Evaluation of open geotechnical knowledge in urban environments for 3D modelling of the city of Seville (Spain)

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 fourphase 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 emergency [ 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 require 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. Unfortunately, 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 region, 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 contamination [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 RemoteSens.2024,16,xFORPEERREVIEW4of22    Figure1.Locationmapoftheurbanstudyareaandmaingeologicalareas. 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1displays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-ex- istences,thepresenceoflargetreeroots,andundergroundinfrastructures. 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] 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 Foundations 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 Transverse Mercator (UTM) coordinates and geographic coordinates, and no implementation 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 subsoil 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 RemoteSens.2024,16,xFORPEERREVIEW8of22    Figure2.Methodologicalandmainresearchandmethodologyphases. 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, georeferencing 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 incorporated 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 RemoteSens.2024,16,xFORPEERREVIEW10of22   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].  Figure3.Groundlayerdiagramandselectionofrepresentativeparametersformodelling. 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4showsinthenextmethodologicalstep. 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). Figure 3. Ground layer diagram and selection of representative parameters for modelling. RemoteSens.2024,16,xFORPEERREVIEW11of22   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km2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/km2,whichoptimisesthenumberofcellsthatdisplaythe maximumdensityvalues.  Figure4.Mappingareaselectionfromdensityanddistributionanalysis.I Figure 4. Mapping area selection from density and distribution analysis. Remote Sens. 2024,16, 141 16 of 21 linear depth distribution of the boreholes. Additionally, Figure 9b shows the geometric layer distribution. RemoteSens.2024,16,xFORPEERREVIEW17of22   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 lineardepthdistributionoftheboreholes.Additionally,Figure9bshowsthegeometric layerdistribution. Bothillustrationsdemonstratehowurbansubsoilmodelshavethepotentialtointegrate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producing3Dmodelsectionsusingvariousaxesandpolygonallines.  Figure9.Underground3Dmodelofthecasestudywith(a)boreholesand(b)soillayers. 4.Discussion Fromtheresultsobtained,thefollowinggeostatisticalandgeotechnicalinterpretationscanbemade. 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 integrate 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 producing 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 information 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 development 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. References 1. Gao, J.; O’Neill, B.C. 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