New Challenges in the Implementation and Exploitation of a Low-Cost

Web Map of the Active Deformation Areas Across Europe

Jos

e A. Navarro

, Danielly Garc

ıa

and Michele Crosetto

Centre Tecnol

ogic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Carl Friedrich Gauss 7,

08860 Castelldefels, Spain

Keywords:

Active Deformation Areas, EGMS, Geoprocessing, Land Subsidence, Open-Source, WebGIS.

Abstract:

The European Ground Motion Service (EGMS) is already offering data on Persistent Scatterers (PS) through-

out Europe, which will aid in analyzing ground deformation on a continental scale. However, to more fully

comprehend ground motion processes, it is preferable to use Active Deformation Areas (ADA) instead of PS.

The CTTC has been using its ADAﬁnder tool to generate ADAs since 2018. With the new availability of

EGMS PS data for the entire continent, the CTTC is now working on producing ADAs for all of Europe and

making them accessible to the public through a self-developed, in-house hosted, web-based map application.

A former paper describes the initial steps taken to develop it. This work focuses on how the challenge of

processing a huge amount of data has affected the design and implementation of the tools used in the data

production workﬂow. Additionally, the manner in which the EGMS data is structured, providing data sets that

spatially overlap, has resulted in a new problem: overlapping ADAs. The approach to resolving this issue is

also discussed in this paper. The result is an evolution of the initial concept where not only economic reasons

but also considerations on automation and handling of large volumes of data have guided the design and im-

plementation of the system.

1 INTRODUCTION

The European Ground Motion Service (EGMS) (Eu-

ropean Environment Agency, 2021) has already pro-

vided billions of Point Scatterers (PS) data for the

whole of Europe (Crosetto et al., 2020), which will

be a valuable tool for analyzing ground deformation

processes at a continental level. However, Active De-

formation Areas (ADA) are a more effective means

of understanding these processes as they depict areas

where actual deformation is occurring, which reduces

the amount of information to analyze. Figure 1 illus-

trates an area covered by several ADAs, with colors

indicating up to four levels of certainty of the ﬁnd-

ings.

(Barra et al., 2017) describe the methodology for

identifying ADAs, which involve creating a poly-

gon that encircles the affected area, and computing

the aforesaid level of certainty. This helps experts

to focus their efforts on areas that actually require

their expertise. The CTTC developed in 2018 a tool,

https://orcid.org/0000-0001-7877-1516

https://orcid.org/0000-0002-8191-3308

https://orcid.org/0000-0001-8545-5490

Figure 1: ADAs and their reliability. The white bullets in-

side the ADAs are the PS used to build them.

ADAﬁnder, that implements this methodology and

has been in use since. (Tom

as et al., 2019; Navarro

et al., 2020) provide more detailed description of

ADAﬁnder and other related tools.

As part of the SARAI project, funded by the Span-

ish Ministry of Science and Innovation (MCIN), the

CTTC is working on publishing ADAs online for the

entire European continent by creating a web map ap-

plication speciﬁcally for this purpose. This section

brieﬂy reviews the technologies used to devise and

implement not only the web map application but also

the several software components targeted at produc-

192

Navarro, J., García, D. and Crosetto, M.

New Challenges in the Implementation and Exploitation of a Low-Cost Web Map of the Active Deformation Areas Across Europe.

DOI: 10.5220/0011964500003473

In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2023), pages 192-199

ISBN: 978-989-758-649-1; ISSN: 2184-500X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

ing the data to be published—that is, to transform

the PS delivered by the EGMS into ADAs stored

in a database accessed by the said web application,

namely the data production pipeline and workﬂow.

This review is provided for the sake of completeness,

since the interested reader may ﬁnd a much more de-

tailed description in (Navarro et al., 2022). Note,

however, that the web application has evolved since

the publication of the aforementioned paper, so any

relevant changes will be discussed where appropriate.

Why Not the Cloud? The initial problem to be

solved was determining where the information about

ADAs and the web map application would be stored.

The initial plan of using the cloud was rejected due

to the high cost it incurred (thousands of euros per

year). Therefore, an in-house solution was chosen.

An existing server was repurposed to host both the

data and the applications.

Server Hardware. The repurposed server had 32

GB of RAM, an Intel(R) Core(TM) i7-4790 CPU @

3.60GHz / 8 cores processor, and a 7 Tb RAID 1

SATA disk system.

Server Software. Keeping the Cost Low. The

required software modules to set up the web map ap-

plication were chosen from the open-source and free

market. These included:

• PostgreSQL (The PostgreSQL Global Devel-

opment Group, 2021) plus PostGIS (PostGIS

Project Steering Committee, 2021) for storing

geospatial data - the ADAs.

• GeoServer - a web-based tool for publishing

geospatial data, which retrieves information from

a database such as PostgreSQL and makes

it available via standard web-based protocols.

See (OSGEO, 2021).

• Apache (The Apache Software Foundation,

2021b) + Tomcat (The Apache Software Foun-

dation, 2021a) are httpd and Java servers respec-

tively - to run both GeoServer and the web map

application.

• Finally, the web map is a self-developed tool pro-

grammed in JavaScript and HTML. It relies di-

rectly on GeoServer to retrieve the ADA data it

displays.

Producing the ADAs. The ADA are generated

through a software pipeline that includes:

• ADAﬁnder - a self-developed tool that has been

in use since 2018, which takes the PS delivered

by the EGMS as input to produce the ADAs.

• ADA2PGIS - a tool speciﬁcally created for this

project, it takes the ADAs created by ADAﬁnder

and produces a series of SQL-compliant ﬁles,

Figure 2: The original data production workﬂow.

Source: (Navarro et al., 2022).

which include the same information but in a for-

mat that can be used to interact with the database.

• psql - a command-line tool that is included with

PostgreSQL, used to execute the ﬁles provided

by ADA2PGIS and insert the ADAs into the

database.

This workﬂow, oriented at processing a single EGMS

data set at a time, is depicted by Figure 2. Note that

once a data set has been processed as described it is

inserted into the database, which makes it immedi-

ately accessible via the web application.

The Web Map Application. It has been devel-

oped using JavaScript and HTML and it is aimed at

the visualization of the stored ADAs. It may zoom,

pan, and ﬁlter data by combining up to three criteria

such as the mean velocity of the ADAs or their quality

assessment value. Different background layers, such

as OpenStreetMaps, Google Satellite, or Google Ter-

rain can be chosen. When an ADA is clicked on, in-

formation about its attributes, including its averaged

deformation time series, is displayed. Figure 3 shows

the web application, depicting an ADA, its attribute

table and time deformation series. The legend, ﬁlters

and other controls as well as the attribute data table

are located at the right side of the image, while the

map itself is shown on the left. The original version

of the application showed the time deformation series

as a numeric table; the current one, as seen in the said

ﬁgure, already presents an interactive chart. Addition-

ally, the current version may tell apart ADAs originat-

ing from either ascending or descending orbits.

From this point, and once the EGMS data were

available, it became clear that the next goal to accom-

New Challenges in the Implementation and Exploitation of a Low-Cost Web Map of the Active Deformation Areas Across Europe

193

Figure 3: The web map application.

plish was to build a system capable of dealing with the

processing and management of very large volumes of

data.

The rest of this document is organized as follows:

section 2 discusses the new challenges that had to be

faced and the solutions adopted to address them once

that the initial system moved from a preparatory to a

production stage; more speciﬁcally, it highlights the

need to automate the production workﬂow to manage

huge volumes of data as well as the problem of over-

lapping data sets producing repeated ADAs. Finally,

to conclude, section 3 analyzes how the original sys-

tem needed to evolve from a design standpoint where

economical reasons prevailed to a real-life situation

where the volume of data to handle became the actual

driver of the process.

2 THE NEW CHALLENGES

Since this project started long before the EGMS made

the PS data generally available, it was possible to de-

sign and implement both the web application and the

data pipeline tools taking into account not only the

purely algorithmic aspects but also those related to

the volume of information to handle; although there

was no indication about how big the ﬁnal number of

PS would be, it was quite clear that it would be big

enough as to take it into account as a design and im-

plementation principle for the software to develop.

ADAﬁnder, the application identifying the ADAs

already existed and had been designed taking the

problem of data volume in mind. In fact, it is usual

to process input ﬁles with as many as ﬁve million PS

without problems. The same idea was kept in mind

when implementing ADA2PGIS and, of course, the

web application itself. In this last case, two aspects

were considered: (1) the storage required to keep the

high volume of ADAs obtained and (2) the perfor-

mance of the server that should host the web appli-

cation. As stated in section 1, a RAID system was

installed in the server which was big enough to hold

millions of ADA data; to check the performance of

the system, hundreds of thousands of synthetic ADAs

were inserted into the PostGIS database and the web

application was able to display them without prob-

lems.

The preceding paragraphs aim to show that con-

siderations for handling a signiﬁcant amount of data

were taken into account from the very beginning.

Nonetheless, and once that data from the EGMS be-

came available, it was evident that not all the facets

of the problem had been foreseen. These were mainly

two: (1) the need to take into account the automa-

tion of some processes due to the aforesaid overabun-

dance of data and (2) the problems created by the

way in which said data is organized, creating areas

where information overlaps thus leading to repeated

outputs. The following subsections elaborate on these

new challenges and how they have been addressed.

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

194

2.1 Simplifying the Operational Aspects

The workﬂow depicted in Figure 2 explains how the

PS delivered by the EGMS are converted into data

stored in the server database. This workﬂow is fully

operational and produces correct results. However, it

was conceived to process a single data set at once.

A data set, in this context, is the minimum item

that may be downloaded from the EGMS web site.

It deﬁnes, consequently, the granularity of the opera-

tions; in other words, since the workﬂow in Figure 2

is conceived to process one of these data sets, to ﬁn-

ish the project, every single available data set must be

processed.

Considering that there are about 15,000 data sets

and all of them need to be processed to complete the

project, it should be clear that a lot of human inter-

vention is required. Such a situation implies that the

total time needed to process all the data will be signif-

icantly increased compared to a more automated pro-

cedure. Furthermore, the greater the human interven-

tion involved in the process, the greater the probabil-

ity of errors. Therefore, it seemed clear to the devel-

opment team that the already implemented workﬂow

needed to be improved to simplify the operational as-

pects of the process.

Automation, however, is conditioned by a tough

prerequisite: uniformity. The ADAﬁnder step, that is,

identifying ADAs using the PS as input, is a task that

requires adjusting a series of parameters that usually

depend on the input data (as, for instance, the size of

the pixel used in the input imagery, see (Barra et al.,

2017)). On the other hand, the ADAs published on

the web, should be obtained using a set of identical

parameters, since, otherwise, the results for differ-

ent geographical areas could not be compared. This

makes it possible to go for a solution where the level

of automation is much higher. The handicap is to ﬁnd

a reasonable set of suitable set of parameters to run

ADAﬁnder for all Europe. This is a task currently un-

dergoing.

At any rate, and since automation is not only de-

sirable but possible, a new set of tools have already

been implemented to make it possible. These are:

• A script that makes possible to run ADAﬁnder for

all the data sets (EGMS data) stored in a single

directory. The same set of parameters will be used

for every data set. All results are stored in the

same output folder.

• A new application, automateADA2PGIS, will run

ADA2PGIS—the step that transforms the output

of ADAﬁnder into something suitable to be in-

serted in the server’s database—for all the input

ﬁles (ADAﬁnder-compatible ones) found in the

same directory. Again, all its outputs will be

stored in an unique folder.

Each time that ADA2PGIS is run, a set of out-

put ﬁles is created; some of them contain SQL (Stan-

dard Query Language) commands and the last one is

a batch (shell) ﬁle that will take care of actually per-

forming the process of inserting the ADAs included

in these SQL ﬁles into the database. Since auto-

mateADA2PGIS will run ADA2PGIS for all the in-

puts found in some directory, there will be an equiv-

alent number of outputs. This means that if the num-

ber of input data sets is n, also n batch (shell) ﬁles

will be created, each of them taking care of inserting

their respective ADAs. This number n is, in fact, the

total number of items that may be downloaded from

the EGMS, which amounts to about 15,000. Conse-

quently, the operator should run, by hand, all these

shell ﬁles to complete the process, which is, again, a

lengthy and tedious procedure, prone to human errors.

To avoid this situation, automateADA2PGIS also cre-

ate a so-called ”master batch ﬁle” that takes care of

running each of the individual batch ﬁles for every

data set. This completes the automation of the pro-

cess.

To summarize, the automation of the data produc-

tion pipeline could be (but it is not, see section 2.3)

the following one:

1. Download the whole EGMS data sets. Store these

in the same directory, namely the EGMS folder.

2. Run the ADAﬁnder automated script, taking as in-

put all the ﬁles stored in the EGMS folder. All

outputs are stored in the same output directory,

namely the ADAﬁnder folder. This step identiﬁes

the ADAs.

3. Run the automateADA2PGIS tool, taking as in-

put the ﬁles stored in the ADAﬁnder folder. All

outputs are stored in the same output directory,

namely the automateADA2PGIS folder. This

step transforms the whole set of ADAs into a

format that may be used to insert them into a

database (ﬁles with SQL commands). A mas-

ter batch ﬁle is generated, controlling the execu-

tion of the individual batch ﬁles output by each

run of ADA2PGIS—which is executed by auto-

mateADA2PGIS.

4. Run the master batch ﬁle. This step will insert the

whole set of ADAs into the server’s database.

Note that the workﬂow above ﬁrst downloads all

the data sets from the EGMS (step 1) to process them

later at once (steps 2 to 4) thanks to the high level of

automation achieved with the script to run ADAﬁnder

and the automateADA2PGIS tool. Of course, it would

New Challenges in the Implementation and Exploitation of a Low-Cost Web Map of the Active Deformation Areas Across Europe

195

Figure 4: Overlapping EGMS data sets (northeast of Spain).

be possible to proceed downloading smaller groups of

EGMS data sets, fully process them, and start again

until the project is ﬁnished. The reason to adopt this

approach (process everything at once at every step) is

due to some restrictions set by a new problem: the

overlapping of ADAs. Section 2.2 describes this is-

sue, how it has been solved and how it affects the

overall production workﬂow.

2.2 Removing Overlapping ADAs

The EGMS has divided the surface of Europe into

tiles; each of these tiles is the minimum unit of down-

load and, therefore, of processing within the work-

ﬂow. As already mentioned, about 15,000 tiles have

been necessary to cover the entire territory for which

PS data is available.

These tiles overlap with their neighbors. This phe-

nomenon can be observed in Figure 4. This is a prob-

lem from the point of view of data processing, since

duplicate information is generated.

The ADAﬁnder application is the one affected by

this information overlapping problem. This tool is

used to identify the ADAs of a certain area of the

terrain (the tile) using the PS existing in that area.

When run with some tile as input, it will produce

ADAs located in areas shared with other tiles. When

ADAﬁnder processes any of these other tiles that are

adjacent to the ﬁrst one, it is very likely that it will

ﬁnd new ADAs in the common, overlapped zones.

Some of these new ADAs are not really new,

but the same ones that had been detected previously.

Their shape or extent may vary slightly as the number

of PS included in the overlapping zones of adjacent

tiles may change, but ultimately they are still the same

ADAs.

Figure 5 depicts this problem. More speciﬁcally,

Figure 5(a) shows ADAs in an overlapping area that

were created from PS in two different tiles, namely a

and b. Purple ADAs were identiﬁed when processing

tile a and the orange ones come from tile b. Here it

Figure 5: Removing overlapping ADAs.

can be seen how the ADAs of tile a overlap with those

that come from the processing of tile b. Their shapes,

as already mentioned, are not identical due to the total

number of points available in each data set, but it is

clear that the ADAs in question represent the same

area and that it does not make sense to store them all

in the server’s database.

This was the reason to develop a new tool,

purge overlaps: removing overlapping ADAs.

This tool had to solve two different problems:

1. Decide, and implement, which are the ADAs to

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

196

remove when overlapping exists and

2. actually remove overlaps for all Europe at once.

Concerning the ﬁrst problem above, the decision

taken was to remove the smaller ADAs. That is, when

two or more ADAs overlap, only the one whose area

is the largest of all is preserved. With this approxi-

mation, it is intended to keep the ADA that most re-

liably represents how the terrain has been deformed.

Obviously, some information may be lost, as smaller

ADAs whose surface include zones not covered by the

one being retained may be removed. In the example

shown in Figure 5(a) two smaller purple ADAs over-

lap with the bigger orange one, which is the one that

is preserved (see Figure 5(b)). The same is true for

the single smallest purple ADA that overlaps with an-

other orange one at the top of the ﬁgure: it is removed

in favor of the one with the largest area.

The criterion used to decide if two ADAs overlap

is the intersection ratio, that is, the area of the inter-

secting zone divided by the area of the ADA itself.

This is a parameter whose value may (and must) be

set by the user. For example, if it is set to x, a couple

of ADAs for which all their overlapping areas have

an intersection percentage lower than x will be con-

sidered as non overlapping, while if this x is exceeded

by any of them the decision is just the opposite: they

overlap.

The second problem, having to remove all the

overlapping ADAs for all Europe at once is due, also,

to the overlapping of the tiles (see again Figure 4).

The reason is that although it would be possible to

check the overlaps stepwise, that is, checking these

for only a couple of tiles at a time, this would signif-

icantly complicate the management of the process. A

tile t overlaps with a number of other tiles, and this is

recursive situation: that is, all the tiles that overlapped

with t also overlaps with other tiles that are not t, and

so on. This means that the number of tile overlaps

to manage is very high. Consequently, purging over-

laps by tile couples would become a long and tedious

process, highly prone to human error due to the large

number of pairs to process and the management over-

head required to ensure no duplicate work is done.

This is, therefore, a clear candidate for task au-

tomation. The new tool purge overlaps has been de-

veloped to solve this problem. It takes the whole set

of outputs of ADAﬁnder for all Europe as inputs to

perform the purge of overlapping ADAs. The initial

estimate of the total number of ADAs to be processed

places this number below 1,000,000, which is a very

reasonable amount of data to manage by a computer

program, so this should not pose an implementation

problem. On the contrary, the process of ﬁnding what

are the couples of ADAs that do intersect, computing

their areas and the intersection between overlapping

candidates might imply a costly computational burden

if an inappropriate algorithm would be used. Fortu-

nately, computer science offers techniques to success-

fully address this problem, techniques that have been

adopted by purge overlaps, so that its performance is

satisfactory.

2.3 The New Workﬂow

Although purge overlaps solves the problem of the

overlapping ADAs, the use of this program imposes

a restriction on the overall production workﬂow: it

is not possible to fully process just a subset of tiles

(i.e. going from the download to the insertion of the

ADAs in the server database) and repeat these steps

with some other subsets until all the data from the

EGMS has been processed. This is so due to the need

to purge overlapping ADAs at once, that is, only when

the ADAs for all Europe are ﬁnally available.

Taking into account this restriction, the new, ﬁnal

workﬂow is as described below:

1. Download the PS from the EGMS.

2. Identify the ADAs using the automated script to

run ADAﬁnder

The two previous steps may be executed repeat-

edly, combining them to download some data and

then identifying the ADAs for a certain number of

data sets. That is, it is not strictly necessary to wait

until all Europe has been downloaded to proceed to

identify their ADAs at once. At any rate, and no mat-

ter how these two steps are combined, all ADAs must

have been processed before proceeding to step 3 due

to the requirements set by purge overlaps (see sec-

tion 2.2), that is, all ADAs must exist before this tool

is run. Consequently, the conversion of ADAs to SQL

format is performed too in a single automated step

(number 4):

3. Purge the overlapping ADAs for all the data sets

covering Europe at once.

4. Convert all remaining ADAs (those not purged

at the previous step) to SQL format using auto-

mateADA2PGIS. This tool takes care of running

the original ADA2PGIS tool for all the data sets

involved in the process.

5. Run the master batch ﬁle created by auto-

mateADA2PGIS, which takes care of performing

the actual insertion of all data into the database.

This step makes the information immediately

available to the web application.

Figure 6 depicts graphically the workﬂow above.

New Challenges in the Implementation and Exploitation of a Low-Cost Web Map of the Active Deformation Areas Across Europe

197

Figure 6: The new workﬂow.

3 CONCLUSIONS

In (Navarro et al., 2022) the authors presented a ﬁrst

attempt to implement a server capable of publishing

the Active Deformation Areas for the whole of Eu-

rope; in that work, the main aspect taken into account

were the economical restrictions that prevented using

the regular solutions available for this kind of prob-

lems, such as hosting both data and software in the

cloud. It is worth noting that the issues related to the

data processing pipeline and the volume of informa-

tion to be handled were already considered; the new

software developed for the project (i.e., ADA2PGIS)

was designed and implemented keeping in mind the

need to automate a production workﬂow and how

much information needed to be managed; addition-

ally, the web server was conﬁgured to be able to store

and display millions of ADAs. The performance of

both the server and the software were tested with syn-

thetic data to ensure that processing and publishing

the ADAs from across Europe was possible using a

low-cost solution. In short, this was the goal of that

work: to show the feasibility of such a system.

It may be said, therefore, that the authors consid-

ered, from the very beginning, not only the technical

aspects of a project of this type but also those issues

related to real-life aspects that only show up when the

actual exploitation of the system begins. The authors,

therefore, felt quite comfortable thinking that every-

thing was ready to start the identiﬁcation and publish-

ing of ADAs.

Data from the EGMS was ﬁnally available by the

beginning of November, 2022. This event was the

trigger to switch from a test environment to a pro-

duction one. As soon as the actual magnitude of the

task (the number of data sets to download and process,

their spatial distribution with overlapping tiles) was ﬁ-

nally known, the problems described in section 2 were

made evident; the development of the scripts to auto-

mate ADAﬁnder or the automateADA2PGIS tool im-

mediately began, and the new workﬂow, taking into

account such changes, was designed.

At the moment of writing this paper, the person-

nel responsible for the exploitation of the system is

still downloading the complete data sets of PS from

the EGMS servers—in other words, it has been de-

cided to execute steps 1 and 2 in the workﬂow de-

scribed in section 2.3 at once: ﬁrst, everything is

downloaded, then ADAs from across Europe are iden-

tiﬁed to then proceed to the remaining steps in the

workﬂow. Therefore, and since the exploitation of

the system is still at a very early stage, no other is-

sues have arisen yet; the authors consider this possi-

bility as remote, but it cannot, however, be discarded.

Thus, and concerning future work, the foreseen activ-

ities are (1) those strictly related to the pure produc-

tion tasks as described in section 2.3 and Figure 6 so

all the ADAs of all Europe are made available to the

general public; then, (2) to cope with any problems

that the software applications might experience due to

the high volumes of data to process, since, as stated

above, said problems may not be yet discarded and

(3), assuming the success of this project and building

on the experience gained, take a few more steps fur-

ther to create a new web application to show, again

at the continental level and using the same EGMS

data, wide-area differential deformation maps indicat-

ing the gradient of the deformation ﬁeld—in this case,

the basic software to produce such results is still un-

der development.

To ﬁnish, it is worth to state that this paper is tar-

geted at two goals; the ﬁrst one, describing the low-

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

198

cost system that the CTTC has developed to provide

public ADA data at a continental level; the second,

but no less important aim, is highlighting something

that is generally forgotten or at least overlooked when

systems similar to the one described in (Navarro et al.,

2022) and in this very same work are designed, devel-

oped and put into operation: that not only the algo-

rithms, software or hardware are important, but also

that the operational aspects related with their routine

exploitation or the characteristics of the data to handle

do play a very important role in how these are created

and later on, exploited. Even worse, not considering

aspects such as automation or the reduction of human

intervention may very well increase the risk of failure

for these projects.

The authors foresee that the web map will be made

available to the general public during the ﬁrst half of

2023.

ACKNOWLEDGEMENTS

This work is part of the Spanish Grant SARAI,

PID2020-116540RB-C21, funded by MCIN/AEI/

10.13039/501100011033.

REFERENCES

Barra, A., Solari, L., B

ejar-Pizarro, M., Monserrat, O.,

Bianchini, S., Herrera, G., Crosetto, M., Sarro, R.,

Gonz

alez-Alonso, E., Mateos, R. M., Lig

uerzana, S.,

opez, C., and Moretti, S. (2017). A Methodology to

Detect and Update Active Deformation Areas Based

on Sentinel-1 SAR Images. Remote Sensing,, 9(10).

Crosetto, M., Solari, L., Mr

oz, M., Balasis-Levinsen, J.,

Casagli, N., Frei, M., Oyen, A., Moldestadk, D. A.,

Bateson, L., Guerrieri, L., Comerci, V., and Andersen,

H. S. (2020). The Evolution of Wide-Area DInSAR:

From Regional and National Services to the European

Ground Motion Service. Remote Sensing, 12:2043.

European Environment Agency (2021). European Ground

Motion Service – Copernicus Land Monitoring

Service. https://land.copernicus.eu/pan-european/

european-ground-motion-service. Accessed: 2023-

01-11.

OSGEO (2021). GeoServer. http://geoserver.org/. Ac-

cessed: 2023-01-11.

Navarro, J. A., Garc

ıa, D., and Crosetto, M. (2022). An

in-house, self-developed low-cost WebGIS relying on

open-source and self-made tools to show the active

deformation areas across europe. In Proceedings of

the 8th International Conference on Geographical In-

formation Systems Theory, Applications and Manage-

ment - Volume 1: GISTAM,, pages 99–106. INSTICC,

SciTePress.

Navarro, J. A., Tom

as, R., Barra, A., Pag

an, J. I., Reyes-

Carmona, C., Solari, L., Vinielles, J. L., Falco, S., and

Crosetto”, M. (2020). ADAtools: Automatic Detec-

tion and Classiﬁcation of Active Deformation Areas

from PSI Displacement Maps. ISPRS Int. J. Geo-Inf.,

9:584.

PostGIS Project Steering Committee (2021). PostGIS

— Spatial and Geographic Objects for postgreSQL.

https://postgis.net/. Accessed: 2023-01-11.

The Apache Software Foundation (2021a). Apache

Tomcat

– Welcome! http://tomcat.apache.org/. Ac-

cessed: 2023-01-11.

The Apache Software Foundation (2021b). Welcome!

– The Apache HTTP Server Project. https://httpd.

apache.org/. Accessed: 2023-01-11.

The PostgreSQL Global Development Group (2021). Post-

greSQL: The world’s most advanced open source

database. https://www.postgresql.org/. Accessed:

2023-01-11.

Tom

as, R., Pag

an, J. I., Navarro, J. A., Cano, M., Pastor,

J. L., Riquelme, A., Cuevas-Gonz

alez, M., Crosetto,

M., Barra, A., Monserrat, O., L

opez-S

anchez, J. M.,

Ram

on, A., Iborra, S., del Soldato, M., Solari, L.,

Bianchini, S., Raspini, F., Novali, F., Ferreti, A.,

Constantini, M., Trillo, F., Herrera, G., and Casagli,

N. (2019). Semi-Automatic Identiﬁcation and Pre-

Screening of Geological-Geotechnical Deformational

Processes Using Persistent Scatterer Interferometry

Datasets. Remote Sensing, 11(14).

New Challenges in the Implementation and Exploitation of a Low-Cost Web Map of the Active Deformation Areas Across Europe

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