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Citation: Soriano-Cuesta, C.;
Romero-Hernández, R.; Mascort-
Albea, E.J.; Kada, M.; Fuls, A.;
Jaramillo-Morilla, A. Evaluation of
Open Geotechnical Knowledge in
Urban Environments for 3D
Modelling of the City of Seville
(Spain). Remote Sens. 2024,16, 141.
https://doi.org/10.3390/rs16010141
Academic Editor: Sara Gonizzi
Barsant
Received: 27 September 2023
Revised: 22 December 2023
Accepted: 26 December 2023
Published: 28 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
remote sensing
Article
Evaluation of Open Geotechnical Knowledge in Urban
Environments for 3D Modelling of the City of Seville (Spain)
Cristina Soriano-Cuesta 1, Rocío Romero-Hernández 1, Emilio J. Mascort-Albea 1,* , Martin Kada 2,
Andreas Fuls 2and Antonio Jaramillo-Morilla 1
1Departamento de Estructuras de Edificación e Ingeniería del Terreno, Escuela Técnica Superior de
Arquitectura, Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla,
41012 Sevilla, Spain; [email protected] (C.S.-C.); rocior[email protected] (R.R.-H.); [email protected] (A.J.-M.)
2
Institut für Geodäsie und Geoinformationstechnik, Methodik der Geoinformationstechnik, Fakultät VI Planen
Bauen Umwelt, Technische Universität Berlin, 10553 Berlin, Germany; [email protected] (M.K.);
[email protected] (A.F.)
*Correspondence: [email protected]
Abstract:
The need for sustainable urban growth management and preventive conservation of
built elements constitute the key factors in today’s increasing demand for the better understanding
of subsoil. This information, mainly available from geotechnical surveys, can be integrated into
spatial databases to produce operational models. Aiming to generate strategies that enable the
visualisation of underground properties in highly anthropised environments, the following four-
phase methodology has been proposed: (a) Gathering of geotechnical data; (b) Spatial and statistical
analysis; (c) Database design; (d) Generation of 2D and 3D models. Following the aforementioned
criteria and using open sources, a spatial dataset of 650 points located within the historical centre of
Seville (Spain) has been developed. This urban area is characterised by the heterogeneous distribution
of its soil layers and their geotechnical properties. The results show that the application of this
method enables a prompt and efficient display of the distribution of geotechnical layers in urban
and metropolitan environments, by considering the variations in their mechanical properties. This
simplified approach therefore establishes a new starting point for the development of predictive
strategies based on approaches of a more complex nature that facilitate the analysis of the interactions
between subsoil, buildings, and infrastructures.
Keywords: microzoning; urban geotechnical maps; Geographic Information Systems (GIS); LIDAR;
Digital Soil Modelling
1. Introduction
Due to the current circumstances of uncontrolled urban growth and climatic emer-
gency [
1
,
2
], the demand for a more accurate understanding of underground reality in
urban environments has become a major challenge [
3
]. Not only does this challenge re-
quire the provision of useful tools for the design of new constructions, but it also needs to
focus on certain major issues such as sustainability, metropolitan planning, and preventive
conservation of built heritage.
From the perspective of sustainable land use management in urban environments, the
potential of underground knowledge has yet to be exploited. This problem displays itself
both on the architectural scale through the use of geothermal energies [
4
], and also in more
extensive situations through the creation of Urban Underground Spaces (UUS) that allow
the infrastructural use of subsoil [
5
]. Similarly, an in-depth understanding of this reality
is essential for the design of new growth in favourable environments while considering
environmental and anthropogenic hazards [
4
] such as earthquakes [
6
], landslides [
7
], and
subsidence [
8
]. Furthermore, this information contributes to the support of preventive
conservation strategies that take into account the effects of climate change [
9
]. Nevertheless,
Remote Sens. 2024,16, 141. https://doi.org/10.3390/rs16010141 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2024,16, 141 2 of 21
there are currently no global digital models of cities that enable a comprehensive analysis
of both the subsoil and the urban environment.
For a better understanding of the situation underground, different methodologies are
currently helping to generate new ways of visualisation through Digital Soil Modelling.
Consequently, the concept of zoning is a key in this regard, as it is of great importance
to know the level of accuracy of the available information and the extent of the selected
working area [
10
]. The macrozoning concept applied to soil characterisation is based on
the determination of basic geological and geotechnical units, which are essential for the
operation of territorial approaches [
11
]. Interesting results have been achieved with these
strategies through the use of information from satellites [
12
], Light Detection And Ranging
(LIDAR), remote sensors [
13
], and Unmanned Aerial Vehicle (UAV) photogrammetric
flights [
14
]. Nevertheless, such techniques are based on the determination of natural land
cover and are not as effective in highly anthropised environments.
Through microzonation strategies, a more accurate underground assessment can
be obtained. This level of detail enables not only a greater number of parameters and
properties to be determined, but also the variation of their value ranges. In this case,
the information usually comes from the use of geotechnical surveys that have a smaller
territorial scope. These surveys include the use of linear inspections using specialised
equipment such as Ground Penetrating Radar [
15
], punctual information from seismic
acceleration records [
16
], in situ resistance tests [
17
], and data from samples collected in
pits and boreholes [
18
]. This type of approach is even more necessary in urban areas, where
the complexity of the layers of existing land information is very high [
19
]. However, the
main problem resides in the need for a large number of surveys to reach urban extensions,
the heterogeneity of the data collected, and the need for interpolation methods to complete
the gaps in information.
Geostatistical Interpolation and Digital Soil Modelling
Among the various interpolation methods provided by traditional geostatistics, the
Kriging method is one of the main references in the field of research. From the Ordinary
Kriging (OK) formulation, numerous variants have been derived, including Simple Kriging
(SK), Universal Kriging (UK), Fixed Rank Kriging, and Inverse Distance Weight (IDW) [
20
].
Consequently, a review of the scientific literature allows us to state that the OK, SK, and
IDW methods have been the most commonly used for the development of interpolation
related to the configuration of subsurface models [4,7,21,22].
In recent years, the development of new algorithms based on the use of Artificial
Intelligence (AI) has triggered a new evolution in possibilities related to data interpolation.
In terms of Digital Soil Modelling, the most common are Multiple Linear Regression (MLR),
Support Vector Machine (SVR), Random Forest (RF), Gaussian Process Regression (GPR),
and Artificial Neural Network (ANN), among others [
23
]. Likewise, the main applications
to subsurface knowledge are related to parameter estimation, uncertainty assessment, and
crop yield prediction [
19
]. In this way, the possibilities of AI are undeniable, not only
for data interpolation, but also for the generation of predictive strategies and decision
making [24]
For this reason, many researchers today strive to generate 3D models that, by means
of complex interpolation strategies, attempt to overcome the uncertainties involved in
understanding the situation underground. [
25
]. These models can complement traditional
2D cartographies and facilitate the visualisation of a generally unknown reality. Unfortu-
nately, the availability of geotechnical information on urban areas is often unorganised,
heterogeneous, scattered, and fragmented. Thus, there is still a need for strategies to enable
the management of and access to this essential information. To this end, the design of
spatial databases presents a challenge in addressing this issue.
This investigation assumes the relevance and innovation of this type of strategy to
create proposals of a more exhaustive and complete nature for the subsoil of cities, which,
due to their extension, leads to the achievement of acceptable results with a limited number
Remote Sens. 2024,16, 141 3 of 21
of points. Therefore, this article focuses on the central area of Seville, a Spanish city
of 750,000 inhabitants located in the southwest of Andalusia [
26
], with a heterogeneous
distribution of soil layers and variable geotechnical properties, and proposes a methodology
that can be easily replicated in other cities.
The main goal of this research involves the automatic modelling of the most important
subsoil characteristics in historical urban areas, based on microzoning strategies using
open data at a high level of detail. This paper is therefore divided into a presentation of
the interest and characteristics of the study area (Section 2), a detailed development of the
proposed methodology (Section 3), and a comprehensive geostatistical interpretation of
the results obtained (Section 4). Finally, the main conclusions are detailed, as are future
research lines (Section 5).
2. Case Study: Seville
Seville is Spain’s fourth-largest metropolitan city, the capital of the Andalusian re-
gion, and is located on the lower course of the Guadalquivir River with the following
geographical coordinates: 37
◦
23
0
24
00
N, 5
◦
59
0
24
00
W. At the environmental level, it is the
largest city close to Doñana National Park, a UNESCO Biosphere Reserve since 1980 [
27
].
The city covers an area of 140.80 km
2
, at an average height above sea level of 7 metres and
a flat topography generally characterised by the following environmental hazards: seismic
events [28], flooding [29], expansive strata layers, subsidence [30], and subsoil contamina-
tion [31]. The city is also acknowledged for its built heritage, and hosts a UNESCO World
Heritage Site [
32
] and significant archaeological remains [
33
]. The current development of
new metro lines also deserves mention—all this with the background of the geotechnical
problems experienced in the city during the construction of Metro Line 1 in the first decade
of the 20th century [34].
Soil Properties
The general geology of the urban area of Seville and its surroundings is based on
Neogene and Quaternary sediments typical of the Guadalquivir Basin (Figure 1): mainly
clays, silts, sands, gravels, and marly clay strata [
35
], although small areas exist where these
layers of soil alternate or even disappear. These alterations are due to the variability of the
subsoil, and in some cases were caused by artificial transformations of the course of the
Guadalquivir River, which has always flowed through the city as the only navigable river
in Spain since its origins [
36
]. Consequently, the presence of groundwater is very close to
the surface, which constitutes another key factor to be taken into account.
On the basis of these general properties, a compilation of the published studies that
have characterised the subsoil of the city of Seville was made. Table 1displays the dates of
the investigations, the number and type of records used, their level of detail, and the main
parameters recorded. Furthermore, comparative analysis shows that research covering a
larger urban working area establishes fewer spatial units to characterise the reality of the
city’s subsoil. The number of proposed units increases proportionally to the level of detail
and the amount of data analysed [
35
,
37
,
38
]. The heterogeneity of the geotechnical strata is
accentuated by the incorporation of urban factors such as archaeological pre-existences, the
presence of large tree roots, and underground infrastructures.
As a result of this comparative work, the main proposals regarding the characterisation
of the properties of the layers that make up the subsoil of the city of Seville were obtained.
Table 2provides an estimate of the main properties of the Sevillian soil layers, by applying
the arithmetic averages of the data obtained in the different studies. It can be seen that
all studies recognise, directly or indirectly, a basic stratigraphy composed of the five main
geotechnical units listed in Table 2. The strata have been characterised with those basic
parameters that enable identification of the soils. These magnitudes are appropriate for
the attainment of general knowledge and can be completed with the knowledge of other
mechanical, chemical, and physical properties.
Remote Sens. 2024,16, 141 4 of 21
RemoteSens.2024,16,xFORPEERREVIEW4of22
Figure1.Locationmapoftheurbanstudyareaandmaingeologicalareas.
Onthebasisofthesegeneralproperties,acompilationofthepublishedstudiesthat
havecharacterisedthesubsoilofthecityofSevillewasmade.Table1displaysthedates
oftheinvestigations,thenumberandtypeofrecordsused,theirlevelofdetail,andthe
mainparametersrecorded.Furthermore,comparativeanalysisshowsthatresearchcov-
eringalargerurbanworkingareaestablishesfewerspatialunitstocharacterisethereality
ofthecity’ssubsoil.Thenumberofproposedunitsincreasesproportionallytothelevelof
detailandtheamountofdataanalysed[35,37,38].Theheterogeneityofthegeotechnical
strataisaccentuatedbytheincorporationofurbanfactorssuchasarchaeologicalpre-ex-
istences,thepresenceoflargetreeroots,andundergroundinfrastructures.
Table1.ComparisonofpublishedstudiesonthesubsoilofthecityofSeville.
InstitutionalYearMain
Topics
ZonationDe-
tail
Mapping
Scale
Unit
NumberTestPointsReferences
Geotechnicaland
MiningInstituteof
Spain
1975GeneralGeotechnical
MapNational1:200,000 5Notdefined[39]
1975GeneralGeological
MapNational1:50,0003Notdefined[40]
1983 UrbanGeotechnical
MapLocal1:25,0009 51 [41]
2008GeneralGeological
MapNational1:50,0003Notdefined[42]
Figure 1. Location map of the urban study area and main geological areas.
Table 1. Comparison of published studies on the subsoil of the city of Seville.
Institutional Year Main
Topics Zonation Detail Mapping Scale Unit
Number Test Points References
Geotechnical and
Mining Institute
of Spain
1975 General Geotechnical
Map National 1:200,000 5 Not defined [39]
1975 General Geological Map National 1:50,000 3 Not defined [40]
1983 Urban Geotechnical
Map Local 1:25,000 9 51 [41]
2008 General Geological Map National 1:50,000 3 Not defined [42]
Andalusian
Regional
Government
2009 Regional Geotechnical
Samples
No zonation: test
points only Not defined Not
defined 58 [43]
University of
Seville
1985 Urban Geotechnical
and Mineral
Identification
No zonation: test
points only Not defined 6 78 [44]
1986 Urban Geological
Study Local 1:50,000 4 132 [35]
1994 No zonation: test
points only No zonation: test
points only Not defined 44 No zonation:
test points only
[45]
2001 Architectonic and
Geotechnical Study
No zonation: test
points only
No zonation: test
points only Not
defined 44 [46]
2009
Geoarchaeological Map
Urban 1:25,000 5 135 [47]
2013 Urban Geotechnical
Map
No zonation: test
points only
No zonation: test
points only Not
defined 117 [48]
2014
Geoarchaeological Map
Local Not defined 3Not defined [49]
2017 Urban Geotechnical
Map Local 1:60,000 4 700 [37]
Architects’
Association
of Seville
2005 Urban Geotechnical
Map
No zonation: test
points only 1:25,000 Not
defined 208 [38]
Spanish State Ports 2009 Geotechnical Map of
the urban sector Urban sector 1:15,000 Not
defined Not defined [50]
Remote Sens. 2024,16, 141 5 of 21
Table 2. Geotechnical layer proposal and estimated average geotechnical parameters 1.
Soil
Unit
Geotechnical
Parameters
References
Soils of
Seville
History of
Sevilla
Metro
Geotechnical
Atlas of the
Port
Historical
Founda-
tions
Building
Inspection
Sevilla
Metro
Works
Foundation
Models
[44] [45] [50] [46] [38] [48] [37]
(a) Fill soils and
brown clays
Top depth (m) Surface level, considered at 0.00 metres of depth, without global height coordinates
Bottom depth (m) - - 3.0 3.0–6.0 3.50 4.0 0.5–11.3
Thickness (m) - - 3.0 3.0–6.0 3.50 4.0 0.5–11.3
USCS (Soil class) - - CL - CL CL -
T200/T 0.08 (%) - - 94.00 - 94.00 94.00 -
Unit weight
(kN/m3)- - 17.30 - 17.30 18.20 -
Water content (%) - - 21.00 - 21.00 21.00 -
Friction angle (◦)- - - - - 22.0–29.0 -
Qu (kN/m2)- - 155.0 - 155.0 155.0 -
(b)
Grey Clays
Top depth (m) - - 3.0 3.0–6.0 3.50 4.0 0.5–11.3
Bottom depth (m) - - 8.0 12.0–15.0 8.00 9.0 2.0–6.0
Thickness (m) 3.0–14.0 - 5.0 6.0–12.0 4.50 5.0 -
USCS (Soil class) - - CH CL/ML CL/CH CL/CH -
T200/T 0.08 (%) 84.10 89.50 93.00 - 93.00 93.00 -
Unit weight
(kN/m3)- 16.00 17.15 - 17.15 14.9–19.4 -
Water content (%) 45.90 15.00 28.90 - 28.90 28.90 -
Friction angle (◦)- 27.5 11.0 19.0–26.0 11.0 11.0–29.0 -
Qu (kN/m2)- 175.0 172.5 29.0–90.0 190.0 190.0 14.0–340.0
SPT (N) - - 29 6–10 29 6–29 -
(c)
Silty sands
Top depth (m) - - 8.0 - 8.0 9.0 -
Bottom depth (m) - - 12.0 - 15.0 14.0 -
Thickness (m) 6.0–15.0 - 4.0 - 7.0 5.0 -
USCS (Soil class) - - ML - ML, S ML, SM-P -
T200/T 0.08 (%) 34.30 47.00 92.00 - 92.00 42.00 -
Unit weight
(kN/m3)- 18.00 17.85 - 17.85 16.0–19.7 -
Water content (%) - 40.00 22.10 - 22.10 22.10 -
Friction angle (◦)- 35.0 25.0 - 25.0 32.0–35.0 30.0–32.0
Qu (kN/m2)- 53.5 130.0 - 130.0 130.0
SPT (N) - - - - - - 10–20
(d)
Sandy gravels
Top depth (m) - - 12.0 12.0–15.0 15.0 14.0 3.4–17.3
Bottom depth (m) - - 17.0 17.0–22.0 19.0 18.0 13.0–25.0
Thickness (m) - - 5.0 2.0–10.0 4.0 4.0 3.0–9.0
USCS (Soil class) - - - SP, GP - GW/P/M -
T200/T 0.08 (%) - 5.00 - - 11.40 3.10 -
Unit weight
(kN/m3)- 21.00 - - - 20.0–21.0 -
Water content (%) - 7.50 - - 16.00 7.50 -
Friction angle (◦)- 40.0 - 34.5 - 34.0–45.0 -
Qu (kN/m2)- - - - - - -
SPT (N) - - - >40 67 67 -
(e)
Marly clays
Top depth (m) - - 17.0 - 19.0 18.0 13.0–25.0
Bottom depth (m) - - - - - - -
Thickness (m) - - - - - - -
USCS (Soil class) - - CH CH CH CH -
T200/T 0.08 (%) - 94.50 95.00 - 95.00 95.00 -
Unit weight
(kN/m3)- 16.00 17.75 15.50 - 15.7–19.8 -
Water content (%) - 25.00 26.90 - 26.90 26.90 -
Friction angle (◦)- 29.5 20.0 25.0 - 25.0–34.0 -
Qu (kN/m2)- - 620.00 450.00 - 620–1800 325–521
SPT (N) - - 75 - 75 75 -
1Values with the symbol “-” refer to non-defined quantities in the corresponding layer.
Remote Sens. 2024,16, 141 6 of 21
Due to the different locations of the points, the data specified in Table 2show major
heterogeneity in the criteria for the selection of the magnitudes, in the values related to the
properties, and in their ranges of variation. This dispersion can be related to the different
disciplinary objectives pursued by each study and may reveal the most common reality in
urban sites. This approach to the basic soil properties of Seville reveals a highly complex
geotechnical reality, which justifies the need to spatially classify the collected information in
a more systematic and detailed way. The published research is highly useful and validates
the hypotheses generated from the various zoning strategies developed. However, at the
technological level, the following set of shortcomings can be disclosed.
•
Missed integration of global topographic levels in survey points. Supplementary
information on surveys is seldom provided.
•
Absence of georeferenced coordinates in most of the surveys, lacking Universal Trans-
verse Mercator (UTM) coordinates and geographic coordinates, and no implementa-
tion of Geographic Information Systems (GIS).
•
Limitation of 2D representations: no hypothesis for a 3D distribution of the strata has
been detected.
•
Mapping hypotheses generally do not use interpolation tools, and when they do, there
is a “blind” selection of the method, with no analytical reflections.
•
Lack of proposals for database structures with management purposes. Previous studies
focus on the description of the properties of soil values, without providing detailed
methodological considerations regarding information processing and mapping.
In order to further explore a topic of great interest, this research strives to overcome
these limitations by developing a special database for the 3D integration of information
using LIDAR data.
3. Geotechnical 3D Modelling for Urban Environments
This section describes the set of steps of a replicable methodology that allows the
integration of geotechnical information collected in urban sites. Figure 2shows the main
phases of the design of a spatial dataset that leads to the automatic visualisation of digital
models that evolve the knowledge of the urban underground.
3.1. Data Gathering
Through this phase, it has been possible to develop a comprehensive microzonation
strategy by massively collecting a detailed open dataset of geotechnical information in an
urban area with an extension of 140 km2.
3.1.1. Available Information
Currently, a large part of the data on the geotechnical characteristics of the Seville sub-
soil is not openly available. In our case study, we started with data that are
accessible [38,41,43,44,46,47]
and, through mathematical interpolation, we have estimated
the numbers of areas with less information, with an emphasis on the importance of the
task of compiling and digitising all the available information that has been developed for
this research.
The data gathering process utilised the aforementioned open data through a systematic
digitisation of the information that was still in print. In this way, two main digitisation
processes have been carried out: (1) those relating to documentation in charts or drawing
format, which can be linked as complementary information to the digitised records; and
(2) the incorporation of the geotechnical parameters collected in the original documentation
for their subsequent integration into the database. Through the procedures, a total of
650 records of the central area of Seville have been generated.
Remote Sens. 2024,16, 141 7 of 21
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Figure2.Methodologicalandmainresearchandmethodologyphases.
Figure 2. Methodological and main research and methodology phases.
Remote Sens. 2024,16, 141 8 of 21
3.1.2. Soil Survey Location
The analysis of the information collected shows that the heterogeneity of the data is
very high and is displayed through numerous formats, including summary tables, maps,
model sheets, and profiles. The following considerations can be drawn from it:
•
Most of the digitised records come from studies related to architectural work (40.90%),
followed in second place by those related to civil work (28.30%) and, finally, by
geological (20.30%) and archaeological (10.50%) studies.
•
The date of the information collected runs in a chronological range from 1974 to 2017.
However, the recent data has verified that there have been no significant alterations in
the oldest points.
•
The definition of the strata has been proven in each case by test results and accredited
by the researcher, technician, or company responsible for the corresponding work.
3.1.3. Georeferencing Process
Due to the lack of a proper definition of the location of a number of the points, geo-
referencing was necessary. It should be borne in mind that in the most favourable cases,
the information on the coordinates provided for each survey was only determined by XY
values and the global altimetry of each point was missing, which was subsequently incor-
porated into the topographic integration stage. It was therefore necessary to georeference
the location maps of the points scanned in the previous stage. The quality control of the
location was finally determined through the creation of specific indicators for this purpose,
including the Root Mean Square Error (RMSE).
Based on these conditions, the points were created in vector format and georeferenced
in accordance with the European INSPIRE directive [
51
], which would allow compatibility
with the future publication of information in the Spatial Data Infrastructure of Seville
(IDE-Sevilla). Consequently, the geodetic system adopted was the European Terrestrial
Reference System 1989 (ETRS 1989), the defined coordinate system was the Universal
Transverse Mercator (UTM), and the geographical area was Zone 30.
3.1.4. Parameter Selection
The parameters for the characterisation of soil properties depend on the disciplinary
approach, the objectives, and the methods followed. In this case, the selection criteria
were designed to provide an identification of the spatial and volumetric distribution of
the main strata and their relationship to groundwater. In the same way, basic parameters
related to the mechanical resistance of the shallow layers of the subsoil have been included,
discarding those corresponding to the fill soil layer due to their low reliability (Figure 3).
Most of the data entered into the selected parametric fields come from the geotechnical
information gathered. The only exception is the value of the top level of the fill soil layer
(parameter a1, according to Figure 3), which comes from the topographical information
from the LIDAR points provided by the National Aerial Orthophotography Plan (PNOA,
Plan Nacional de Ortofotografía Aérea) developed by the Spanish Government [52].
3.2. Exploratory Analysis
Before designing the definitive structure of the database, preliminary spatial and
statistical analyses were performed. These tasks have enabled us to assess the quality of
the data collected and the possibilities of the developed information structure.
3.2.1. Point Density and Distribution
The study of the density and distribution of the survey points has validated the spatial
quality of the data obtained. This process has finally allowed the selection of the mapping
area, as Figure 4shows in the next methodological step.
Remote Sens. 2024,16, 141 9 of 21
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strataandtheirrelationshiptogroundwater.Inthesameway,basicparametersrelatedto
themechanicalresistanceoftheshallowlayersofthesubsoilhavebeenincluded,discard-
ingthosecorrespondingtothefillsoillayerduetotheirlowreliability(Figure3).
Mostofthedataenteredintotheselectedparametricfieldscomefromthegeotech-
nicalinformationgathered.Theonlyexceptionisthevalueofthetoplevelofthefillsoil
layer(parametera1,accordingtoFigure3),whichcomesfromthetopographicalinfor-
mationfromtheLIDARpointsprovidedbytheNationalAerialOrthophotographyPlan
(PNOA,PlanNacionaldeOrtofotografíaAérea)developedbytheSpanishGovernment[52].
Figure3.Groundlayerdiagramandselectionofrepresentativeparametersformodelling.
3.2.ExploratoryAnalysis
Beforedesigningthedefinitivestructureofthedatabase,preliminaryspatialandsta-
tisticalanalyseswereperformed.Thesetaskshaveenabledustoassessthequalityofthe
datacollectedandthepossibilitiesofthedevelopedinformationstructure.
3.2.1. PointDensityandDistribution
Thestudyofthedensityanddistributionofthesurveypointshasvalidatedthespa-
tialqualityofthedataobtained.Thisprocesshasfinallyallowedtheselectionofthemap-
pingarea,asFigure4showsinthenextmethodologicalstep.
Thedensityhasbeendefinedasthequotientofthenumberoftestpointsdividedby
theareaofthereferencepolygonconsideredtocoverthegeoreferencedpoints.According
tothisapproachandconsideringdifferentgeometricpossibilities(eitherpolygonalorrec-
tangularinshape),acomparativedensitystudyofdifferentmappingareashasbeenes-
tablished(Table3).
Figure 3. Ground layer diagram and selection of representative parameters for modelling.
RemoteSens.2024,16,xFORPEERREVIEW11of22
Table3.Pointdensityvaluesfromdifferentgeometriccriteria.
AreaGeometryNumberofPoints
(n)AreaExtension(km2)Density(n/km2)
Urbanarea(polygon)1256142.088.84
Urbanarea(rectangle)1503322.304.66
Historicalcity(polygon)3207.7541.29
Historicalcity(rectangle)49518.0427.43
Complementarily,thedistributionanalysisdisplaysthedegreeofspatialhomogene-
ityoftheinformation,determinedthroughthebehaviourofthecoincidentspatialgrids.
Thedefinitionofgridsisusuallycarriedoutbydeterminingthesize,proportion,orienta-
tion,andextensionofthecomprisingcells.Forthiscase,theGEOSTATGridDataset2011
wasemployedtovisualisethespatialdistributionoftherecordedpoints[53].Thisdataset
isalsopublishedandspatiallyfilteredbytheSevilleCityCouncilSpatialDataInfrastruc-
ture(IDESevilla),whichprovidesvariousgridswithsquaredcellsthatrangebetween
0.25,0.50,and1.00kminlength[54].
3.2.2..MappingAreaSelection
Fromtheevaluationofthevariousworkinghypothesesthathavebeencharacterised
throughdensity(Table3)andspatialdistributionstudies,arectangulargeometrywork
areaand54km2ofextensionhavebeenproposedduetothearea’srepresentativeposition
andorientationfromnorthtosouth(Figure4).Thisareacomprises650pointsandtakes
adensityvalueof12.03points/km2,whichoptimisesthenumberofcellsthatdisplaythe
maximumdensityvalues.
Figure4.Mappingareaselectionfromdensityanddistributionanalysis.I
Figure 4. Mapping area selection from density and distribution analysis.
Remote Sens. 2024,16, 141 10 of 21
The density has been defined as the quotient of the number of test points divided by
the area of the reference polygon considered to cover the georeferenced points. According
to this approach and considering different geometric possibilities (either polygonal or
rectangular in shape), a comparative density study of different mapping areas has been
established (Table 3).
Table 3. Point density values from different geometric criteria.
Area Geometry Number of Points (n) Area Extension (km2) Density (n/km2)
Urban area (polygon) 1256 142.08 8.84
Urban area (rectangle) 1503 322.30 4.66
Historical city (polygon)
320 7.75 41.29
Historical city (rectangle)
495 18.04 27.43
Complementarily, the distribution analysis displays the degree of spatial homogeneity
of the information, determined through the behaviour of the coincident spatial grids. The
definition of grids is usually carried out by determining the size, proportion, orientation,
and extension of the comprising cells. For this case, the GEOSTAT Grid Dataset 2011 was
employed to visualise the spatial distribution of the recorded points [
53
]. This dataset is
also published and spatially filtered by the Seville City Council Spatial Data Infrastructure
(IDE Sevilla), which provides various grids with squared cells that range between 0.25, 0.50,
and 1.00 km in length [54].
3.2.2. Mapping Area Selection
From the evaluation of the various working hypotheses that have been characterised
through density (Table 3) and spatial distribution studies, a rectangular geometry work
area and 54 km
2
of extension have been proposed due to the area’s representative position
and orientation from north to south (Figure 4). This area comprises 650 points and takes a
density value of 12.03 points/km
2
, which optimises the number of cells that display the
maximum density values.
3.2.3. Statistical Values
The use of mathematical analysis of the gathered geotechnical information has led to
the identification of a first set of behaviour patterns in the selected data. Through these stud-
ies, it has been possible to determine the validity not only of each of the records included
in the database, but also of the distribution of the values of the recorded information.
As shown in Figure 5, the statistical characterisation of the samples has considered
the maximum and minimum values, and the arithmetic means and medians. Similarly, the
distribution of the records has been analysed through their grouping into quartiles and the
calculation of kurtosis and skewness coefficients.
Finally, the validity of the data has been corroborated by determining the typical
error values and standard deviation. Through this methodological step, anomalous and
erroneous results have been removed, which has led to a definitive selection of data
regarding the subsequent modelling. Gaps of information have also been detected in
certain parameters, or non-existent soil layers in certain areas of the city. In this respect, it
can be observed that none of the selected parameters has the full number of 650 records
collected in the database. The maximum acceptance values, above 90%, correspond to the
upper levels of the soil layers, while the lower values, lying below 60%, correspond to the
data related to the resistance of the layers.
Remote Sens. 2024,16, 141 11 of 21
RemoteSens.2024,16,xFORPEERREVIEW12of22
3.2.3.StatisticalValues
Theuseofmathematicalanalysisofthegatheredgeotechnicalinformationhasledto
theidentificationofafirstsetofbehaviourpatternsintheselecteddata.Throughthese
studies,ithasbeenpossibletodeterminethevaliditynotonlyofeachoftherecordsin-
cludedinthedatabase,butalsoofthedistributionofthevaluesoftherecordedinfor-
mation.
AsshowninFigure5,thestatisticalcharacterisationofthesampleshasconsidered
themaximumandminimumvalues,andthearithmeticmeansandmedians.Similarly,
thedistributionoftherecordshasbeenanalysedthroughtheirgroupingintoquartiles
andthecalculationofkurtosisandskewnesscoefficients.
Figure5.Statisticalparametersofthesoilproperties.
Finally,thevalidityofthedatahasbeencorroboratedbydeterminingthetypicaler-
rorvaluesandstandarddeviation.Throughthismethodologicalstep,anomalousander-
roneousresultshavebeenremoved,whichhasledtoadefinitiveselectionofdataregard-
ingthesubsequentmodelling.Gapsofinformationhavealsobeendetectedincertainpa-
rameters,ornon-existentsoillayersincertainareasofthecity.Inthisrespect,itcanbe
observedthatnoneoftheselectedparametershasthefullnumberof650recordscollected
inthedatabase.Themaximumacceptancevalues,above90%,correspondtotheupper
levelsofthesoillayers,whilethelowervalues,lyingbelow60%,correspondtothedata
relatedtotheresistanceofthelayers.

Figure 5. Statistical parameters of the soil properties.
3.3. Database Design
This phase has included normalisation of the subsoil data selected from the avail-
able information. It strives to design a unified spatial database aimed at evaluating the
geotechnical knowledge of the mapping area.
3.3.1. Normalisation
The database has been organised based on three main blocks of information that define
the conditions of the study points (A), the geotechnical classification of the strata (B), and
the mechanical properties of the most significant layers (C).
Block A determines the position of the survey points, as well as the date of the survey,
the company in charge, and the type of work to which it is associated. Block B provides the
geometric limits of the strata, through their normalised classification, the depth ceilings
of their layers referenced from ground level, and their thicknesses. Block C provides
complementary values related to the existence of groundwater, and the values of the
strengths of the most superficial layers, defined through the simple compressive strength
tests in the case of the clayey layers and the number of hits from the Standard Penetration
Tests (SPT) in the case of the sandy layers. Similarly, correlation formulae have been used
for the determination of these strength parameters in cases where the available information
has been provided by alternative tests.
Remote Sens. 2024,16, 141 12 of 21
3.3.2. Database Structure
The standardisation work has made it possible to define a relational database (RDBMS)
proposal that is organised based on the Unified Modelling Language (UML) class diagram
shown in Figure 6. Its design is oriented to work with the basic information for the
visualisation of the layers and the mechanical properties of the soil, being susceptible to
future evolutions to generate more complex data flows.
RemoteSens.2024,16,xFORPEERREVIEW14of22
Figure6.UMLclassdiagramforthedatabase.BlockA(grey);BlockB(blue);BlockC(red).
3.3.3.FormatsandInteroperabilityProcesses
Thefulfilmentofthedatahasbeenachievedthroughthedirectinterpretationand
transcriptionofthedatafromtheoriginalsourceswithoutanyautomationprocesshaving
beenapplied.Fromthere,rasterandvectormodelshavebeenworkedwiththroughin-
teroperabilityprocessesthathavemainlyinvolvedtheuseofCADandGIStools.Specifi-
cally,thesoftwareusedwasAutoCADversion2023andArcGISProversion3.1.1,witha
campuslicencefromtheUniversityofSeville.
3.4.Modelling
Themodellingphaseinvolvedmergingthematicandspatialdata,payingparticular
attentiontotheintegrationprocessesoftopographicdata.Theresultingcartographiesand
3Dmodels,createdusingvariousinterpolationtechniques,canbepublishedasinteractive
spatialinformation.
3.4.1.TopographicalIntegration
Giventhatmostoftheoriginaldatalackglobaltopographiccoordinates,withrec-
ordedvaluesof0.00metresabovegroundlevel,thetopographicintegrationprocessbe-
comescrucial.Bytakingthisstep,asignificantproblemhasbeenresolvedtoguarantee
precisevaluesinsubsequentinterpolationprocedures.
Theaccuratesurfacecoordinatesforthedatabasepointswereestablishedonthebasis
oftopographicdataprovidedbythemajoropeninstitutionalsources.Table4showsa
comparisonofkeyproperties,includingthesource,date,GeographicCoordinateSystem
(GCS),rastercellsize,totalLIDARpointsusedinthemodel,andtheclassificationsystem
utilisedforthesepoints.
Figure 6. UML class diagram for the database. Block A (grey); Block B (blue); Block C (red).
Through this diagram, the main information tables have been defined, which respond
to the blocks in the previous section. Likewise, the relationships between the different
tables and their attributes have been established. The current proposal sets out a working
basis for the incorporation of future considerations and parameters, which will allow a
subject as complex as that related to the reality of the subsoil in urban and metropolitan
areas to be addressed with greater scope.
Remote Sens. 2024,16, 141 13 of 21
3.3.3. Formats and Interoperability Processes
The fulfilment of the data has been achieved through the direct interpretation and
transcription of the data from the original sources without any automation process having
been applied. From there, raster and vector models have been worked with through inter-
operability processes that have mainly involved the use of CAD and GIS tools. Specifically,
the software used was AutoCAD version 2023 and ArcGIS Pro version 3.1.1, with a campus
licence from the University of Seville.
3.4. Modelling
The modelling phase involved merging thematic and spatial data, paying particular
attention to the integration processes of topographic data. The resulting cartographies and
3D models, created using various interpolation techniques, can be published as interactive
spatial information.
3.4.1. Topographical Integration
Given that most of the original data lack global topographic coordinates, with recorded
values of 0.00 metres above ground level, the topographic integration process becomes
crucial. By taking this step, a significant problem has been resolved to guarantee precise
values in subsequent interpolation procedures.
The accurate surface coordinates for the database points were established on the basis
of topographic data provided by the major open institutional sources. Table 4shows a
comparison of key properties, including the source, date, Geographic Coordinate System
(GCS), raster cell size, total LIDAR points used in the model, and the classification system
utilised for these points.
Table 4. Sources available for the topographic integration of the model.
Source Year GCS Cell Size (m2)LIDAR Points Class
ALOS 2016 WGS_1984 30.00 ×30.00 Not defined Not defined
PNOA 2021
ETRS89_UTM_Z30
1.50 ×1.50 6,017,209 ASTM
IECA 2018
ETRS89_UTM_Z30
5.00 ×5.00 200,000 ASTM
IDE Sevilla 2009 WGS_1984 Not defined 1250 Unclassified
In this study, the ALOS Global Digital Surface Model [
55
] was considered, but did
not offer the level of accuracy necessary for an urban setting. At the national level, PNOA
LIDAR data were employed [
52
]. At the regional level, information provided by Digital
Elevation Models (DEM), published by the Institute of Statistics and Cartography of An-
dalusia (Instituto de Estadística y Cartografía de Andalucía, IECA), has been contemplated [
56
].
Finally, the values of the points of the geodetic networks of the city, published by IDE
Sevilla, were also used at the local level [54].
Through the process shown in Figure 7, the database points within the mapped area
have been assigned global Z coordinates. The starting point is to download the LIDAR
points in LAZ format, which are provided by the PNOA in Seville (Figure 7, Stage 1).
During the decompression process to LAS format, the points are spatially filtered. The
mapping area was employed to establish the XY coordinates, while the local geodetic
information provided by IDE Sevilla verified the Z coordinates and made it possible to
eliminate incorrect height values (Figure 7, Stage 2). This local geodata contain a much
smaller number of points but are still strongly representative due to the high quality of
the information sheets they provide. Once a filtered LAS dataset of the working area has
been created, a raster file is generated and overlaid within the selected geotechnical points
(Figure 7, Stage 3). This completes the topographic integration process by assigning Z
values to the points through the raster.
Remote Sens. 2024,16, 141 14 of 21
RemoteSens.2024,16,xFORPEERREVIEW15of22
Table4.Sourcesavailableforthetopographicintegrationofthemodel.
SourceYearGCSCellSize(m2)LIDARPointsClass
ALOS2016WGS_198430.00×30.00NotdefinedNotdefined
PNOA2021ETRS89_UTM_Z301.50×1.506,017,209ASTM
IECA2018ETRS89_UTM_Z305.00×5.00200,000ASTM
IDESevilla2009WGS_1984Notdefined1250Unclassified
Inthisstudy,theALOSGlobalDigitalSurfaceModel[55]wasconsidered,butdid
notofferthelevelofaccuracynecessaryforanurbansetting.Atthenationallevel,PNOA
LIDARdatawereemployed[52].Attheregionallevel,informationprovidedbyDigital
ElevationModels(DEM),publishedbytheInstituteofStatisticsandCartographyofAn-
dalusia(InstitutodeEstadísticayCartografíadeAndalucía,IECA),hasbeencontemplated
[56].Finally,thevaluesofthepointsofthegeodeticnetworksofthecity,publishedby
IDESevilla,werealsousedatthelocallevel[54].
ThroughtheprocessshowninFigure7,thedatabasepointswithinthemappedarea
havebeenassignedglobalZcoordinates.ThestartingpointistodownloadtheLIDAR
pointsinLAZformat,whichareprovidedbythePNOAinSeville(Figure7,Stage1).
DuringthedecompressionprocesstoLASformat,thepointsarespatiallyfiltered.The
mappingareawasemployedtoestablishtheXYcoordinates,whilethelocalgeodeticin-
formationprovidedbyIDESevillaverifiedtheZcoordinatesandmadeitpossibletoelim-
inateincorrectheightvalues(Figure7,Stage2).Thislocalgeodatacontainamuchsmaller
numberofpointsbutarestillstronglyrepresentativeduetothehighqualityoftheinfor-
mationsheetstheyprovide.OnceafilteredLASdatasetoftheworkingareahasbeencre-
ated,arasterfileisgeneratedandoverlaidwithintheselectedgeotechnicalpoints(Figure
7,Stage3).ThiscompletesthetopographicintegrationprocessbyassigningZvaluesto
thepointsthroughtheraster.
Figure7.Stagesforintegratingtopographicdataintothegeotechnicaldatabase.
Figure 7. Stages for integrating topographic data into the geotechnical database.
3.4.2. D Soil Mapping and Interpolation Criteria
Once all the altimetric values of the layers from the various survey points have been
organised as spatial entities, raster maps can be produced from the location and depth
values of each geotechnical layer. In Figure 8, the layers selected for the 3D modelling
process are displayed as flat maps, showing their depth values by means of isovalues.
In order to identify areas lacking geotechnical information, spatial interpolation has
been performed using the Ordinary Kriging (OK) technique, which is currently considered
the most consistent in the scientific literature regarding research [
20
,
21
,
57
]. The results
demonstrate a geometric distribution of isovalues that align with the statistical analyses
conducted in a preliminary manner.
Remote Sens. 2024,16, 141 15 of 21
Figure 8. Raster maps of the geotechnical layers selected for 3D modelling.
3.4.3. Geotechnical 3D Model
By converting 2D raster maps into Triangulated Irregular Networks (TIN), a 3D model
of the subsoil of the city of Seville can be generated within the selected mapping area.
The following geoprocesses offered by ArcGIS Pro software version 3.1 have been used
to carry out this transformation: “raster to TIN” and “TIN domain”. On the one hand,
Figure 9a displays the geotechnical layout of each 3D point in the model and exhibits the
Remote Sens. 2024,16, 141 16 of 21
linear depth distribution of the boreholes. Additionally, Figure 9b shows the geometric
layer distribution.
RemoteSens.2024,16,xFORPEERREVIEW17of22
3.4.3.Geotechnical3DModel
Byconverting2DrastermapsintoTriangulatedIrregularNetworks(TIN),a3D
modelofthesubsoilofthecityofSevillecanbegeneratedwithintheselectedmapping
area.ThefollowinggeoprocessesofferedbyArcGISProsoftwareversion3.1havebeen
usedtocarryoutthistransformation:“rastertoTIN”and“TINdomain”.Ontheonehand,
Figure9adisplaysthegeotechnicallayoutofeach3Dpointinthemodelandexhibitsthe
lineardepthdistributionoftheboreholes.Additionally,Figure9bshowsthegeometric
layerdistribution.
Bothillustrationsdemonstratehowurbansubsoilmodelshavethepotentialtointe-
gratethecity’stopographyandthefootprintofitsbuildingsinaspatialmanner.Similarly,
visualisingtherelationshipsbetweenthesubsoilandthesurfacecanbeachievedbypro-
ducing3Dmodelsectionsusingvariousaxesandpolygonallines.
Figure9.Underground3Dmodelofthecasestudywith(a)boreholesand(b)soillayers.
4.Discussion
Fromtheresultsobtained,thefollowinggeostatisticalandgeotechnicalinterpreta-
tionscanbemade.
Figure 9. Underground 3D model of the case study with (a) boreholes and (b) soil layers.
Both illustrations demonstrate how urban subsoil models have the potential to inte-
grate the city’s topography and the footprint of its buildings in a spatial manner. Similarly,
visualising the relationships between the subsoil and the surface can be achieved by pro-
ducing 3D model sections using various axes and polygonal lines.
4. Discussion
From the results obtained, the following geostatistical and geotechnical interpretations
can be made.
4.1. Geostatistical Interpretation
The special behaviour of the interpolation performed can be analysed from a statistical
point of view by studying the error metrics. Consequently, the study of isotropic variograms
Remote Sens. 2024,16, 141 17 of 21
has been carried out using linear, spherical, exponential, and Gaussian models. Table 5
illustrates their suitability for each of the selected working parameters.
Table 5. Comparison of isotropic error metrics, highlighting best-fit models.
Layer Model R2Nugget (Co) Still
(Co + C) Range Proportion
(C/(co + C)) Residual
Linear 0.00 3.87 0.00 1.32 −25.86 3124.00
Spherical 0.13 0.01 32.34 11,100.00 1.00 233.00
Exponential 0.14 0.30 41.20 23,460.00 1.00 285.00
a2. Fill soil
thickness (m)
Gaussian 0.13 0.48 45.60 10,929.24 0.95 116.00
Linear 0.86 1.20 32.84 5542.05 0.96 214.00
Spherical 0.85 1.50 54.00 13,600.00 0.97 239.00
Exponential 0.80 1.00 53.00 20,850.00 0.98 320.00
b1. Clay top-level
depth (m)
Gaussian 0.91 6.10 53.00 10,149.82 0.90 150.00
Linear 0.65 10.57 16.85 5542.49 0.37 27.80
Spherical 0.89 7.82 15.65 3590.00 0.50 9.26
Exponential 0.86 7.27 15.93 4320.00 0.54 11.30
b2. Clay
thickness (m)
Gaussian 0.87 7.91 15.83 2857.88 0.50 14.10
Linear 0.88 4.09 9.72 5543.41 0.58 5.88
Spherical 0.87 4.07 16.64 18,160.00 0.76 6.08
Exponential 0.86 3.95 24.63 52,620.00 0.84 6.49
b3. Clay undrained
compression
resistance (kN/m2)Gaussian 0.89 4.95 15.74 11,933.83 0.69 5.15
Linear 0.97 9.41 54.56 5543.15 0.83 81.40
Spherical 0.97 8.80 78.60 12,070.00 0.89 93.60
Exponential 0.94 6.50 74.00 15,900.00 0.91 159.00
c1. Sand top-level
depth (m)
Gaussian 0.95 15.00 65.82 7863.51 0.77 133.00
Linear 0.27 11.52 14.37 5543.75 0.20 28.90
Spherical 0.41 0.48 13.27 460.00 0.96 23.40
Exponential 0.69 6.76 13.79 2040.00 0.51 12.30
c2. Sand
thickness (m)
Gaussian 0.41 2.03 13.28 398.17 0.85 23.30
Linear 0.96 490.02 1264.76 5547.02 0.61 34,340.00
Spherical 0.96 484.00 1997.00 15,660.00 0.76 37,462.00
Exponential 0.95 470.00 3050.00 46,920.00 0.85 44,360.00
c3. Sand (SPT)
Gaussian 0.97 589.00 1549.00 8521.69 0.71 21,892.00
Linear 0.99 17.31 73.69 5544.04 0.77 41.30
Spherical 0.99 15.20 81.94 8490.00 0.81 28.90
Exponential 0.98 12.40 85.80 12,210.00 0.86 121.00
d1. Top-level depth
of the rock (m)
Gaussian 0.99 21.60 73.70 5958.25 0.71 38.00
Linear 0.98 5.42 80.15 5540.72 0.93 223.00
Spherical 0.96 4.90 100.80 9930.00 0.95 331.00
Exponential 0.91 0.90 96.80 13,320.00 0.97 767.00
e1. Marls top-level
depth (m)
Gaussian 0.98 15.50 112.00 8902.74 0.86 157.00
Linear 0.89 2.84 23.89 5538.59 0.88 71.70
Spherical 0.88 2.90 36.80 13,010.00 0.92 81.50
Exponential 0.83 2.50 36.00 19,470.00 0.93 113.00
o1. Water table
top-level depth (m)
Gaussian 0.93 6.10 43.20 11,327.61 0.86 49.80
4.2. Geotechnical Interpretation
Through the use of microzoning mapping, the heterogeneity of the city’s subsoil can
be observed, where the stratigraphic cross-section varies widely from one area to another,
leading in certain areas to the absence of several layers in the stratigraphic cross-section.
These differences, on a territorial scale, cannot be noticed or taken into consideration, since
they are not displayed on the cartography: these characteristics or the strata themselves
have been simplified and grouped, in most cases in a very general way. By means of
geotechnical evaluation maps, the validity of the hypothesis based on the distribution of
the basic geotechnical units defined for the Seville subsoil has been confirmed.
On analysing the results, it is possible to notice the problems generated by the areas
where the depth of the clay ceiling exceeds 2 metres, with layers of mainly anthropic fill
Remote Sens. 2024,16, 141 18 of 21
soils with heterogeneous geotechnical properties and low resistance that lack contrasted
parametric information. Likewise, there are several areas with clay thicknesses of more than
3 metres, which can be the origin of problems of expansivity affecting civil infrastructures
and buildings. Finally, areas with anomalies in the characteristic geotechnical distribution
have been detected, in which the sandy strata are above the clay strata and overlap old,
buried river courses or former watercourses of the Guadalquivir River.
5. Conclusions
Studies of the microzoning of subsoils on an urban scale are needed, since they are
necessary for the development of related applications such as urban planning, seismic
risk evaluation, urban and architectural design, and preventive conservation of built
heritage. This approach goes into further depth than the existing approaches developed
with macrozonation techniques, whereby the particularity of the subsoil in the urban area
of the city is not covered, but parameters or general characteristics are established on a
territorial level.
The application of the proposed methodology has shown that it is possible to efficiently
determine not only the distribution of the geotechnical units that make up the subsoil
in urban environments, but also the variations in their essential mechanical resistance
properties. In this way, realistic and accurate hypotheses regarding the underground
reality of urban and metropolitan environments can be formed with agility. Thanks to the
developed model, it is possible to attain a better understanding of the different strata in the
city in relation to the topographic profile, by detecting areas where the strata have different
thicknesses, through the application of the 3D model. In this respect, the gathering process
together with the compilation and simplification criteria should contribute towards the
future design of a geotechnical interpretation protocol.
Transformation of existing geotechnical information into spatial data enables control
of the spatial distribution of the strata, thereby making it possible to visualise the variations
of the values and to cross-check these values with the urban information provided by
geospatial technologies. Nevertheless, the configuration of the database as a predictive
model using artificial intelligence requires a comprehensive revision of the current database
to consider in greater depth the large number of parameters that set up the complex reality
of the urban subsoil, from both an integral perspective and a new contemporary paradigm.
There is currently a clear need for data to be published with open access in order to
promote better knowledge of the subsoil, since a key factor in many issues is related to
the development and management of urban environments, greater accessibility, and open
access to all data. Similarly, it is necessary to encourage the development of strategies to
standardise and systematise existing knowledge in order to provide geotechnical informa-
tion for knowledge models aimed at the creation of digital twins. Thus, through this type
of experience, this transformation aims to promote the register of geotechnical information
from public institutions, which could lead to its publication and display, and hence enhance
the density of points and the development of maps with greater accuracy on an urban and
metropolitan scale.
Finally, this work suggests future research directions, such as automating processes
through geospatial database systems like PostgreSQL, incorporating the voxel format into
3D model generation, and integrating them into built environments via CityGML.
Author Contributions:
All the authors of this publication have collectively contributed to the de-
velopment of the published version of this manuscript. All authors have read and agreed to the
published version of the manuscript.
Funding:
The research and writing of this article were supported by a Grant for the International
Mobility of Research Staff from the VII Research Plan of the University of Seville for 2022 (VI
PPI-US, 2022).
Data Availability Statement: Data are contained within the article.
Remote Sens. 2024,16, 141 19 of 21
Acknowledgments:
The authors wish to acknowledge the necessary support from the IUACC
(Instituto Universitario de Arquitectura y Ciencias de la Construcción) for the development of this research.
Conflicts of Interest:
The authors declare there to be no conflicts of interest. The funders played no
role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of
this manuscript; or in the decision to publish the results.
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