: the base supports

naval and aerial patrols and

strengthens China’s claims over

the artificial islands.

• Multilayered defense: protected

by HQ-9 SAM systems,

coastal radar surveillance, and

layered air defense systems.

Multi-temporal Monitoring of

Chinese Naval Activities

The multi-temporal monitoring

of naval activities at

the Chinese naval base on

Yulin Island was conducted

using 41 Synthetic Aperture

Radar (SAR) images from the

Sentinel-1 satellite, acquired

during the specified period.

The images were processed

through a dedicated workflow

designed to optimize data quality

and result accuracy.

The data were downloaded

Fig. 2 - Copernicus Data Space Ecosystem

from the ESA website, and the

tools used for the analysis included

ERDAS Imagine, Radar

ToolBox, Spatial Modeler, and

Google Earth.

The main objective of the

monitoring was to analyze

movements and naval activities

at the Yulin base, leveraging

Sentinel-1’s ability to acquire

images regardless of weather

conditions or sunlight. This

enabled the creation of a detailed

temporal dataset useful for

analyzing behavioral patterns

and operational anomalies.

Data Processing Workflow

P To ensure accurate analysis,

a tailored pipeline was implemented

for the multi-temporal

analysis of radar backscatter

in decibel scale. The workflow

focused on relative radiometric

quality, avoiding unnecessary

steps for the type of analysis

conducted.

The adopted workflow was as

follows:

• Calibration: applied

to obtain normalized radar

backscatter values (σ⁰) in VV

polarization.

• Orbit Correction.

• Speckle Filtering: using

the Lee Sigma 5x5 filter.

• Linear to dB Conversion:


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converting σ⁰ values to decibel

(dB) scale.

• Terrain Correction (Range-

Doppler): geometric correction

based on the SRTM 1Sec

DEM.

• Subset (AOI Yulin): spatial

cropping of the area of interest.

In particular:

• Calibration

The calibration function for

Sentinel-1 is used to convert

the raw values (DN, digital

number) contained in SAR

images into physically interpretable

radar backscatter values,

that is, the normalized power

of the signal reflected from

the Earth's surface toward the

sensor.

• Orbit update

Sentinel-1 is equipped with

precise orbits, but ephemerides

can be retroactively updated

through files provided by the

European Space Agency (ESA).

These updates improve the geometric

accuracy of the images,

reduce geolocation errors, and

enable accurate pixel-wise comparison

between images acquired

on different dates.

• Subset (AOI)

To focus on the Yulin naval

base and reduce the computational

load, a geographic subset

was applied to the images. This

process limited the analyzed

area to the portion containing

the base and the surrounding

waters of strategic interest,

eliminating irrelevant information.

• Speckle filtering

The SAR radar signal is affected

by speckle, a coherent

noise caused by multiple wave

interference. To improve the

visual and quantitative quality

of the data, an adaptive filter,

such as the Lee 5x5 filter, was

applied, reducing noise and

preserving structural details.

• Co-registration and orthocorrection

Pixel-wise co-registration was

performed to align all images

(pixel by pixel in relation to

time) to a common geometry,

which is essential for temporal

comparison. Subsequently,

ortho-correction was applied to

correct geometric distortions

introduced by the acquisition

angle and topography, ensuring

that the images are accurately

georeferenced.

• VV Signal Transformation

Sentinel-1 dual-polarization

SAR images include VV and

VH polarizations. For the

analysis of naval activities, the

VV channel was used, as it is

more sensitive to targets with

complex geometries, such as

ships. The amplitude values

were converted to logarithmic

intensity expressed in dB, as

this allows for a clearer comparison

between pixels and greater

sensitivity in detecting temporal

variations, emphasizing

the relative differences between

values, which is useful for identifying

anomalies or patterns in

the dataset.

• Stack generation

The generation of a SAR image

stack serves to combine multiple

radar images acquired at

different times into a single

coherent dataset, facilitating

multi-temporal analysis. The

"stack" is therefore a common

basis on which to perform

comparisons over time, pro-

Fig. 3 – Multitemporal stack (the yellow area is a temporal decorrelation between images, due to the variation in the field covered

during the 18 months)


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file extractions, or advanced

analyses such as interferometry,

change detection, and classification.

Time series analysis

In order to observe the temporal

variations that occurred in

the area of interest, the analysis

was carried out in two phases.

The first phase involved

gathering information from

open sources (OSINT and

SOCINT) and searching for

evidence in the time series.

The second phase, on the other

hand, was guided by IMINT

evidence processed from 41

SAR images, which made it

possible to highlight the changes

that had occurred over time

thanks to the composition of

statistics and averages of the

images over time.

• Phase 1, OSINT indications

Different multispectral images

were created by associating

RGB colors (additive synthesis)

with data from different SAR

products. The images obtained

allowed us to observe specific

periods during which the information

gathered in the OSINT

(Open Source Intelligence)

sector clearly showed the occurrence

of events collected in

the OSINT report. This method

allowed us to validate the

information gathered, showing

the numerous naval activities

observed during the 18 months

of analysis, with intense movement

of military ships throughout

the period. In particular,

the presence of the Shandong

aircraft carrier at berth was

monitored closely over time,

accurately determining its presence

or absence in port. The

same was observed for other

large military ships, such as the

Type 055 destroyers moored

in the eastern part of the base

and the Type 052 destroyers.

Fig. 4 Timeline of the movements of the aircraft carrier Shandong

Fig. 5 Time chart of destroyer movements

Fig. 6 Time chart of submarine movements


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Both classes were characterized

by great dynamism and periods

similar to the absence of

the Shandong aircraft carrier.

The docks that typically house

some Chinese nuclear submarines

(Type 094 and Type 093A)

also showed a certain amount

of activity, often correlated in

time with the naval activities

of larger military ships (aircraft

carriers and destroyers).

• Phase 2, evidence emerging

from multitemporal stacks

With the help of the numerous

images collected, statistics

were compiled which, thanks

to band composition, made it

possible to highlight the construction

of new moorings inside.

In particular, the following

statistics were generated:

• The CoV (Coefficient of

Variation) applied to a stack

of SAR images is a normalized

statistic that measures how

much a pixel varies over time,

relative to its average. It is a

very useful tool in multi-temporal

SAR analysis to highlight

dynamic areas, such as ships,

construction sites, moving

objects, or operational activities.

CoV= σ/μ, where σ is

the standard deviation of pixel

values in the stack (in the time

domain), μ is the average of

the same values over time. The

higher the CoV in the pixel,

the greater the variation over

time.

• The Minimum calculates,

for each pixel in the stack, the

minimum value of backscatter

(σ⁰) that that pixel has

assumed over time. The value

represents the weakest radar

return observed for each pixel

among all images in the series.

• The Maximum calculates

the maximum value observed

over time for each pixel in the

time stack. The Maximum

value shows you the point of

maximum radar reflectivity

in each pixel throughout the

time series. It is very useful for:

Identifying highly reflective

objects that have appeared at

least once (e.g., ships, containers,

radar, metals) and highlighting

areas of episodic or

intense activity.

Analysis of the results

The analysis of the images highlighted

the following main

aspects:

• Operational Pattern: The

images showed an increase in

naval activity during certain

specific periods, probably related

to exercises or planned

operations.

• Presence of ships of interest:

It was only possible to identify

specific types of military

ships, thanks to their size and

the specific moorings of the

naval units (Aircraft Carrier

Shandong, Type 055 destroyer

class, and observation of the

presence/absence of submarines)

in known docks.

• Structure Patterns: Several

expansion projects for naval

Fig. 7 - Band composition CoV, Min, Max. Elements of interest


FOCUS

vessel mooring structures were

observed during the observation

period.

Conclusions and prospects

Sentinel-1 data has proven to

be very useful and effective for

monitoring naval targets, given

the strong contrast between

the sea surface and complex

metallic targets. The high

observation frequency (revisit

time) allows for accurate monitoring

over long periods for

strategic assessment. However,

operational or tactical use is

not feasible due to the spatial

resolution characteristics of

the images and the time interval

between acquisitions. It is

certainly a very useful tool for

studying the tactics, techniques,

and procedures (TTPs)

of a naval base, allowing continuous

monitoring of larger

units and submarines over time

if their traditional moorings are

known. A system that activates

an alarm requiring higher-performance

products (in the SAR

and EO domains) can certainly

be integrated in order to fill the

gaps in spatial and temporal resolution

and, at the same time,

activate an additional monitoring

chain in other intelligence

domains to identify any changes

of military interest.

REFERENCES

Bovenga, F., 2018. Synthetic Aperture Radar (SAR) Techniques and Applications. Roma: Università degli Studi

Roma Tre. [Tesi di dottorato].

Copernicus Data Space Ecosystem, 2024. Copernicus Sentinel-1 Data Access. [online] Available at: https://dataspace.copernicus.eu

[Accessed 15 Jul. 2025].

European Space Agency (ESA), 2022. Sentinel-1 SAR User Guide. [online] Available at: https://sentinel.esa.int/

web/sentinel/user-guides/sentinel-1-sar [Accessed 15 Jul. 2025].

Lillesand, T., Kiefer, R.W. and Chipman, J., 2015. Remote sensing and image interpretation. 7th ed. Hoboken, NJ:

John Wiley & Sons.

Pietranera, L., Fardelli, M., Spada, G. and Ciminelli, M.G., 2011. Near real-time flood detection and damage

assessment using high-resolution satellite data. Proceedings of the 2011 IEEE International Geoscience and Remote

Sensing Symposium (IGARSS), pp.3560–3563. doi:10.1109/IGARSS.2011.6050010.

Small, D., 2011. Flattening gamma: Radiometric terrain correction for SAR imagery. IEEE Transactions on Geoscience

and Remote Sensing, 49(8), pp.3081–3093. doi:10.1109/TGRS.2011.2120616.

Quegan, S. and Yu, J., 2001. Filtering of multichannel SAR images. IEEE Transactions on Geoscience and Remote

Sensing, 39(11), pp.2373–2379. doi:10.1109/36.964970.

Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N.,

Brown, M. and Traver, I., 2012. GMES Sentinel-1 mission. Remote Sensing of Environment, 120, pp.9–24.

doi:10.1016/j.rse.2011.05.028.

Ulaby, F.T., Moore, R.K. and Fung, A.K., 1986. Microwave Remote Sensing: Active and Passive. Volume II: Radar

Remote Sensing and Surface Scattering and Emission Theory. Dedham, MA: Artech House.

KEYWORDS

SAR; SERIE TEMPORALE; OPEN DATA; OPEN SOURCE; ESA - COPERNICUS

ABSTRACT

A multi-temporal analysis of Sentinel-1 images (VV polarization) was conducted to monitor activity at the

Chinese naval base in Yulin between January 2024 and June 2025. By constructing a time series of radar

backscatter in decibels, variations compatible with the presence or absence of naval units were detected. The

observed reflectivity peaks are consistent with events documented by OSINT sources, indicating strategic

movements and periods of high operational activity.

AUTHOR

superifa, Planetek Italia - Intelligence & Defense Senior Advisor


GUEST PAPER

Continental-Scale Assessment of

Urban Sprawl in Africa (2016–2024)

Quantifying Built-Up Expansion Using Sentinel-2-Derived

Dynamic World V1 Data and Google Earth Engine

by Johnny Muhindo Bahavira*, Michael Paluku Lukumbi, Junior Lukoo Mitsindo, Mpanga Numbi Loïc,

Hélène Akilimali Kabisuba, Acacia Muley Nyande

Between 2016 and

2024, Africa’s builtup

area expanded by

33.3 % (+ 60 687 km²).

Large economies—

Ethiopia, Nigeria and

Kenya—drove most

absolute growth, while

smaller states—Central

African Republic and

Mauritius—registered

the highest relative increases.

Using Dynamic

World V1 in Google

Earth Engine, this study

maps heterogeneous

urbanization pathways

across 55 countries.

Current findings from

the United Nations

Global Assessment

Report on Disaster Risk

Reduction (DRR) points out

that the economic loss from

disasters such as earthquakes,

hurricanes and flooding range

from US$250 billion to

US$300 billion each year.

In this context Space assets,

such as satellites and remotely

piloted aircraft (drones), can

play a crucial role in emergency

response and disaster

management.

Africa is experiencing one

of the fastest rates of urban

growth worldwide, with its

urban population projected

to double by 2050 (United

Nations Department of

Economic and Social Affairs

[UN DESA], 2018). This

rapid urbanization often

manifests as urban sprawl—an

outward expansion of builtup

areas into peri-urban and

rural landscapes—which poses

significant challenges for

sustainable land management,

infrastructure provision, and

environmental resilience (Seto,

Güneralp, & Hutyra, 2012).

Remote sensing has become a

pivotal tool for monitoring the


GUEST PAPER

spatiotemporal patterns of urban

expansion at multiple scales.

Continental-scale land cover

mapping in Africa has been

demonstrated using Google

Earth Engine, achieving high

accuracy with multi-source data

(Li, Qiu, Ma, Schmitt, & Zhu,

2020). However, most studies

on urban sprawl in Africa

remain confined to specific cities

or regions (e.g., Iandolo, Rossi,

& Bonomi, 2023; Mhangara,

Gidey, & Manjoo, 2024), and

comprehensive assessments

across the entire continent are

still scarce.

This study addresses this gap

by quantifying the evolution of

built-up areas in Africa for the

years 2016, 2020, and 2024

using the Dynamic World V1

dataset within Google Earth

Engine. By aggregating results

at both national and provincial

levels, it aims to reveal spatial

disparities in urban expansion

and lay the groundwork

for subsequent causal and

comparative analyses.

Methodology

The Dynamic World V1 dataset

is a 10 m near real-time land

use/land cover (LULC) product

derived from Sentinel-2 imagery

(Brown et al., 2022). National

administrative boundaries from

the FAO GAUL 2015 dataset

were used to delineate each

African country (FAO, 2015).

For three distinct time periods

(2015–2016, 2019–2020, and

2023–2024), the majority land

cover class (mode) per pixel was

computed, and pixel counts

per class were extracted via

frequency histograms. These

statistics were then aggregated

into result tables exported

in CSV format, providing a

quantitative overview of intraannual

land cover dynamics per

country.

Context and Dataset

Dynamic World V1

Dynamic World V1 is a 10 m

resolution dataset derived from

Sentinel-2 imagery. It provides

predictions of nine LULC

classes along with associated

probabilities. This near real-time

product is generated by a deep

learning model deployed on

the Google Cloud AI Platform

and is updated continuously as

new Sentinel-2 data becomes

available (Brown et al., 2022).

GAUL 2015

National administrative

boundaries were obtained

from GAUL (Global

Administrative Unit Layers),

version 2015, developed by

the FAO to standardize spatial

representations of administrative

units (FAO, 2015). On Google

Earth Engine, this dataset is

available under the identifier

“FAO/GAUL/2015/level0”.

Study Area and Time Periods

The study covers all countries

in Africa, each retrieved as a

Feature in a list. Three time

windows were defined:

• July 1, 2015 – June 30, 2016

(2016 season)

• July 1, 2019 – June 30, 2020

(2020 season)

• July 1, 2023 – June 30, 2024

(2024 season)

These periods correspond to

the historical availability of the

Dynamic World dataset (starting

June 2015).

Land Cover Extraction and

Processing

Spatial Selection and Aggregation

• For each country,

the GOOGLE/

DYNAMICWORLD/V1

collection was filtered by

geometry and time window.

• The mode() function was

applied to determine the most

frequent label per pixel.

• The resulting image was

clipped (clip()) to the country

boundary.

Band Renaming

• The band resulting from

mode() was renamed to

"classification" to standardize

naming.

• A time suffix (_2016, _2020,

_2024) was dynamically

added to each property via a

renaming function (Brown et

al., 2022).

Statistical Aggregation

To quantify the area of each

LULC class:

• The unweighted

frequencyHistogram() reducer

was applied to the classification

band.

• The spatial resolution was set

to 10 m, with maxPixels set to

1e13.

• The reduceRegion() method

returned a dictionary of pixel

counts per class.

Each histogram was converted

into a Feature with null

geometry and merged to form

a single record summarizing

statistics for all three periods.

Exporting Results

The outputs were exported

to Google Drive as CSV files

(one table per country) using

Export.table.toDrive(), with

the filename specified by

fileNamePrefix = countryName

and column headers including

time suffixes.

Post-Processing of Results

The 55 CSV files (one per

African country) included pixel

counts for each land use class—

Water, Trees, Grass, Flooded

Vegetation, Crops, Shrubs,

Built-up, Bare Soil, and Snow—

for the years 2016, 2020,

and 2024. Pixel counts were

converted to square kilometers


GUEST PAPER

2016 2020 2024 relative change in percent

area in sq. km percentage (%) area in sq. km percentage (%) area in sq. km percentage (%) 2016-2020 2020-2024 2016-2024

water 362448.65 1.20 326728.93 1.08 341467.47 1.13 -9.86 4.51 -5.79

Trees 7803943.06 25.74 8156978.02 26.90 8367859.94 27.60 4.52 2.59 7.23

Grass 511620.74 1.69 484988.85 1.60 443645.19 1.46 -5.21 -8.52 -13.29

Flooded vegetation 41392.20 0.14 53025.22 0.17 63837.67 0.21 28.10 20.39 54.23

Crops 1447514.19 4.77 1684740.50 5.56 1613359.38 5.32 16.39 -4.24 11.46

shrub and scrub 6710345.96 22.13 6806064.03 22.45 6763149.07 22.31 1.43 -0.63 0.79

Built 182059.92 0.60 211862.70 0.70 242747.26 0.80 16.37 14.58 33.33

Bare 12945957.57 42.70 12543866.50 41.37 12447398.70 41.06 -3.11 -0.77 -3.85

snow and ice 4033.29 0.01 1154.63 0.00 854.24 0.00 -71.37 -26.02 -78.82

null_values 308989.43 1.02 48895.62 0.16 33986.08 0.11 -84.18 -30.49 -89.00

total 30318305 100 30318305 100 30318305 100

Tab. 1 - Africa Land use land Cover distribution.

in

Excel,

and only the Built-up class

was retained. A summary table

was then created with built-up

surface area per country for the

three years.

From these values, we calculated

the relative percentage increase

for each period (2016–2020,

2020–2024, and 2016–2024),

and the percentage contribution

of each country to the overall

continental urban expansion

from 2016 to 2024.

Visualization

The final table was joined to the

shapefile of African countries

in ArcGIS to visualize two

indicators: the relative increase

in built-up area between 2016

and 2024, and each country’s

percentage contribution to

overall urban growth in Africa.

Fig. 1 – Land use land cover (%) 2016-2020-2024.

AFRICA

Built Up area 2016 (KM2) 182059.92

Built Up area 2020 (KM2) 211862.70

Built Up area 2024 (KM2) 242747.26

Urban sprawl 2016-2020 (KM2) 29802.78

Growth 2016-2020 (%) 16.37

Urban sprawl 2020-2024 (KM2) 30884.56

Growth 2020-2024 (%) 14.58

Urban sprawl 2016-2024 (KM2) 60687.33

Growth 2016-2024 (%) 33.33

Tab. 2 – Built-up areas in Africa and Urban sprawl between 2016, 2020 and 2024.

Results

The land-cover classes provided

by the Dynamic World Version

1 dataset, accessed through

Google Earth Engine, include:

Water; Tree; Grass; Flooded

Vegetation; Crops; Shrub &

Scrub; Built; Bare Soil; and

Snow & Ice. Together, these

nine classes span a total area

of 30 318 305 km² across the

African continent as shown in

Table 1.

Pixels lacking reliable satellite

observations—i.e., null or


GUEST PAPER

ID COUNTRY Urban sprawl

2016-2020

(KM2)

Growth

2016-2020

(%)

Urban sprawl

2020-2024

(KM2)

Growth

2020-2024

(%)

Urban sprawl

2016-2024

(KM2)

1 Algerie 1091.20 14.21 -396.52 -4.52 694.68 9.05

2 Angola -203.91 -7.35 287.64 11.20 83.73 3.02

Growth

2016-2024

(%)

3 Benin 265.51 10.58 471.20 16.98 736.71 29.35

4 Botswana -19.19 -1.32 245.03 17.06 225.84 15.52

5 Burkina Faso 606.13 40.96 679.09 32.56 1285.22 86.86

6 Burundi 355.60 44.63 406.09 35.24 761.69 95.59

7 Cabo Verde 51.79 58.61 26.29 18.76 78.08 88.36

8 Cameroon 933.73 29.81 517.69 12.73 1451.42 46.34

9 Central African

Republic

121.50 57.49 189.31 56.88 310.81 147.07

10 Chad 214.80 35.87 297.97 36.62 512.76 85.63

11 Comores 58.18 59.81 5.73 3.68 63.91 65.70

12 Democratic

republic of the

congo

1431.91 33.59 1291.08 22.67 2722.99 63.88

13 Djibouti 36.18 51.58 12.00 11.28 48.18 68.68

14 Egypt 822.50 11.10 666.42 8.09 1488.91 20.09

15 Equatorial

Guinea

-65.45 -21.30 8.10 3.35 -57.35 -18.67

16 Eritrea 61.76 24.67 55.56 17.80 117.32 46.86

17 Eswatini 23.33 4.45 5.23 0.96 28.56 5.45

18 Ethiopia 5688.86 33.01 2920.74 12.74 8609.60 49.95

19 Gabon -942.61 -47.40 -169.75 -16.23 -1112.36 -55.93

20 Gambia 21.90 4.98 39.24 8.50 61.14 13.90

21 Ghana 1363.38 22.38 1871.16 25.10 3234.54 53.09

22 Guinea -21.57 -1.03 17.72 0.86 -3.86 -0.19

23 Guinea-Bissau 19.14 3.95 6.51 1.29 25.65 5.30

24 Ivory coast 1135.69 36.98 1132.63 26.93 2268.32 73.87

25 Kenya 3611.85 26.78 3082.29 18.03 6694.14 49.63

26 Lesotho 3.25 0.42 119.94 15.60 123.18 16.09

27 Liberia 123.68 17.14 121.05 14.32 244.73 33.92

28 Libya 488.04 14.22 -72.28 -1.84 415.76 12.11

29 Madagascar 252.77 15.54 256.77 13.67 509.55 31.33

30 Malawi -363.92 -9.58 429.85 12.51 65.93 1.73

31 Mali 516.76 32.64 430.52 20.50 947.29 59.84

32 Mauritania 1.72 2.83 40.23 64.33 41.95 68.98

33 Mauritius 197.72 119.45 30.43 8.38 228.15 137.83

34 Morocco 706.78 9.70 79.00 0.99 785.78 10.79

35 Mozambique -244.44 -5.68 508.54 12.53 264.10 6.14

36 Namibia -50.06 -7.53 107.57 17.49 57.52 8.65

37 Niger 171.70 32.53 268.47 38.38 440.18 83.39

38 Nigeria 2597.06 11.87 5312.45 21.71 7909.51 36.16

39 Republic of the

congo

267.19 47.99 -23.44 -2.84 243.75 43.78

40 Rwanda 856.01 33.05 660.01 19.15 1516.02 58.52

41 Sao Tome and

Principe

4.89 22.12 0.43 1.58 5.32 24.05

42 Senegal 190.03 11.84 509.47 28.38 699.50 43.57

43 Seychelles 19.92 103.53 3.50 8.94 23.42 121.72

44 Sierra Leone 13.06 1.94 41.85 6.09 54.91 8.14

45 Somalia 374.73 54.80 311.36 29.41 686.10 100.34

46 South Africa 1659.76 7.30 1105.80 4.53 2765.57 12.17

47 South Sudan -2.91 -0.66 200.31 45.93 197.40 44.96

48 Sudan 914.55 38.43 378.93 11.50 1293.47 54.35

49 Togo 279.86 19.46 581.43 33.84 861.29 59.88

50 Tunisia 1214.27 40.57 -729.96 -17.35 484.31 16.18

51 Uganda 2106.10 29.77 2497.80 27.21 4603.90 65.08

52 United Republic

of Tanzania

1029.19 11.42 2835.59 28.24 3864.78 42.88

53 Western sahara - - - - 0.00 0.00

54 Zambia 210.32 9.00 755.63 29.66 965.95 41.32

55 Zimbabwe -397.43 -18.33 454.85 25.69 57.42 2.65

Tab. 3 – Urban sprawl for the periods 2016-2020, 2020-2024 and 2016-2024 and the percentage of

relative increase for each country.

missing data—decline

markedly over time,

from 1.02 % of the

continental extent in

2016 to 0.16 % in

2020 and 0.11 % in

2024. This reduction

reflects a progressive

enhancement in

both spatial coverage

and data quality,

yielding an overall

data completeness

exceeding 99 % for

Africa and thereby

ensuring high

confidence in landcover

analyses.

In 2016, vegetationrelated

coverages (the

sum of Tree, Grass,

Shrub & Scrub,

Flooded Vegetation,

and Crops) accounted

for 54.47 % of

Africa’s surface. This

proportion increased

to 56.68 % in 2020

and 56.90 % in

2024, indicating a

modest upward trend

in vegetated areas

(figure 1). The Bare

Soil class—principally

comprising desert

and sparsely vegetated

lands—remains

the second most

extensive cover type,

representing 42.70 %

of the continent’s area.

The results show that

the area of built-up

areas in Africa in

2016 was 18,259.92

km², while in 2020

it was 211,862.70

km², compared to

242,747.26 km² in

2024. Africa thus

experienced an urban

sprawl of 16.37%

between 2016 and

2020 and an increase


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Rank

N°

Country ID PAYS Urban sprawl 2016-2024 (KM2) Contribution % of Africa

1 18 Ethiopia 8609.60 14.19

2 38 Nigeria 7909.51 13.03

3 25 Kenya 6694.14 11.03

4 51 Uganda 4603.90 7.59

5 52 United Republic of Tanzania 3864.78 6.37

6 21 Ghana 3234.54 5.33

7 46 South Africa 2765.57 4.56

8 12 Democratic republic of the congo 2722.99 4.49

9 24 Ivory coast 2268.32 3.74

10 40 Rwanda 1516.02 2.50

11 14 Egypt 1488.91 2.45

12 8 Cameroon 1451.42 2.39

13 48 Sudan 1293.47 2.13

14 5 Burkina Faso 1285.22 2.12

15 54 Zambia 965.95 1.59

16 31 Mali 947.29 1.56

17 49 Togo 861.29 1.42

18 34 Morocco 785.78 1.29

19 6 Burundi 761.69 1.26

20 3 Benin 736.71 1.21

21 42 Senegal 699.50 1.15

22 1 Algerie 694.68 1.14

23 45 Somalia 686.10 1.13

24 10 Chad 512.76 0.84

25 29 Madagascar 509.55 0.84

26 50 Tunisia 484.31 0.80

27 37 Niger 440.18 0.73

28 28 Libya 415.76 0.69

29 9 Central African Republic 310.81 0.51

30 35 Mozambique 264.10 0.44

31 27 Liberia 244.73 0.40

32 39 Republic of the congo 243.75 0.40

33 33 Mauritius 228.15 0.38

34 4 Botswana 225.84 0.37

35 47 South Sudan 197.40 0.33

36 26 Lesotho 123.18 0.20

37 16 Eritrea 117.32 0.19

38 2 Angola 83.73 0.14

39 7 Cabo Verde 78.08 0.13

40 30 Malawi 65.93 0.11

41 11 Comores 63.91 0.11

42 20 Gambia 61.14 0.10

43 36 Namibia 57.52 0.09

44 55 Zimbabwe 57.42 0.09

45 44 Sierra Leone 54.91 0.09

46 13 Djibouti 48.18 0.08

47 32 Mauritania 41.95 0.07

48 17 Eswatini 28.56 0.05

49 23 Guinea-Bissau 25.65 0.04

50 43 Seychelles 23.42 0.04

51 41 Sao Tome and Principe 5.32 0.01

52 53 Western sahara 0.00 0.00

53 22 Guinea -3.86 -0.01

54 15 Equatorial Guinea -57.35 -0.09

55 19 Gabon -1112.36 -1.83

Tab. 4 – Percentage contribution of each country to urban sprawl in Africa between 2016-2024.


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of 14.58% between 2020 and

2024, resulting in an overall

increase of 33.33% in built-up

areas in Africa between 2016

and 2024, or 60,687.33 km², as

shown in Table 2.

Table 3 presents the results of

urban sprawl for the periods

2016-2020, 2020-2024 and

2016-2024 and the percentage

of relative increase for each

country. When considering

relative growth (percentage

increase relative to the 2016

baseline) for the period 2016-

2024 as illustrate in the figure 2

, the Central African Republic

had the highest rate at +147%,

followed by Mauritius (+137%),

Seychelles (+121%), Somalia

(+100%), Burundi (+95%),

Burkina Faso (+86%), Chad

(+85%), Niger (+83%), and

Côte d'Ivoire (+73%). The

Democratic Republic of the

Congo ranked 15th with a

+63.8% increase. The number

labeled on the maps in

Figures 2 and 3 indicates the

Country ID that you can find

in Tables 3 and Table 4.

These results underscore

significant heterogeneity

in urban sprawl across

Africa. The absolute growth

is concentrated in large

economies and rapidly

expanding cities (e.g., Lagos,

Nairobi, Addis Ababa), while

the highest relative growth

rates are observed in smaller

or rapidly urbanizing nations,

possibly reflecting emerging

urban centers or peri-urban

development (Seto et al.,

2012).

and infrastructure investments

have led to rapid expansion

around Addis Ababa and other

cities (World Bank, 2021).

Conversely, countries with

limited infrastructure may

exhibit lower absolute growth,

even when relative growth is

high.

Future research should

incorporate socioeconomic

variables (e.g., GDP per capita,

population density, governance

indices) to analyze causal

relationships and simulate

urban growth under alternative

development scenarios (Seto

et al., 2012; Schneider &

Woodcock, 2008).

Conclusion

This study offers a

comprehensive, continent-wide

assessment of urban sprawl

in Africa from 2016 to 2024,

using high-resolution remote

sensing data (Dynamic World

V1) and cloud-based processing

(Google Earth Engine). The

findings illustrate both absolute

and relative growth patterns,

revealing stark differences

between countries.

Major economies and urban

hubs drove most of the absolute

expansion, while smaller or less

urbanized nations exhibited

remarkable relative growth—

highlighting future urbanization

hotspots. These insights are

crucial for land-use planning

and policy design. Integrating

remote sensing data with

socioeconomic and governance

indicators will enable predictive

modeling and scenario-based

planning to guide sustainable

urban development across

Africa.

Discussion

The spatial patterns

suggest that economic size,

population pressure, and

governance frameworks are

key drivers of urban sprawl.

For example, Ethiopia's largescale

urban development

Fig. 3 – Percentage contribution of each country to urban sprawl in Africa between

2016-2024.


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BIBLIOGRAPHY

Anderson JR, Hardy EE, Roach JT & Witmer RE A Land Use and Land Cover Classification System for Use with Remote Sensor Data. U.S.

Geological Survey , Report 964 (1976).

Brown CF et al. Dynamic World training dataset for global LULC categorization of satellite imagery. PANGAEA (2021). https://doi.

org/10.1594/PANGAEA.933475

Brown CF, Brumby SP, Guzder -Williams B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data 9,

251 (2022). https://doi.org/10.1038/s41597-022-01307-4

Deng, X., Shang, S., & Li, S. (2022). Dynamic World: A near real-time global land use land cover dataset at 10 m resolution derived from Sentinel-2

imagery. International Journal of Remote Sensing, 43 (10), 3500–3525.

ESA. Comparative validation of recent 10 m-resolution global land cover maps. Remote Sensing of Environment (2024).

Food and Agriculture Organization. (2015). Global Administrative Unit Layers (GAUL) . FAO.

Gorelick , N., Hancher , M., Dixon, M., Ilyushchenko , S., Thau , D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial

analysis for everyone. Remote Sensing of Environment, 202 , 18–27. https://doi.org/10.1016/j.rse.2017.06.031

Iandolo , A., Rossi, F., & Bonomi , M. (2023). Urban sprawl dynamics in West African cities: A remote sensing approach. Journal of Urban Studies,

58 (2), 213–231.

Li, Q., Qiu, GY, Ma, R., Schmitt, M., & Zhu, AX (2020). Continental-scale urban mapping using Google Earth Engine. Remote Sensing of Environment,

234 , 111482.

Mhangara , P., Gidey , WT, & Manjoo , V. (2024). Monitoring urban sprawl in Southern Africa using satellite imagery and GIS. International

Journal of Applied Earth Observation and Geoinformation , 102 , 102779.

Phiri D. et al. Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing 12, 2291 (2020). https://doi.org/10.3390/rs12142291

Schneider, A., & Woodcock, C.E. (2008). Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities

using remotely sensed data. Landscape and Urban Planning, 87 (1), 54–73.

Seto , K.C., Güneralp , B., & Hutyra , LR (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon.

Proceedings of the National Academy of Sciences, 109 (40), 16083–16088.

Shekhar , S., & Xiong , H. (2008). Encyclopedia of GIS . Springer.

United Nations Department of Economic and Social Affairs. (2018). World Urbanization Prospects: The 2018 Revision . United Nations.

World Bank. (2021). Ethiopia Urbanization Review . World Bank Publications.

KEYWORDS

URBAN SPRAWL; AFRICA; IMAGE ANALYSIS; SATELLITE IMAGES

ABSTRACT

This study provides a comprehensive, continent-wide quantification of urban sprawl in Africa between 2016 and 2024 by exploiting the

Dynamic World V1 dataset within Google Earth Engine. We computed the dominant land-cover class per pixel for three time windows

(2015–2016, 2019–2020, 2023–2024), aggregated built-up area changes nationally, and calculated both absolute and relative growth rates.

Results reveal a net increase of 60,687 km² of built-up land in Africa, driven primarily by Ethiopia, Nigeria, and Kenya in absolute terms,

while smaller states like the Central African Republic and Mauritius exhibit the highest percentage gains. Our findings highlight pronounced

spatial heterogeneity in urban expansion and underscore the need to integrate socioeconomic and governance indicators to inform sustainable

land-use planning across Africa.

AUTHOR

Johnny Muhindo Bahavira (1, 2,)

Johnny.muhindo@inbtp.com

Michael Paluku Lukumbi (2, 3, 4)

Junior Lukoo Mitsindo (2, 3,4)

Mpanga Numbi Loïc (5)

Hélène Akilimali Kabisuba (5)

Acacia Muley Nyande (6)

Department of Building and Civil Engineering, Institut National du Bâtiment et des Travaux Publics (INBTP), Kinshasa, Democratic

Republic of Congo (1)

Laboratoire INBTP : Geomatics, IT, Civil Engineering, Geotechnics, Hydraulics, Physics and Chemistry, Institut National du

Bâtiment et des Travaux Publics (INBTP), Kinshasa, Democratic Republic of Congo (2)

Department of Hydraulic and Environmental Engineering, Institut National du Bâtiment et des Travaux Publics (INBTP),

Kinshasa, Democratic Republic of Congo (3)

Engineering of Structures, Foundations and Material’s Phd Program, Universidad Politécnica de

Madrid (UPM), Madrid, Spain (4)

Geotechnical Engineering Master’s Program, Department of Building and Civil Engineering, Institut National du Bâtiment

et des Travaux Publics, Kinshasa, RDC (5)

Department of Environmental Science, Université Nouveaux Horizons, Lubumbashi, Democratic Republic of Congo (6)


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From Heat Islands to Green Spaces:

Graz's Comprehensive Climate Strategy

by Ada Perello

Aerial thermal imagery

of Graz city Centre.

Thermal imagery,

or thermography,

visualizes the infrared

energy (heat)

emitted by objects

and surfaces, making

temperature differences

visible. This

powerful tool is used

in urban planning to

identify a variety of

thermal phenomena,

from "heat islands"

in densely populated

areas to heat loss

from individual buildings.

The data helps

decision-makers create

more sustainable and

energy-efficient cities.

Credit City of Graz

The Austrian city of

Graz, home to 300,000

residents in Styria,

has established itself

as a pioneer in urban

climate analysis across

Central Europe.

With decades of experience integrating

climatic data into urban planning—

following Stuttgart's early example—

Graz continues to push boundaries through

dedicated research centers like the Wegener

Center at the University of Graz. This

commitment to climate science has recently

manifested in two groundbreaking projects

that demonstrate the city's innovative approach

to environmental monitoring, both involving

collaboration with EAASI member AVT

Airborne Sensing.


REPORT

Comprehensive Climate Data

Integration Through USAGE

The first initiative sees Graz

participating as an external

partner in the USAGE

(Urban Data Space for

Green Deal) project, an EU

Horizon-funded program

designed to help cities

implement the European

Green Deal through

advanced data analytics.

While Graz already

possessed extensive climatic

datasets from decades of

research, interdependencies

and interactions between

these resources were only

partly exploited.

USAGE has transformed

this landscape by

implementing two specific

use cases that showcase

the power of integrated

geospatial analysis. The

first addresses urban heat

island monitoring, where

the city traditionally relied

on satellite thermal imagery

without quality assessment.

Through USAGE, a

comprehensive validation

methodology now compares

satellite data with thermal

aerial imagery, analyzing

error distribution across

different surface materials.

This approach revealed

that estimation errors

varied significantly between

surfaces—asphalt showing

different patterns compared

to grass or other materials.

The second use case tackles

accessibility to green

infrastructure through

the innovative "3-30-

300 rule"—a framework

ensuring residents can

see at least three trees

from their homes, live in

neighborhoods with 30%

tree canopy cover, and access

open green spaces within

300 meters. Buildings

meeting all three criteria

appear in green on resulting

maps, while those failing all

criteria show in red, creating

powerful visualization tools

for decision-makers and

urban planners.

This methodology

directly supports Graz's

Klimainformationssystem

(KIS), launched in 2020 to

provide dynamic climate

visualization. The system

integrates diverse data

sources, including climate

observation stations and

thermal aerial images,

enabling large-scale analysis

and vulnerability assessments

while supporting predictive

modeling based on IPCC

climate scenarios.

Graz is one of four pilot

cities in the USAGE

consortium, alongside

Ferrara, Leuven, and

Zaragoza. Each location

addresses specific

local challenges while

contributing to scalable

methodologies designed for

replication across European

cities.

Precision Thermal Mapping

for Energy Efficiency

The second major

project represents a direct

application of crewed aerial

surveying technology. In

late November 2024, Graz

conducted a comprehensive

aerial thermal survey to

identify heat loss across

urban rooftops, supporting

city-wide energy efficiency

initiatives.

AVT Airborne Sensing

operated the mission using

a crewed aircraft equipped

Cockpit. Unlike satellite imagery, a crewed aircraft offers superior precision and control, allowing

operators to fly under optimal conditions—such as at night with a clear sky—to capture highresolution

data. This method provides the detailed, actionable intelligence that turns broad climate

goals into practical planning solutions. Credit AVT


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with dual longwave

infrared (LWIR) thermal

cameras during optimal

nighttime conditions—

clear skies, minimal wind,

and temperatures below

5°C. Over four hours, the

aircraft captured more than

13,000 thermal images

along a 600-kilometer

flight path, processed into

a seamless thermal mosaic

achieving approximately

35-centimeter ground

resolution.

The surveying department's

fusion of thermal imagery

with geospatial roof data

enabled precise rooftop-byrooftop

analysis, creating a

publicly accessible web tool

allowing residents to assess

their buildings for potential

heat loss. This practical

application supports

both the city's climate

action goals and provides

actionable guidance for

homeowners considering

thermal renovations.

Graz 3-30-300 RULE. The 3-30-300 rule is a simple but powerful framework for urban greening.

It means residents should be able to see at least 3 trees from their homes, live in a neighborhood

with 30% tree canopy cover, and have a park or green space within 300 meters. Credit: Image created

with Canva Pro/ Ada Perello EAASI

Setting Standards for

European Cities

The integration of crewed

aerial surveying with

advanced data analytics

showcases how traditional

geospatial techniques

remain essential for modern

climate monitoring. While

satellite data provides broad

coverage, the precision

and validation capabilities

offered by crewed aircraft

operations prove invaluable

for detailed urban analysis.

These initiatives position

Graz at the forefront

of climate-responsive


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urban planning,

demonstrating how

decades of research

excellence, combined

with innovative

partnerships and

cutting-edge

aerial surveying

capabilities, can create

practical solutions

for contemporary

environmental

challenges. The

city's approach

offers a blueprint for

municipalities seeking

to leverage geospatial

technology in support

of sustainable urban

development and

climate resilience.

ABOUT EAASI

The European Association of Aerial Surveying Industries (EAASI) is a non-profit organization

founded in 2019 that represents Europe's crewed aerial surveying sector. Its primary mission is

to unify and advocate for an industry that is the leading provider of high-resolution geospatial

data for critical applications like infrastructure planning and flood protection. EAASI champions

the importance of crewed aerial surveying and fosters collaboration among its members to

promote best practices and ensure the industry's continued growth and influence in a rapidly

evolving digital world.

ABOUT THE AUTHOR

Ada Perello is a journalist specializing in geospatial content and a regular contributor to industry

magazines. She serves as the Communications Manager for the European Association of Aerial

Surveying Industries (EAASI), where she brings a passion for technology and expertise in strategic

communications. She holds a Master's degree in both Communication and International

Business Administration.

KEYWORDS

Graz, thermal imagery, green deal, climate change

ABSTRACT

Graz has emerged as a pioneer in climate-responsive urban planning, combining decades of

research with innovative geospatial applications. Within the EU-funded USAGE project, the

city validated satellite thermal imagery against aerial surveys and applied the “3-30-300 rule”

to map access to green infrastructure, feeding results into its Klimainformationssystem. A complementary

aerial thermal campaign captured high-resolution rooftop data to identify energy

loss, supporting both municipal strategies and citizen-led renovations. These initiatives illustrate

how integrating satellite, aerial, and ground-based data can transform climate monitoring into

practical planning tools. Graz’s approach offers a scalable model for European cities seeking

sustainable development and greater climate resilience.

AUTHOR

Ada Perrello, EAASI

communication@eaasi.eu


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Lake District, UK

(13th july 2025)

The varied landscape of England’s Lake District is

featured in this image captured by the Copernicus Sentinel-2

mission. Located in northwest England in the county of Cumbria,

the Lake District is shaped by a harmonious mix of several natural landforms,

and extends to the coast of the Irish Sea, facing the Isle of Man, partly

visible in the far left of the image. Lakes, hills, valleys, woodland, settlements and

farmland combine to give life to England's largest national park, which was designated a

UNESCO World Heritage Site in 2017. As we see in the image, the Lake District features

a roughly circular core of mountains, deeply carved by valleys of glacial origin, hosting long,

narrow lakes in their hollows. The region is home to England’s highest peak, Scafell Pike,

which reaches an elevation of 978 m. On a clear day, the view can span from the Galloway Hills

of Scotland to the Mourne Mountains in Northern Ireland, as well as the Isle of Man, and Eryri

(Snowdonia) in Wales. As the name suggests, the area is home to the principal lakes of England, including

the largest, Windermere, whose elongated shape can be seen south of the central massif. Next

to Windermere is Coniston Water and further west are Wasdale valley and Wastwater, the deepest lake

in England.Northwest of Wastwater lies Derwentwater, studded with wooded islands, and further east

is Ullswater, the second largest lake in the region, known for the daffodils that inspired Wordsworth’s

famous poem of the same name. While the higher hills – also known as fells – are mainly rocky,

deciduous native woodland occurs on many of the lower, steeper slopes. Extensive agriculture and

farmland can be seen lower down around the mountains, interspersed with villages and settlements,

which appear as grey areas. The Lake District is also home to varied freshwater habitats,

such as mires, lakeshore wetlands, coastal heath, dunes and a number of estuaries, including

Morecambe Bay, visible in the bottom right corner of the image. Covering an area of 310 sq

km, Morecambe Bay is the UK’s largest expanse of intertidal mudflats and sand and supports

a wealth of wildlife, with abundant bird and marine species. Zooming in off the

coast of Morecambe Bay, the turbines of several offshore wind farms stand out as

white dots in the Irish Sea water.

[Credits: contains modified Copernicus Sentinel data (2025),

processed by ESA]


MARKET


REPORT

Why you need to use ground control

points (GCP) for drone mapping

by Eric van Rees

Measuring GCP with Emlid Reach RX

Ground control points

(GCPs) are physical

markers on the ground

with known coordinates.

These points are essential

in drone mapping as they

improve the georeferencing

accuracy of aerial images,

ensuring that maps and

models align with realworld

coordinates. GCPs

are critical for industries

such as construction,

surveying, and

environmental monitoring,

where precision is key.

This article clarifies GCPs

role, answering common

questions like: do I need

GCPs even with an RTK setup?

How many should I use? How

to place and collect them? Read

on to understand why GCPs

can still be essential, even with

advanced drones, and learn best

practices for integrating them

effectively.

How do GCPs work in drone

mapping?

GCPs work by providing

known coordinates that help

align the images captured

by a drone with real-world

coordinates. During the

mapping process, the position

of each GCP is recorded

using high-precision GNSS

equipment. After the drone

flight, these GCPs are identified

in the aerial images, and the

mapping software uses their

positions to correct distortions

and errors in the imagery.

How do GCPs enhance accuracy

in different drone mapping

workflows?

When it comes to drone

mapping, there are two main

workflows to consider:

1. Drone mapping with regular

(non-RTK GPS) drones. This

workflow relies heavily on

GCPs because of the lower

accuracy of standalone GNSS

systems installed on regular

drones, which typically result

in positional errors of 1–2

meters. GCPs correct these

errors during post-processing

to enhance the accuracy of the

aerial maps. This method was

common before the widespread

adoption of RTK drones.

2. RTK/PPK drone mapping.

RTK (Real-Time Kinematic) and

PPK (Post-Processing Kinematic)

drone mapping offer more

precise positioning, reducing

errors to a few centimeters.

While RTK drones can

significantly reduce the need

for GCPs, they still benefit

from GCPs in large or complex

environments where maximum

accuracy is required.

Do you need GCPs

with RTK drones?

A common question is whether

GCPs are necessary when

using an RTK drone. The

answer is yes, GCPs are still

recommended, especially in

large or uneven areas. Although

RTK drones provide real-time,

high-precision positioning,

GCPs act as an extra layer

of quality control, ensuring

that even minor positional


REPORT

deviations are corrected. GCPs

become even more critical when

mapping areas with complex

topography or when the highest

accuracy is essential, such

as topographic mapping or

construction site planning.

How to identify and mark

ground control points?

GCPs appear in various forms,

including the following:

• Painted marks on the ground,

including circles, the letter

“X,” squares, and checkerboard

patterns;

• Metal or plastic survey disks

and plates, that are fixed to the

ground;

• Metal or wooden survey stakes

or pins, that are driven into

the ground;

• Natural features such as

boulders or the corner of a

field.

GCPs should be marked

properly so they are clearly

visible in the field and in the

drone imagery.

Bright and high-contrast

colors such as white, yellow,

and fluorescent colors are

good choices, to differ from

the surroundings as much as

possible. The materials used for

GCPs should be durable, for

example, metal discs, stakes, or

fabric targets. See in the BOX

some creative examples of GCPs

from Emlid users.

the Nevada Department of

Transportation. More GCPs

do not significantly improve

accuracy beyond this range, and

the time investment in placing

extra GCPs may not yield

substantial benefits.

Key considerations include:

• Project area size: For smaller,

less complex areas, fewer

GCPs may be sufficient. More

GCPs will be needed to cover

the site adequately for larger

areas or projects requiring high

precision.

• Terrain complexity: If the

landscape has significant

variations in elevation or

rugged terrain, additional

GCPs might be required to

account for distortions in the

mapping data.

• Accuracy requirements: For

projects demanding high

accuracy, more GCPs will

ensure that the entire site

is accurately georeferenced.

Lower accuracy projects may

require fewer GCPs.

Where should GCPs be placed?

Placing Ground Control Points

(GCPs) for a drone mapping

project requires careful planning

to ensure accurate results.

While uniform placement of

GCPs across the entire area

might seem ideal, it’s often not

practical or necessary in reallife

scenarios. Instead, GCP

placement should be adapted to

the project’s needs and terrain

characteristics. Here’s how to

approach it:

• Strategic placement:

GCPs should be distributed

strategically, rather than

uniformly, across the area to

capture both horizontal and

vertical variations. It’s crucial

to cover areas with significant

elevation changes to minimize

distortions and ensure accurate

georeferencing.

• Avoid boundary distortions by

placing GCPs correctly: While

GCPs should be placed near

the corners of the project area,

ensure the coverage extends

beyond the area of interest. This

prevents mapping distortions

that can occur when critical

points fall outside the GCP

boundaries. Expanding the

coverage beyond the immediate

site ensures accuracy across the

entire mapped area, including

the edges.

• Elevation consideration: In

areas with significant elevation

differences, place GCPs at both

How many GCPs are needed for

drone mapping?

The number of GCPs required

for a drone mapping project

depends on various factors,

including the size of the site,

terrain complexity, and the

desired level of accuracy. The

ideal number of GCPs for

most drone mapping projects

is between 5 and 10, according

to the tests conducted by

The key rule is to use high-contrast colors for GCP to ensure they are easily visible from the

drone. This is why GCPs are often designed in black and white


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the highest and lowest points to

account for vertical accuracy.

This helps correct potential

distortions caused by elevation

changes during data processing.

Stable and visible locations:

GCPs should be placed on

stable ground and positioned

in areas that are not easily

disturbed. Avoid placing GCPs

in spots where they could

be obstructed by vegetation,

shadows, or other barriers that

could affect visibility in drone

imagery.

For detailed guidance on GCP

placement, including the entire

workflow from setup to postprocessing,

watch Emlid’s indepth

webinar and tutorial.

How are ground control points

collected?

After placing a GCP, its

position needs to be measured

with an RTK or PPK-capable

GNSS receiver. Any Emlid

Reach device including

the Reach RX and Reach

RS3 can be used for this task.

Due to its compatibility,

portability and lightweight,

the Reach RX rover suits well

for placing and measuring of

GCPs, while the Reach RS3

can function as both a base and

a rover.

The Reach RX is an ideal

GNSS receiver for jobs that

require quick, efficient, and

precise measurements, such

as placing and measuring the

position of ground control

points (GCPs). This compact,

lightweight rover excels in

scenarios where portability, ease

of use, and speed are critical.

How are the measured GCPs

used during image processing?

A list of all coordinates of

different GCPs is stored in

a .csv or .txt file, specifying

the longitude, latitude, and

altitude for each point. That

file must be uploaded with the

drone imagery to a software

platform that stitches all

imagery together in a single,

orthorectified map of the

surveying area.

By identifying the exact pixel

locations of GCPs in the

images, photogrammetric

software can match these points

to their known geographic

coordinates. This alignment

corrects positional errors and

distorts, ensuring the drone

images accurately reflect realworld

locations.

www.youtube.com/emlid

Conclusion

GCPs remain essential even

with RTK and PPK drones.

While these advanced drones

can reduce the number of

GCPs needed, they cannot

eliminate the need for them

entirely, especially for large or

complex sites. GCPs act as a

safeguard, ensuring the highest

possible accuracy for all drone

mapping projects.

Whether you’re working

on a small-scale project

or a large industrial site,

incorporating GCPs ensures

your drone imagery is as

accurate as possible, saving

time, minimizing errors, and

producing reliable results.

For most drone mapping

projects, the optimal number

of GCPs ranges from 5 to

10, as adding more than 10

offers minimal improvement

in accuracy. Once a GCP is

placed, its location should

be measured using a GNSS

receiver with RTK or PPK

capabilities. Any Emlid Reach

device, such as for example

Reach RS3, can be used for this

purpose.

Learn more about how to

enhance your RTK workflows

with Emlid’s high-precision

receivers and software here:

https://emlid.com/drone-mapping/.

FAQ

How to use ground control points for drone

mapping?

Ground Control Points (GCPs) are used to

correct for positional errors of drone imagery

and match them with their real-world

positions. After flying a drone to map the area

in which the GCPs are placed, the known

position of the GCPs are correlated with the

captured drone imagery using specialized

software to enhance spatial accuracy.

How do we collect ground control points (GCPs)?

GCPs are recorded by placing highly visible

markers at known coordinates throughout

the survey area. These coordinates are

precisely measured using GPS or total station

equipment. During drone flights, GCPs are

captured in the imagery.

Do you need ground control points with RTK?

Yes, it is recommended to use ground control

points in combination with RTK for quality

control and error correction in aerial surveys.

Try to place GCPs at the highest and lowest points on the site. It will help perform

the correct flat projection.

What is a GCP on a map?

A GCP on a map is a distinct and easily

identifiable location for which the ground

coordinates are known. A GCP can be a

marked point on the ground, but also a natural

or man-made object on a well-noticeable spot.


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Creative examples of GCPs

Credits: All images by the "Show us your Ground Control Point targets - Share your experience in the Emlid User community".

(https://community.emlid.com/t/show-us-your-ground-control-point-targets-share-your-experience/13039)


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REFERENCES

https://blog.emlid.com/how-to-place-ground-control-points-tutorial-by-emlid/

https://blog.emlid.com/more-than-six-thousand-points-over-four-days-interview-with-emlid-user/

https://blog.emlid.com/precise-drone-mapping-with-emlid-gear-setup-gnss-base-station-gcps-and-more/

KEYWORDS

GCP; drone mapping; Ground Control Point; photogrammetry;

ABSTRACT

The article explains why Ground Control Points (GCPs) remain essential in drone mapping, even with the advent of

RTK/PPK drones. GCPs act as reliable reference markers that correct positional errors, ensuring that aerial imagery aligns

accurately with real-world coordinates. The text outlines how to design and place GCPs, how many are typically needed

(usually 5–10), and how they are integrated into photogrammetry software. By providing best practices and practical tips,

the article highlights how GCPs serve as a critical layer of quality control, guaranteeing accurate and dependable results for

applications in construction, surveying, and environmental monitoring.

AUTHOR

Eric van Rees

Consultant in geospatial technology

https://www.linkedin.com/in/ericvanrees/


REPORT

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REPORT

Watershed Analysis and Risk

Assessment Using Global Mapper

by Jenna Nelson

Humans typically conceptualize

land based on administrative

boundaries—such as countries,

states, and municipalities. However,

physical features like ridgelines and

drainage basins often provide more

ecologically relevant demarcations.

Tools like Global Mapper® allow users

to move beyond political borders

and analyze the topographical and

hydrological characteristics of

landscapes.

Fig. 1 From a publicly available national dataset, large watershed areas for the continental United

States are shown. Each watershed covers portions of multiple states, with the watershed boundary

not conforming to state lines.

Introduction

Watershed analysis is especially

important in identifying flood

risks, managing stormwater,

and understanding the

potential for contamination

from point sources such as

landfills. This study focuses

on the Tennessee Region

watershed and illustrates how

Global Mapper’s suite of spatial

and hydrological tools can be

used for watershed delineation,

area measurement, and

environmental risk modeling.

Methods

Watershed Delineation and

Spatial Operations

A publicly available national

watershed dataset was loaded

into Global Mapper to visualize

large watershed areas across the

continental United States. Each

watershed spans multiple states,

often without adhering to state

boundaries. A specific focus

was placed on the Tennessee

Region watershed, which

crosses nine state lines.

To isolate the regions of

each state that fall within

this watershed, a Spatial

Operation was performed

using the Intersection function.

This operation created a

new layer containing only

the overlapping portions of

each state and the Tennessee

watershed.

To quantify area by state,

Global Mapper’s Digitizer Tool

was used to measure the size

of the intersecting polygons.

States with multiple disjointed

features (e.g., Georgia) were

grouped into multipart

polygons, enabling accurate

calculation of total watershed

area per state.

Sub-Watershed Generation

Using a national terrain dataset,

the Watershed Generation

tool in Global Mapper was

employed to delineate microwatersheds.

Parameters such

as stream flow length, stream

size, and depression fill depth

were adjusted to control the

resolution and scale of the

output.

• Larger stream thresholds

resulted in broader watershed

areas.

• Smaller stream parameters

identified finer sub-basins,


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capturing localized water flow.

Environmental Risk Modeling

Environmental hazards,

particularly those affecting

water quality, were analyzed

using landfill location data.

The Search Vector Data tool

identified currently open

solid waste management

facilities within the Tennessee

watershed.

Selected landfills were used as

starting points in the "Trace

Flow from Selected Point(s)"

analysis, which generates

streamlines from each location

to show surface water flow

across the terrain. These

streams were then intersected

with watershed or municipal

boundaries to determine the

potential impacted area.

Results

Watershed and State Overlap

The Tennessee Region

watershed was successfully

isolated from the national

dataset and intersected with

nine state boundaries. Unique

colors were applied to each

state's portion within the

watershed. Area calculations

produced a quantitative

breakdown, enabling watershed

management planning by state.

Micro-Watershed Delineation

Adjusting stream size

thresholds resulted in two types

of watershed outputs:

• Macro-watersheds, which

illustrated general drainage

regions.

• Micro-watersheds, showing

detailed local catchment zones

that feed into larger systems.

These layers provided the

groundwork for linking terrain

analysis with environmental

impact assessments.

Landfill

Contamination Risk

Using landfill location data,

open sites were identified

within the Tennessee Region

watershed. The water drop

analysis generated flow paths

from each landfill, showing

potential contamination

trajectories.

Intersecting these flow paths

with watershed boundaries

highlighted areas at risk for

pollution from leachate. This

model allows planners to focus

mitigation efforts on high-risk

zones.

4. Discussion

The findings demonstrate the

capability of Global Mapper

to conduct in-depth watershed

analysis across multiple spatial

and thematic layers. While

administrative boundaries

remain critical for governance,

watershed-based planning

offers a more ecologically

sound framework, particularly

for managing shared water

resources and environmental

risks.

The ability to simulate water

flow and quantify areas at risk

from point-source pollution

is invaluable for proactive

resource management.

With increasing pressure on

water systems from climate

Fig. 2 - From a publicly available national dataset, large watershed areas for the continental United States are shown. Each watershed covers

portions of multiple states, with the watershed boundary not conforming to state lines.


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Fig. 3 -Left: A simple Spatial Operation is set up to isolate the areas where specific states and the Tennessee Region watershed intersect.

Right: Colored uniquely, areas from each state in the watershed are seen.

change, urbanization, and

waste mismanagement, tools

like Global Mapper provide

essential support for evidencebased

environmental planning.

Conclusion

Watershed analysis using

Global Mapper® offers a

robust methodology for

understanding and managing

hydrological systems beyond

administrative borders. The

Tennessee Region watershed

case study highlights the tool's

strength in spatial operations,

terrain modeling, and

environmental risk assessment.

By integrating terrain data,

landfill locations, and flow

simulations, stakeholders can

better predict, visualize, and

respond to watershed-scale

threats, ensuring sustainable

management of land and water

resources.

Implementation Extension:

COAST for Coastal Risk and

Damage Assessment

As a further extension of

Global Mapper’s capabilities,

the COAST (COastal

Adaptation to Sea Level Rise

Tool) provides a purposebuilt

interface to evaluate

the economic impacts of sea

level rise and coastal flooding.

Developed by Blue Marble

Geographics in partnership

with the New England

Environmental Finance Center

(NEEFC) and Catalysis

Adaptation Partners, COAST is

built using the Global Mapper

SDK and is available as a free,

standalone tool.

COAST assists stakeholders in

planning for future climaterelated

threats by modeling

expected damages under

various storm and sea-level

scenarios and comparing the

costs and benefits of mitigation

strategies. Two key outputs are

generated:

• Damage visualization maps

that show flood extent and

asset loss in a given scenario.

• Cumulative damage charts

that assess long-term impacts

across different planning

horizons.

For example, in a modeled

scenario for downtown

Portland, Maine, with 1.8

meters of sea level rise and

a 100-year flood event by

2100, COAST projected losses

exceeding $500 million, with

single-parcel damage reaching

over $54.1 million. Alternative

strategies—such as levees or

surge barriers—can then be

evaluated for their effectiveness

and return on investment.

Adaptation options modeled

in COAST include both hard

infrastructure (e.g., levees) and

soft strategies (e.g., rezoning,

flood-proofing). Outputs can

be exported to Google Earth

and used for cost-benefit

analysis, allowing community

leaders to make informed

decisions about how and where

to invest in resilience.

Importantly, COAST allows

for scenario modeling

across a range of sea level

rise projections and storm

intensities, giving planners

the flexibility to evaluate risk

under uncertainty and test

both "action" and "no-action"

outcomes.


REPORT

Fig. 4 - With the water drop analysis, only streams are generated, showing the flow from the selected landfill points over the terrain.

REFERENCES

“Principles of Watershed Management.” Watershed Academy Web Environmental Protection Agency,

cfpub.epa.gov/watertrain/moduleFrame.cfm?parent_object_id=490. Wilson, Reid. “Map: The United

States of Watersheds.” The Washington Post, WP Company, 23 Apr. 2013, washingtonpost.com/blogs/

govbeat/wp/2013/11/19/map-the-united-states-of-watersheds/.

KEYWORDS

GIS; Global Mapper; watershed analysis; environmental risk; infrastructure; land use; COAST

ABSTRACT

Watershed analysis provides critical insights into the natural flow of water across landscapes, helping to

identify environmental risks and inform land-use and infrastructure planning. This paper explores how

Global Mapper®, a geographic information system (GIS) tool, can be used to delineate watersheds, analyze

topographic boundaries, and assess environmental threats such as landfill contamination. Through a

case study of the Tennessee Region watershed, the paper demonstrates how spatial operations, watershed

generation tools, and terrain analysis can support data-driven watershed management and risk mitigation.

The paper also presents COAST, an extension of Global Mapper developed for coastal adaptation

planning, as an advanced implementation that enhances watershed and coastal flood risk analysis through

economic impact modeling.

AUTHOR

Jenna Nelson,

info@bluemarblegeo.com

Training and Outreach Coordinator | Blue Marble Geographics


REPORT

Harnessing Space Assets for

Emergency Response: Insights from

the First EUSATfinder Advisory Board

by Marco Nisi

Rapid disaster response

depends on integrated

satellites, terrestrial

networks, and digital tools.

The EUSATfinder project

unites GOVSATCOM,

Copernicus, and Galileo

with drones and AI, giving

first responders secure

communications, real-time

situational awareness,

and mobile operational

centers—enabling faster,

coordinated action across

Europe during floods,

wildfires, landslides, and

earthquakes.

On 1 July 2025, the municipality

of Île-Rousse

in Corsica hosted the

inaugural EUSATfinder Advisory

Board (AB#1), gathering

representatives of European

institutions, first responders,

technology providers, and research

bodies. The aim was to discuss

how Europe’s space, aerial,

and terrestrial technologies can

strengthen emergency and crisis

management.

The meeting underscored the

growing role of secure communications,

real-time data

processing, and multi-platform

integration in supporting first

responders. Special focus was

given to the EUSATfinder

project, a Horizon Europe initiative

funded by the European

Union Agency for the Space

Programme (EUSPA).

The context: European space

assets for emergencies

The Advisory Board emphasized

the synergistic use of three

flagship European programmes:

GOVSATCOM, Copernicus,

and Galileo. GOVSATCOM

provides secure satellite communications

for government and

emergency users. Copernicus

delivers Earth observation services

offering critical geospatial

data. Galileo ensures precise

positioning and timing, which

is essential for coordinated interventions.

Speakers highlighted that

combining these assets with

terrestrial networks allows rapid

and coordinated responses to

natural disasters, geo-hazards,

and technological accidents. Dr.

Vasileios Kalogirou from EU-

SPA stressed how cross-border

collaborations can be strengthened

through such integrated

frameworks.

User needs across

operational contexts

A core part of the meeting was

dedicated to user needs identification,

drawing on field experiences

from Italy and France.

The Geological Service of Valle

d’Aosta in Italy presented its

work in monitoring landslides

and rockfalls. By leveraging

drones, GNSS, InSAR satellite

sensors, and stratospheric balloons,

the service conducts safe

inspections of unstable rock

faces and generates real-time 3D

models of hazardous sites. For

future operations, the service

expressed interest in robust data

transmission systems to guarantee

communication during

emergencies.

Colonel Stéphane Drenne of the

French Civil Security (UIISC5)

described the history and global

missions of the brigade, ranging

from wildfire management in

Corsica to deployments in Chile

and Greece. He underlined the


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importance of a clear chain of

command supported by reliable

communication systems.

The fire services of Corsica,

SIS 2A and SIS 2B, described

their specialized teams covering

mountain rescue, hazmat

response, drones, and canine

units. Their main challenges include

real-time event detection,

wildfire propagation modeling,

and integration of artificial

intelligence into command systems.

Satellite and aerial data

are increasingly seen as essential

to achieve these goals.

The Italian National Firefighters

Corps (CNVVF), with more

than 675,000 interventions

in 2024, stressed the need for

satellite-based situational awareness

in floods, earthquakes, and

large-scale fires. Rapid access to

Earth observation imagery and

geospatial platforms is vital for

planning and intervention.

The Civil Protection Centre

of the University of Florence

(CPC-UNFI) highlighted the

demand for integrated, userfriendly

platforms that merge

satellite, aerial, and in-situ data.

A live questionnaire confirmed

that secure communications,

drones, and AI-driven analysis

rank among the top technological

priorities for European

practitioners.

Innovation at work: projects

and solutions

Several ongoing projects showcased

how research and industry

are advancing emergency management

tools.

The Université de Corte presented

Project FEU, a multidisciplinary

effort to enhance

wildfire resilience, focusing on

firefighter safety, smoke analysis,

and fire-resistant construction

materials.

GTER introduced the I-EM

platform, a web-based solution

that integrates Copernicus data,

GNSS monitoring, and AI risk

analysis to optimize intervention

times and coordination.

The MIDGARD initiative

demonstrated AI-powered decision-support

systems to detect

hazards early and provide realtime

field imagery.

The European Union Satellite

Centre (SATCEN) described

how it provides geospatial intelligence

services to EU decisionmakers,

with strong emphasis

on timely data sharing across

stakeholders.

The EUSATfinder value

proposition

At the heart of the meeting was

the EUSATfinder project, which

aims to deliver secure, reliable,

and integrated communications

infrastructure for emergency

responders.

Its approach combines GEO

and LEO satellites such as

ATHENA FIDUS, Konnect

VHTS, Iridium Certus, and

Inmarsat SwiftBroadband with

terrestrial networks. This architecture

enables beyond visual

line of sight drone missions for

surveillance, search and rescue,

and disaster monitoring, supported

by satellite communications.

Mobile operational centers are

another key feature, designed

as deployable units equipped

with terminals and sensors to

provide immediate connectivity

and data access in the field.

The project also foresees mobile

applications allowing citizens in

distress to connect securely with

responders via satellite.

A comprehensive zero trust

security architecture is being

developed to protect the system.

This includes encryption of

data, intrusion detection, access

management with multi-factor

authentication, and systematic

vulnerability testing.

A preliminary SWOT analysis

confirmed strong advantages in

data integration and operational

reliability, while also highlighting

challenges such as training

needs, system costs, and market


REPORT

competition. Importantly, the survey conducted

during AB#1 revealed high demand

for flexible procurement models such as payper-use,

with budgets adapted to the scale

of missions. Respondents also expressed a

preference for remote support and training

to ensure rapid adoption across dispersed

operational units.

Conclusions and next steps

The first EUSATfinder Advisory Board created

a shared understanding between users,

developers, and policymakers. It reaffirmed

that the future of emergency management

in Europe will depend on the seamless integration

of secure satellite and terrestrial

communications, the combination of satellite

and drone imagery with ground data for

situational awareness, and dedicated multiplatform

tools enabling real-time information

exchange across teams.

The next milestone for the project will be

showcased at the Technology for All event in

Rome in November 2025, with further demonstrations

planned for 2027 in the Aosta

Valley.

EUSATfinder exemplifies how Europe can

leverage its strategic space assets to deliver

resilient, user-driven, and secure technologies

for first responders, ultimately saving

lives and protecting communities in times of

crisis.

KEYWORDS

EUSATFINDER; Emergency Response;

Satellite communication

ABSTRACT

The first EUSATfinder Advisory Board (AB#1) took place in Île-

Rousse, Corsica, on 1 July 2025, bringing together first responders,

institutions, and technology providers to discuss the role of space,

aerial, and terrestrial assets in crisis management. Central to the debate

was the integration of Europe’s flagship programs: GOVSAT-

COM for secure communications, Copernicus for Earth observation,

and Galileo for positioning and timing.

Operational insights from Italian and French agencies showed how

drones, satellites, and advanced monitoring systems are already

transforming disaster response, while also exposing needs for reliable

communication, rapid data sharing, and user-friendly platforms.

Industry and research partners presented innovative tools ranging

from AI-powered decision support to geospatial intelligence platforms.

At its core, the EUSATfinder project seeks to deliver secure, integrated

communications, enabling beyond visual line of sight drone

missions, mobile operational centers, and citizen applications. The

project will be further demonstrated in Rome in November 2025

and in the Aosta Valley in 2027.

AUTHOR

Marco Nisi

marco.nisi@grupposistematica.it

Venue, Municipality of Ile Rousse, Corsica (France)


REPORT

During the meeting, all participants completed a questionnaire designed to capture user

needs, challenges, and current use of technology to refine EUSATfinder’s market fit.

The survey focused on drone surveillance systems with satellite communication, exploring

value, mission types, procurement preferences, decision-making autonomy, desired features,

support expectations, budgets, and partnerships. Respondents highlighted the critical role of

satellite links for beyond visual line of sight (BVLOS) operations and real-time video feeds,

especially in emergencies.

Questionnaire Key Points and results:

Value of Satellite Communication

Satellite communication is considered highly valuable, with a

rating score of 4.4 out of 5

Preferred Mission Types for Drone Deployment High priority missions include hazardous area monitoring (78%),

search and rescue (67%), forest fire surveillance (56%), and earthquake

or flood response (56%).

Procurement Preferences

Autonomy in Procurement Decisions

Majority prefer pay-per-use models (67%) and purchasing/

owning systems (56%), with no interest in leasing.

44% have full autonomy, 33% engage in joint procurement

with national/regional bodies, and 22% have limited autonomy.

Important Features for Operations

Support and Maintenance Expectations

Budget Range

Real-time video feed (86%) is the most critical feature, followed by

night vision/infrared (56%) and satellite communication (56%).

Other notable features include AI-based object detection (33%),

longrange flight endurance (33%), and autonomous flight/autopatrol

(33%).

Strong preference for remote support with training (80%), with

minimal demand for full on-site support or outsourced contracts.

56% find budgets up to 100,000 Euros acceptable; 33% can

justify budgets exceeding 500,000 Euros.

Results show strong perceived value (4.4/5) for satellite-enabled surveillance, particularly for

hazardous area monitoring and search and rescue. Real-time video and night vision emerged

as top priorities for situational awareness, while AI detection and autonomous flight were rated

lower, likely due to maturity or operational focus.

Interestingly, pay-per-use and ownership models were favored over leasing, reflecting the need

for flexibility and budget alignment. High procurement autonomy supports quick adoption

but requires solid justification for larger investments.

Respondents also valued remote support

with training, pointing to scalable

maintenance needs, and emphasized

collaboration with private partners

and consortia to share expertise and

risks in pilot projects.

EUSATfinder is funded by the European

Union Agency for the Space Programme

(EUSPA) under the Horizon

Europe programme (Grant Agreement

No. 101180157).


REPORT

From Analyst to AI Orchestrator:

Evolving Roles in the Age of Autonomy

by Erin Eckles

The geospatial industry is entering

a new era of autonomy that is

being driven by AI workflows that

are reshaping how organizations

handle massive volumes of data.

Across industries, from national

security to utilities, infrastructure,

energy and environmental

monitoring, intelligent agents now

perform an ever-growing share of

routine tasks, from data triage to

full product creation, and they do

so at speeds humans simply can’t

Fig. 1 xxxxxxx

match.

Analysts are no longer

just interpreters of

imagery; they are

becoming orchestrators of

AI-driven workflows. This

machine-driven acceleration

delivers critical information

in seconds, rather than

hours, reducing operational

bottlenecks, and empowering

experts to tackle deeper

strategic problems rather

than wading through the

mountains of raw data.

Tomorrow’s professionals

will orchestrate these

AI workflows, supervise

autonomous systems, and

direct complex data pipelines

to deliver results with greater

speed, precision, and impact

than ever before.

The Challenge: A Data

Tsunami

Every sector is experiencing

growth in geospatial data:

• More sensors, more feeds:

Satellites, UAVs, lidar, SAR,

hyperspectral, video, and IoT

sensors/devices are producing

more raw data than any team

can manually review.

• More urgency: From

real-time disaster response

to grid resilience, decision

makers demand near realtime

actionable intelligence

with less tolerance for error or

delay.

• More complexity: Multisource,

multi-resolution data

requires consistent workflows

and expert oversight.

Traditional methods can’t

keep pace. Analysts risk being

overwhelmed by volume,

format conversions, and slow

manual reviews. That’s where

AI agents excel:

• Automating data triage

and first-pass analysis

• Identifying anomalies,

patterns, and trends in near

real-time

• Reducing the noise so

experts can focus on what

matters

Yet AI is only part of the

solution. The analyst remains

essential to ensure that AI

findings are accurate, credible,

and actionable.

The Evolving Role: From

Analyst to Orchestrator

As AI takes on repetitive tasks,


REPORT

analysts gain the freedom to

lead at the strategic level:

• Configuring Workflow:

Selecting the right data

sources, chaining AI models,

and adapting pipelines as

missions evolve.

• Applying Domain

Expertise: Handling

ambiguous or novel cases

where human judgment is

necessary.

• Directing Multi-INT

Fusion: Guiding how imagery,

spectral, signals, and opensource

data converge into one

coherent picture.

• Supervising AI Outputs:

Ensuring transparency and

validating machine results and

mission-appropriate results.

In this model, analysts and

engineers become conductors

of an AI orchestra. They set

the priorities, define what

matters, and scale their impact

by leveraging intelligent

automation.

The NV5 Advantage:

Analyst-Centered Autonomy

NV5 is building solutions

that empower experts to lead

in this new era. GeoAgent,

NV5’s flagship orchestration

environment, provides an AI

“command center” designed

to be flexible, transparent, and

human driven.

• Model Chaining and

Orchestration: Seamlessly

configure multi-stage AI/ML

pipelines across imagery, SAR,

spectral, lidar and more.

• Explainable AI and

Human-in-the-loop: Deliver

transparency, traceability, and

oversight for every AI output.

• Flexible Deployment:

Operate in the cloud, on

premises, or at the edge,

wherever the mission or

business demands.

• Interoperability: Support

for industry standards (like

ONNX) and integration with

tools such as ENVI® ensures

models trained anywhere can

run everywhere.

This approach guarantees that

analysts continue to be the

ultimate decision makers. AI

becomes a trusted teammate

that is fast, scalable, and fully

auditable.

AI for Every Industry

While these innovations grew

from defense and intelligence

needs, their value spans

industries.

• Infrastructure and

Utilities: Automating

vegetation management,

pipeline monitoring, and

post-storm assessments.

• Environmental

Monitoring: Detecting

deforestation, tracking flood

impacts, or managing wildfire

risk.

• Commercial and

Industrial: Supporting

mining, construction, and

transportation with site-level

insights.

• Government and Policy:


REPORT

Enabling regional planning,

resiliency programs, and

sustainable growth.

Wherever geospatial data

intersects with critical

decisions, AI orchestration

tools accelerate the path from

pixels to answers.

Elevate the Analysts.

Accelerate the Mission.

AI is transforming geospatial

intelligence, but not by

replacing the analyst. Instead,

it’s equipping them to lead.

Professionals who embrace

this evolution and learn

to orchestrate AI-driven

workflows will be the force

multiplier of tomorrow’s

projects and missions, driving

faster, smarter, and more

trusted outcomes.

For more information, visit

www.nv5.com/geospatial or

contact us at geospatialinfo@

NV5.com.

KEYWORDS

Artificial Intelligence; Geospatial

Data Analysis; Decision

Support Systems

ABSTRACT

The geospatial industry is undergoing

a transformation

driven by AI-powered workflows

that automate data triage,

analysis, and product creation.

Analysts are no longer just interpreters

of imagery; they

are becoming orchestrators of

AI-driven workflows. NV5’s

GeoAgent platform empowers

this shift with flexible, explainable,

and interoperable AI

tools. Across industries—from

national security to utilities,

infrastructure, energy and environmental

monitoring—AI

enhances decision-making, elevating

analysts, and accelerating

missions.

AUTHOR

Erin Eckles

NV5 Geospatial

erin.eckles@NV5.com


REPORT


REPORT

AI-Powered Data Informs Wildfire

Hazard Assessment in California

by Ada Perello

Climate change is intensifying

wildfire risks, with longer fire

seasons, severe droughts, and

vast areas burned in Europe

and California. In response,

geospatial technologies are

becoming essential tools for

effective fire management.

Credit: Ada Perello/EAASI

Wildfire threats have intensified

globally over the past decade,

with increasingly severe seasons

affecting not only traditional

fire-prone regions like California

and Australia, but also European

countries from Portugal to Greece,

and even northern territories

previously considered low-risk.

Climate change has

extended fire seasons,

increased drought

conditions, and created

unprecedented challenges

for emergency response

agencies worldwide. The 2023

European fire season alone

burned over 500,000 hectares,

while California continues

to face annual threats across

millions of acres of vulnerable

terrain.

Against this backdrop of

escalating wildfire risk,

innovative geospatial

technologies are proving

essential for modern fire

management strategies. The

California Department of

Forestry and Fire Protection

(CAL FIRE) has recently

selected EAASI observer

Ecopia AI to deliver

comprehensive, AI-generated

mapping data across the state's

Fire Hazard Severity Zones—a

project that exemplifies how

advanced geospatial solutions

are revolutionizing wildfire

preparedness.

Precision Mapping at

Unprecedented Scale

Ecopia AI, a leader in

artificial intelligencebased

mapping with over a

decade of experience, has

undertaken one of the most

comprehensive wildfire

risk mapping projects to

date. Using high-resolution

aerial imagery provided by

EAASI member Hexagon,

the company's advanced

AI systems have digitized

geospatial data across

California's Fire Hazard

Severity Zones, covering

more than 32 million acres—

approximately 30% of the

entire state.

The scope of data extraction

demonstrates the sophisticated

capabilities of modern

AI-powered geospatial

analysis. Ecopia's systems

have identified and mapped

buildings, roads, bridges,

driveways, parking areas,

sidewalks, pavement,

swimming pools, sports fields,

grass, shrubs, railways, open

water bodies, tree canopy, and

barelands. This comprehensive

dataset enables CAL FIRE to

analyze critical relationships

that influence fire behavior

and spread patterns.

Particularly significant

is the attribution of

essential proximity data to

each building footprint,

including distance to nearest

trees, water bodies, and

neighboring structures—

factors that directly impact

fire vulnerability and spread

potential. This level of

detailed analysis, delivered at

scale through AI processing,

would be practically

impossible using traditional

manual mapping techniques.

Supporting Multi-Phase

Fire Management

The Ecopia dataset addresses

multiple phases of wildfire

management, from prevention

through recovery. During

the preparedness phase, the


REPORT

Image 2: A sample of high-precision data extracted by Ecopia in Paradise, California. Credit: Ecopia AI

data enables sophisticated

hazard zoning, ensuring

that new construction and

redevelopment are accurately

classified within California's

fire hazard framework.

Post-fire applications prove

equally valuable, with

the detailed building and

infrastructure data supporting

rapid damage assessment and

recovery prioritization. The

ability to compare pre- and

post-fire conditions using

consistent, high-precision

datasets accelerates both

insurance processing and

reconstruction planning.

Data-Driven Future

of Fire Protection

While this project addresses

California's specific needs,

the methodologies and

technologies deployed have

clear applications for fireprone

regions worldwide.

European countries facing

increasing wildfire threats—

from Mediterranean nations

dealing with annual fire

seasons to northern European

countries confronting

unprecedented forest fires—

can benefit from similar

comprehensive mapping

approaches.

The scalability of AI-powered

mapping systems means that

the techniques proven in

California's 32 million-acre

project could be adapted for

pan-European applications,

supporting coordination

between national fire services

and enabling standardized

risk assessment methodologies

across borders.

This project demonstrates

how the geospatial industry

continues to provide

essential infrastructure for

public safety applications.

As wildfire threats intensify

globally, the combination

of advanced imagery

acquisition, AI-powered

analysis, and comprehensive

data integration offers hope

for more effective protection

of communities and critical

infrastructure.

The integration of Hexagon's

high-resolution aerial

imagery with Ecopia AI’s

processing capabilities

showcases the value of

collaborative approaches

within the geospatial industry.

Both companies, as EAASI

members, represent the type

of technological leadership

that advances the broader

crewed aerial surveying and

mapping sector.

KEYWORDS

wildfire; Ecopia AI; GeoAI; Aerial imagery

ABSTRACT

As climate change exacerbates wildfire risks

globally, innovative geospatial technologies are

becoming crucial for effective fire management.

This article discusses a groundbreaking project

by the California Department of Forestry and

Fire Protection (CAL FIRE) in collaboration

with Ecopia AI, which utilizes AI-generated

mapping data to assess wildfire hazards across

California's Fire Hazard Severity Zones. Covering

over 32 million acres, this comprehensive

dataset includes detailed information on

buildings, infrastructure, and environmental

features that influence fire behavior. The project

enhances wildfire preparedness, supports

rapid post-fire recovery, and offers a scalable

model for fire-prone regions worldwide. By

integrating high-resolution aerial imagery with

advanced AI analysis, this initiative exemplifies

the potential of geospatial solutions to improve

community safety and infrastructure resilience

in the face of escalating wildfire threats.

AUTHOR

Ada Perello

communication@eaasi.eu

EAASI


THE ITALIAN NATIONAL

AIRPHOTO ARCHIVE

A RANDOM AERIAL PHOTOGRAPH:

DATING A HISTORICAL IMAGE BY

CROSS-REFERENCING SOURCES

The Italian National AirPhoto

Archive tells...

by Gianluca Cantoro,

Giampiero Federici

The Italian National AirPhoto

Archive (Aerofototeca Nazionale

– ICCD, MiC) preserves one of

the richest collections of historical

aerial photographs in Europe.

Among the millions of images are

vertical views, oblique perspectives,

and photographs produced

by both military and civilian operators.

One particularly striking

picture depicts a jet flying low

over Verona. The image, though

visually impressive, is undated (as

it sometimes happens for several

reasons in historical archives),

which greatly reduces its historical

value. Without a precise

temporal frame, the photograph

risks remaining a beautiful curiosity

rather than a solid historical

source. The challenge, therefore,

is to reconstruct its date by carefully

cross‐referencing visual, historical,

and environmental clues.

Fig. 1 - Our “random” -oblique- photograph from the Italian Airforce with the caption “Verona

– L’Arena e Piazza Bra”. Indeed, the angle and clarity of the image allow for the identification

of several landmarks, including the Arena di Verona and Torre dei Lamberti. In the top right,

a silver airplane in flight. Italian National AirPhoto Archive (Aerofototeca Nazionale – ICCD,

MiC); image reference AM collection, unknown date, neg. 173146.

The Image and Its Context

The oblique black‐and‐white

photograph shows the city of Verona

with the Roman Arena in

clear view. A jet aircraft dominates

the composition, caught in flight

as it passes over the city. From

the perspective of the photo, it

appears that the aircraft approached

from the direction of Lake

Garda, swept across the Adige River,

and then climbed beyond the

urban area. Calculations based on

the likely camera and lens combination,

together with the size of

identifiable landmarks, suggest

that the photograph was taken

from an altitude of roughly 800

to 1000 meters (thus capturing

the jet at an even lower altitude).

Such a relatively low level offered

a dramatic view of both the aircraft

and the city. This reinforces

the interpretation that the purpose

of the image was less about documenting

construction or urban

fabric and more about celebrating

the aircraft itself, perhaps in connection

with an airshow, civic holiday,

or military demonstration

(fig.1).

The Airplane in the Scene

Closer inspection of the aircraft

reveals several unmistakable features:

arrow‐shaped swept wings,

two large underwing tanks, and

a distinctive tail configuration.



AIRPHOTO ARCHIVE

These details identify the jet as

a Republic RF‐84F Thunderflash.

This was a reconnaissance

variant of the F‐84F Thunderstreak,

developed in the United

States in the early 1950s. Unlike

its predecessor, the Thunderflash

was designed from the outset for

tactical reconnaissance. It replaced

the traditional nose intake

with a rounded nose housing

multiple camera systems, while

the air intakes were moved to

the wing roots. The aircraft was

capable of carrying up to six different

cameras for vertical, oblique,

and forward‐facing photography,

making it an essential

intelligence‐gathering tool in the

tense atmosphere of the Cold

War. With a top speed exceeding

1,100 kilometres per hour,

the RF‐84F combined speed and

precision, qualities that were vital

for low‐level reconnaissance missions.

For Italy, the arrival of this jet

represented a leap forward in

technology and in strategic posture.

Under the Mutual Defence

Assistance Program, Italy received

78 RF‐84F Thunderflashes

between 1955 and 1956. They

entered service with the 3rd Aerobrigata

at Villafranca, near Verona.

The presence of this aircraft

in Italian skies marked a decisive

transition from piston‐engine reconnaissance

planes to jet‐powered

machines aligned with

NATO standards. The jet captured

in the Verona photograph

carries the tail number 7397

and fuselage code 3‐38, both of

which provide essential clues for

narrowing down its date. Military

records confirm that these codes

changed over time, which allows

historians to bracket the period

when such markings were in use

(fig.2).

Fig. 2 - Detail of Figure 1 with the airplane with tail number 7397 and military (fuselage) code

3-38, captured during its flight over Verona (Northern Italy). The Jet Airplane with this coding

belonged to the 132nd Group RT of the 3rd AeroBrigade.

TOWARDS THE DATING OF

THE PHOTOGRAPH

The question of dating the photograph

can be approached from

several angles. First, we know

that aircraft number 7397 entered

Italian service in February

1956. This gives us the earliest

possible date for the image. Secondly,

we can compare the photograph

with other aerial and

ground images of Verona from

the same period. The Philharmonic

Theatre, for example, was

heavily damaged during the Second

World War. In the oblique

photograph it is still standing in

a ruined state. By 1958, however,

vertical photographs taken from

higher altitude show that the

ruins had been cleared. This immediately

places our photograph

before 1958 (fig.3). Another important

clue comes from Ponte

Pietra, the city’s Roman bridge

over the Adige. Destroyed by retreating

German troops in 1945,

it was provisionally replaced with

a metal structure. Reconstruction

of the stone bridge began in February

1957 and was completed

in 1959. In our photograph the

temporary structure is clearly visible

and no reconstruction work

seems to have started (fig.4). This

narrows the timeframe further to

the period between early 1956

and the beginning of 1957.

Environmental evidence adds

another layer. The river in the

photograph appears full, suggesting

springtime snowmelt from

the Alps. The trees are in full

foliage, outdoor cafés are set up,

and shadows are short, indicating

a time when the sun was high but

before the dryness of late summer.

Meteorological records for

1956 show peaks in river levels

in May and June, corresponding

closely with the visual evidence.

These elements together suggest

that the photograph was taken in

late spring or early summer 1956,

most likely around May or June

of that year.

Fig. 3 - Left, detail of the Philharmonic Theatre, bombed in 1945, behind the Museo Lapidario.

Note the chairs organized in a grid and white screen presumably for theatrical plays; Right,

detail of a vertical photograph dated 1958.



AIRPHOTO ARCHIVE

Fig. 4 - Detail of our undated photograph against another aerial photograph dated January

29th, 1957.

Thus, with all the above, we can

gradually narrow quite a lot the

dating, we can narrow quite a lot

the dating of our photo, arriving

to a time/span between February

1956 (date of arrival of the airplane

in Italy) and January 1957. If

we include also the environmental

considerations, we could consider

the historical data of precipitation

in Verona (ISPRA dataset)

in 1956 and the Adige River’s

water level at Verona for the same

year (Annali idrologici, Ufficio

idrografico del Magistrato alle

acque, Venezia. Parte 2 1 ) (fig.5).

These two charts give us some

clues about the rainiest months

of that year and the water level

per month against the annual

mean, and we can appreciate

the peaks in May-June and

October-November. If it was to

choose between one of these two

time-windows, probably the first

would look more realistic, for the

following reasons:

-The Adige seems still quite

full, and this suggests we’re likely

seeing the effects of spring

snowmelt from the Alps, which

typically peaks from late April

through June. By late July–August,

the river usually runs lower

going towards the late- summer

drought, unless there’s unusual

rainfall.

- No particular shadows (or very

short ones) can be spotted in the

photo, probably suggesting a sun

high in the sky but not yet at the

deep-summer drought stage.

- Trees present full and lush foliage,

and no autumn leaf drop or

bare branches seem to be present,

ruling out winter and late autumn.

- Our oblique photograph shows

sunshade and gazebos in the patio

and dehors of bars and cafes

in the Bra square and, as we

mentioned earlier, chairs are organized

in rows in the open space

of the (bombed) Philharmonic

theatre, suggesting that time passed

since the notorious cold of

February 1956 (temperature in

Verona was documented to be as

low as -18 o Celsius) and weather

is more compatible with outdoor

living.

Why Dating Matters & Methodological

Framing

Determining the date of a historical

aerial photograph is more

than an academic exercise. A precisely

dated image gains value as

a document for multiple disciplines.

In archaeology, as shown by

Scardozzi (2010), historical aerial

imagery has been fundamental

for recovering the outlines of

ancient landscapes in Italy and

Turkey. Carta (2018) has used

diachronic aerial photographs to

track landscape change on Elba

Island, providing a basis for tourism

management and conservation

policies. Other applications

(Cantoro 2017a and b; Cowley

& Stichelbaut 2012) has shown

how oblique aerial imagery can be

used to interpret difficult WWII

landscapes in southern Italy, and

how combined aerial and ground

surveys can provide digital documentation

of archaeological heritage.

These examples all highlight

how an undated photograph,

once properly placed in time, becomes

a reliable tool for historical,

environmental, and cultural

research.

Conclusions

By cross‐referencing details of the

aircraft, the cityscape of Verona,

and environmental evidence, the

undated oblique photograph can

be assigned with confidence, at

the best of the current knowledge,

to the late spring of 1956.

This seemingly simple flyover

captured far more than a jet above

a city: it documented the arrival

of a new era in Italian aviation,

the resilience of Verona’s urban

fabric after the war, and the

importance of dating in giving

archival materials renewed life.

What began as an anonymous

image becomes, through careful

interpretation, a window into a

transformative moment in Italy’s

postwar history.

While the aircraft’s physical presence

over Verona is documented

photographically, the experience

of operating the RF-84F is cap-

Fig. 5 - Rainfall data and Adige River water level in 1956.



AIRPHOTO ARCHIVE

tured in anecdotal records and

oral history. Though direct quotations

are rare, composite testimony

reflects the technical demands

and emotional resonance

of flying such missions. Navigating

rugged terrain at high speed

with minimal margin for error required

exceptional skill. Ground

crews, too, recall the RF-84F as

a machine that demanded precision:

a powerful yet sensitive jet

with little room for maintenance

error. The flyover of aircraft 3-38

/ 7397 over this historic city thus

represents a fusion of heritage

and forward-facing military posture.

In conclusion, the flight of the

RF-84F Thunderflash over Verona

offers more than a visually

arresting image; it provides a

window into a transformative

period in Italian military aviation.

Through the convergence of

photographic evidence, aircraft

history, operational documentation,

and pure photointerpretation,

this article highlights the

multifaceted role of the RF-84F

in the Aeronautica Militare and

cements its legacy as a symbol of

Cold War vigilance and technological

progress.

NOTES

1 https://archive.org/details/Annali_idro_VE-1956_P2 for 1956 and https://archive.org/details/

Annali_idro_VE-1957_P2 for 1957.

REFERENCES

Cantoro, G. (2017a). Reading a difficult landscape from the air: A methodological case‐study from a

WWII airfield in South Italy.

Cantoro, G. (2017b). Ground and aerial digital documentation of cultural heritage (ISPRS Archives).

Scardozzi, G. (2010). The contribution of historical aerial and satellite photos to archaeological and

geo‐archaeological research: Case studies in Italy and Turkey. Advances in Geosciences, 24, 111–123.

Carta, A. (2018). Diachronic analysis using aerial photographs across fifty years: Elba Island, Italy.

Tourism Management Perspectives.

Thunderstreaks.com. RF‐84Fs of the Italian Air Force.

Cowley, D.C., & Stichelbaut, B. (2012). Historic aerial photographic archives for European archaeology.

European Journal of Archaeology.

KEYWORDS

Aerial photography; Historical archives; RF-84F Thunderflash;

Verona (Italy); Photo interpretation.

AUTHOR

Gianluca Cantoro (ISPC-CNR, Consiglio Nazionale delle Ricerche),

gianluca.cantoro@cnr.it

Giampiero Federici (SARA Nistri), giampierofederici@yahoo.it

A. Dell’Anna edits the column "L'Aerofototeca Nazionale racconta..."

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Additional information is available at topconpositioning.com/solutions/agriculture/crop-care.

About Topcon Positioning Systems

Topcon Positioning Systems is an industry-leading designer,

manufacturer and distributor of precision measurement

and workflow solutions for the global construction,

geospatial and agriculture markets. Topcon

Positioning Systems is headquartered in Livermore,

California, U.S.

(topconpositioning.com, LinkedIn, X, Facebook, Ins

tagram). Its European head office is in Zoetermeer,

Netherlands. Topcon Corporation (topcon.com),

founded in 1932, is traded on the Tokyo Stock

Exchange (7732). Topcon Agriculture: (LinkedIn, X,

Facebook, Instagram)


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