Validation of regional positioning service user connections as support in update cartography

1 of 16 Transactions in GIS, 2025; 29:e13293 https://doi.org/10.1111/tgis.13293 Transactions in GIS RESEARCH ARTICLE OPEN ACCESS Validation of Regional Positioning Service User Connections as Support in Update Cartography CristinaTorrecillas1 | FranciscoJ.RamosSánchez1 | CarmenMarínBuzón1 | AmparoVerdúVázquez2 1Universidad de Sevilla, Seville, Spain | 2Universidad Politécnica de Madrid, Madrid, Spain Correspondence: Francisco J. RamosSánchez ([email protected]) Received: 12 March 2024 | Revised: 29 October 2024 | Accepted: 4 December 2024 Keywords: cartographic update| GBAS| public works| RAP| territorial changes| urban development ABSTRACT The significant demand for highly updated geospatial or cartographic information surpasses the capacity of the official agencies responsible for its generation. The procedure for updating official cartography, in some Spanish regions, can take more than three or 4 years. This long process depends to a large extent on the national planning of aerial flights, but also on actions linked to the updates of changes in their geodatabases. There are alternative noncartographic sources of geoinformation that can locate these variations more quickly than waiting for triannual orthophoto generation and reduce these times. This article focusses on a potential option derived from the location of users connections to a regional differential position corrections transmission system in Andalusia (Spain) used by surveyors. Three data selection studies are presented to evaluate this source for the detection of new or altered construction, covering the period 2008–2016. The results show a 95% success rate for new developments, 87% for new linear public works, and 72% for new urban services or facilities, validating its potential usefulness for updating cartography. The main advantages of this source lie in the speed of detection of an area under construction and in the definition of a preliminary boundary. The analysis of temporal statistics from this source, filtered by construction activities, can also be useful in targeting and prioritizing the mapping planning of public agencies and improving the effectiveness of false positive imagetoimage change detection, reducing the time to update the official cartography. 1 | Introduction Official cartography is produced by public institutions on various territorial scales, including municipal, regional, and national levels. Its application in geospatial studies, civil engineering, urban and territorial planning, construction projects, web mapping, or locationbased services requires the use of the update products to provide correct information and solutions that closely reflect reality and to avoid errors stemming from outdated information (Chuang, Chang, and Lee 2018; Soares Nascimento etal.2023). Nowadays the cartographic update processes could be classified based on various factors such as the type of data source, the level of automation, and the specific techniques used for detecting and updating changes, see Figure1. In the elaboration of territorial official cartography, the main sources are: (i) orthophotos by aerial photogrammetry using light aircraft (Holland etal.2008; Mikhail, Bethel, and McGlone2001) or Remotely Piloted Aircraft Systems (RPAS) in small or mediumsized areas (Alves Júnior etal.2018; Barry and Coakley2013; Caroti, Piemonte, and Nespoli 2017; Colomina and Molina2014), (ii) very high spatial resolution (VHR) satellite images (Aguilar, del Saldaña, and Aguilar2013; Åstrand etal.2012); as well as (iii) Light Detection and Ranging (LiDAR) flights to capture precise 3D model (Aijazi, Checchin, and Trassoudaine2013; Hodgson et al. 2003). Ground surveys based on Global Navigation Satellite Systems (GNSS), Total Station, Mobile or Static Laser Scan are not suitable techniques for large areas because the level of scale required is usually no greater than 1:5 k and these This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). Transactions in GIS published by John Wiley & Sons Ltd. 2 of 16 Transactions in GIS, 2025 techniques offer higher scale detail (Ordóñez, Cabo, and SanzAblanedo 2017). Crowdsourced data, obtained through social cartography platforms like OpenStreetMap (https:// www. opens treet map. org/ accessed 5 February 2024) or Volunteered Geographic Information (VGI) (Hachmann, Jokar Arsanjani, and Vaz2018; Harvey2013), can play a vital role in mapping areas lacking the necessary financial resources. However, they lack the official support required for use in urban or territorial planning, or in the construction of public infrastructure. In terms of automation, while manual updates are still performed by cartographers through digitizing new features or modifying existing ones, semiautomated and fully automated processes have been developed to assist with these tasks. Most proprietary software (e.g., Bentley ContextCapture, Trimble Inpho, Pix4D, or ERDAS Imagine) offers sophisticated algorithms and automation capabilities that leverage advanced photogrammetry techniques (Casella etal.2020), such as image matching, bundle adjustment (Jiang, Jiang, and Wang2022; Matthew David Frank Tucsok2023), accurately rectify and stitch together images captured from different perspectives (Clapuyt, Vanacker, and Van Oost 2016; Pikelj et al. 2015) and true orthophoto generation (Gharibi and Habib 2018). These tools enable precise delineation—manual, semiautomated, or automated—of features such as roads, buildings, or rivers, primarily utilizing edge detection techniques and increasingly incorporating deep neural networks (Bakhtiari, Abdollahi, and Rezaeian 2017; Nunes, Medeiros, and de Dos Santos2018; Ye and Yilmaz2017). Additionally, some software includes update function based on change detection from images captured at different times, allowing for the identification of construction changes, ground elevation modifications, or variations in land use. These tasks rely on differences between supervised or unsupervised classification (Knudsen and Olsen2003; Mas1999; Pande etal.2018) or Machine Learning (ML) approaches (Aijazi, Checchin, and Trassoudaine 2013; Ceresola et al. 2005; Jung 2004; Kalantar etal.2020; Knudsen and Olsen2003; Niederöst2001; Sarp et al. 2014) preferably within a GIS environment (Weis etal.2005). Such methods are also highly effective for assessing areas affected by natural disasters like earthquakes, fires, or floods (Calantropio etal.2021; Kalantar etal.2020). However, all these methods depend on imagery and face challenges with false positives, particularly in cases where obscured elements— such as rooftops covered by trees, variations in roof materials, or road rehabilitation instead of new construction—complicate the detection process (Jamali, Kumar, and Rahman2019; Jifroudi etal.2022; Karsli etal.2024). Beyond these imageryrelated issues, the official cartographic updates require highresolution images, which are often not compatible with free satellite imagery, leading to additional cost for aerial flights or purchasing satellite imagery from private companies. In this way, obtaining precise annual images is a luxury that many countries find difficult to afford. In Spain, for example, this process typically takes around three to 4 years, as dictated by the National Aerial Photography Plan (PNOA, in Spanish Plan Nacional de Ortofotografía Aérea) and the annual flight area allocation (Martínez etal.2015). During this waiting period for new aerial images, additional time must be factored in for orthophoto generation and the subsequent mapping processes to update cartographic databases, tasks that are far from simple. Although the elimination of paperbased cartographic sheet production, alongside the introduction of digital formats such as GeoPDF or GIS/CAD vector files, and the adoption of versioned, continuous, and thematically organized layers have reduced update times (Peerbocus, Jomier, and Badard2002), the overall duration is still contingent on public cartographic agencies and can take several years due to territorial extension and revision tasks. The official cartographic updates primarily involve the identification of artificial alterations, most commonly observed in metropolitan areas and attributable to human activities. These modifications include the construction of new buildings, residential homes, shopping centers, and public infrastructure such as roads, railways, or solar power plants. While such constructions are critical, the conventional method for detecting them often relies on waiting for the next photogrammetric flight. Emerging approaches offer the potential for faster detection by nonconventional cartographic sources that can help identify areas where changes are likely to occur, thus aiding in the prioritization of image searches or updates. For instance, information accessible through tender announcements on the web or advertisements in the press, as suggested by initiatives like CARTOBOT—used by the Spanish National Cartographic Institute—(Asensio etal.2019). CARTOBOT has been particularly effective in road detection, though it mainly provides definitions of connecting segments between localities or kilometer points, which still require geolocation and digitization. Additionally, the Spanish Cadastral Office offers relatively updated parcel and building information, thanks to a registration requirement that mandates digital contour submission by project developers. However, this source is inherently limited, focusing only on specific geographic entities, excluding others like roads, which are not subject to public taxation. FIGURE 1 | Classification of cartographic update methodologies. 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3 of 16 This paper introduces a novel cartographic update methodology based on a nonconventional source of information to detect urban or territorial artificial changes caused by human activity. The new input data will be sourced from a GroundBased Augmentation System (GBAS), which is used to obtain highprecision positioning in real time with GNSS (Teunissen and Montenbruck2021). GBAS has a worldwide presence, typically supported by public or private entities. An example of a private network with global coverage is the GBAS HxGN SmartNet NTRIP, operated by Hexagon company (https:// hxgns martn et. com/ cover agemap, accessed 2 October 2024). These services require user location to provide better position correction and are widely used by surveyors at construction sites. Areas where GBAS usage is intense over several days or months are often indicative of potential construction activities, making this a useful indicator for identifying regions where cartographic updates may be needed. 2 | Testing Area Andalusia is the southernmost autonomous community in mainland Spain and the second largest with 87,600 km2 (see Figure 2a). The Andalusian Statistical and Cartographic Institute (IECA, in Spanish Instituto de Estadística y Cartografía de Andalucía) is the official institution responsible for producing official cartography on a scale of 1:10k scale, which constitutes its main output. The metadata for the latest PDF digital sheet num. 098422 at 1:10k (where 0984 is the 1:50k reference, see Figure2b, and 22 denotes the column and row following the grid in Figure2c), indicates a flight year of 2013, but the restitution process was not completed until 2018. To illustrate how these timeframes impact the inclusion of new constructions in Andalusia, consider the case of the Lagoh shopping center in Seville city (37.34162° N, 5.98696° W, IECA sheet num. 098422 in Figures2c and 3 in detail). This commercial area was inaugurated in September 2019, and while Google Maps (GM) had already labeled the center months before its opening, its vector data is still absent as of February 2024 (see cartography background in Figure3a). Until the end of 2023, a 2019 Pleiades satellite image displayed the construction process in the spring season on GM (Figure3b). The geometric definition of the boundary remains absent in the online Geospatial Data Infrastructure of the Seville city council, at 1:500 scale, as of January 2024 (Figure3c). Furthermore, the latest orthophoto from PNOA in Andalusia, captured in summer of 2022 (PNOA flight status: https:// pnoa. ign. es/ web/ portal/ pnoaimagen/ estad odelosvuelos, accessed 5 October 2024), and depicted in Figure3d, was added to the PNOA Web Map Service (PNOA WMS: ign. es/ wmsinspi re/ pnoama, accessed 5 February 2024) in August 2023, 14 months after the flight, making one of the best turnaround times currently achieved for the PNOA project. IECA is still working on the 1:10k cartographic update derived from the previous 2019 PNOA flight, meaning this area has not been updated since 2018 (https:// ws089. junta deand alucia. es/ insti tutod eesta disti cayca rtogr afia/ blog/ tag/ desca rga/ accessed 5 February 2024). The delay in integrating or updating new cartographic entities in the digital databases accessible to users, such as the absence of a major shopping center as a key focal point, could potentially affect mobility studies, geospatial statistics, and other applications. This construction had used a GBAS system, making it worthwhile to explore this nontraditional source of information and test whether it could help in locating construction change. 3 | Input Data RAP (in Spanish, Red Andaluza de Posicionamiento) is the Andalusian GBAS used to obtain highprecision positioning for GNSS instruments (Páez etal.2017). RAP consists of a GNSS network of 22 multiconstellation stations that provide free differential positioning correction services (Garrido et al. 2012). This network is one of the several differential correction FIGURE 2 | (a) Location of Andalusia (red polygon) in World Mercator projection (EPSG:3395) in ESRI Gray (light) XYZ tile map service; (b) distribution of 1:50k cartographic sheets from DERA product (IECA2016) highlighting in red the sheet no.0984 of Seville city on the Mean depth Web Map Service from EMODnet release 2020 (http:// ows. emodn etbathy metry. eu/ ows, accessed 5 February 2024) in UTM 30 projection (EPSG 25830) using a geographical grid; and (c) Detail of the 1:10k numbering grid of cartographic sheet num. 0984 (EPSG 25830). 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4 of 16 Transactions in GIS, 2025 infrastructure that uniformly cover the entire Spanish territory, as shown in Figure4. IECA has been the public institution responsible for overseeing the RAP network since its inception in mid2008. Among the services offered by RAP are various GNSS corrections for realtime positioning, with the most accurate corrections requiring the user's coordinates upon first contact with the service. These corrections enable centimeterlevel precision, and their use was widespread, with more than 100,000 geolocated connections per year in the early years. However, usage declined following the 2008 economic crisis (FernándezTabales and Cruz2013), before rebounding to 285,145 connections in 2019 and 407,898 connections in 2020, as shown in Table1. A 2015 study revealed that nearly 70% of RAP users belonged to the construction sector, using the service for applications such as new road and rail construction or urban development projects—including new urbanizations, shopping centers, or industrial areas—as well as some maintenance works (Páez et al. 2017). Using the land use layer defined by the SIOSE project in 2011 (Zaragozí etal.2020), as the average year between 2008 and 2015, and coinciding with the closest fouryear version of the product, we analyzed RAP user connections by classifying them into 10 categories, as shown in Figure5. The categories “Agricultural Areas”, “Urban Areas”, and “Roads and Rail Networks” show the highest values. Most categories exhibit a stable trend during this period, except for the “Road and Rail Network” category, which shows an unexplained anomalous value in 2010. The significant increase in the number of connections in recent years (2017–2020), as indicated in the “Original connections” field in Table 1, appears to coincide with a broader range of RAP usage, as observed in Páez et al. (2017), revealing connections in precision agriculture, river or coastal navigation, and smallerscale construction projects such as houses or water ponds for irrigation (Garrido etal.2019; Páez etal.2017). Figure 6 illustrates the RAP connection points for two distinct periods: 2008–2016 and 2017–2021. Figure6a reveals the presence of linear patterns over the territory and focal points predominantly located in the major cities of Andalusia, while Figure6b highlights a significant increase in RAP connections during the latter fouryear period (2017–2021) compared to the previous span (2008–2016). Focusing on urban areas at a detailed scale, Figure7 illustrates the RAP connections in two different scenarios. The first row is the Lagoh shopping center in Seville, as commented in the “Testing area” section, depicted with different PNOA orthophotos: Figure7a presents the 2016 orthophoto, captured 1 year before the start of construction, and is accompanied by cadastral vector information updated to 2022; Figure7b showcases the colored annual connections in that FIGURE 3 | Lagoh commercial area in Seville city from different map services: (a) GM default view on 5 February 2024, (b) GM satellite view on 12 November 2022, (c) Seville Spatial Data Infrastructure website (http:// sig. urban ismos evilla. org/ visor gis/ geoSe villa. asp, accessed on 5 February 2024) and (d) 2022 PNOA from WMS service, included in GM in January 2024 (WMS: https:// www. ign. es/ wmsinspi re/ pnoama? SERVI CE= WMS& , accessed 5 February 2024). FIGURE 4 | The Spanish GBAS network with Andalusian RAP stations in green triangle with black point inside (GNSS Viewer: http:// ntrip. repgnss. es/ , accessed 15 October 2024). 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 5 of 16 area, starting from the initiation of construction works in 2017 (blue dots), increasing the intensity within the construction zone in 2018 (green), and focusing on access roads in 2019 (yellow dots), coinciding with the year of its inauguration. Some connections were noted in 2020, outlining the building and possibly associated with completion works (orange dots). The second row, Figure7c,d, depict the post2016 demolition of a detached house on a plot that was replaced by the construction of terraced houses. This transformation is precisely contoured with the RAP connections from 2019 to 2020, as shown in the 2022 orthophoto. These RAP user connections offer several advantages: • Geolocation: Each connection is geolocalized, with the RAP control center storing users connection coordinates, which are updated every 30 min or upon first connection or reconnection. • Temporality: The system records the day and time of each connection, which can indicate the potential start of work activities (even before any initial actions) and the potential end of these activities, as connections in the area diminish. • Zoning: The connections are tailored to the changing environment, serving as an approximate perimeter of the work zone. Nevertheless, there are drawbacks, including the current unavailability of user locations connecting to other services (such as the national service offered by the Spanish National Geographic Institute or the HxGN SmartNet network, among others) and a percentage of users (13% in 2017) who utilize a different type of correction that does not transmit their location to the RAP server. Table2 summarizes the attributes of the input data used and provided by IECA. TABLE 1 | RAP users' connections for period 2008–2020, both original and filtered (note: original connections from 2014 to 2016 are missing), with format and stored period. Year Original connections Filtered connections File format Period 2008 25,713 1842 Log file (CSV) Month 2009 106,496 76,003 Log file (CSV) Month 2010 88,092 61,367 Log file (CSV) Month 2011 74,239 54,406 Log file (CSV) Month 2012 49,795 33,826 Log file (CSV) Month 2013 52,796 37,031 Log file (CSV) Month 2014 —35,858 Log file (CSV) Month 2015 —36,167 Log file (CSV) Month 2016 —48,262 Log file (CSV) Month 2017 106,551 GeopackageaYear 2018 150,955 Geopackage Year 2019 285,145 Geopackage Year 2020 407,898 Geopackage Year aThe input format has changed from the initial monthly CSV log files to the latest annual files in Geopackpage GIS format, as shown in this table. FIGURE 5 | RAP users' connections for 2009–2015 in contrast to land use from the 2011 SIOSE project (Zaragozí etal.2020). 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6 of 16 Transactions in GIS, 2025 4 | Methodology To validate the use of RAP connections in cartographic update, three case studies were conducted focusing on two different types of construction projects, both linked to the geometry of the construction: the first centered on linear construction, and the other two on both urban and nonurban polygonal construction. Only data from 2008 to 2016 was used to ensure the availability of finalized official cartographic products. The cartographic reference for the initial situation in 2008 was the digital product called DEA100, a DVDROM containing a directory of cartographic layers derived from the 1:10k cartography with a planimetric precision of 3 m (IECA2009). The final cartographic situation was based on the DERA product, the evolution of DEA100, which is an accessible online resource providing the most uptodate version—without historical versioning—a copy was downloaded in 2016 (IECA2016; MorenoNavarro, LópezMagán, and AuzAramillo2021). FIGURE 6 | Overlapping RAP users' connections: (a) 2008–2016 with the 12 selected municipalities in Urban Polygonal Works study (red outline polygons and labels), and (b) with the eight province delimitations of Andalusia (black outline polygons and labels) including Seville (in red) as the test area in Nonurban Polygonal Works study. Maps in EPSG 25830 with geographical grid and ESRI Gray (light) XYZ tile service as background. 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 7 of 16 A GIS environment was required, utilizing QGIS 3.16 (QGIS.org2024) and ArcGIS Pro 9 (ESRI2024), along with formats compatible with these platforms (Shapefile, personal geodatabase, and Geopackage). All vector data was reprojected to Universal Transverse Mercator Zone 30 in the European Terrestrial Reference System 1989 (EPSG 25830), and any RAP points located outside of Andalusia were removed using a 1 km polygon buffer to ensure the elimination of any crossborder or maritime navigation actions. After this preliminary step, each study began with the selection of valid points, which were then transformed using GIS processes to generate line or polygon layers representing potential artificial changes. These were contrasted with IECA data (DEA and DERA products) and more recent sources, such as PNOA orthophotos since 2008, Sentinel 2 images starting from the beginning of this constellation in 2015, and road scheme data provided by the Department of Development, Regional Coordination, and Housing of the Regional Government of Andalusia for 2008–2016 (Road inventory), as shown in Figure8. A model builder was used to apply the methodology annually for each study. 4.1 | Identification of Linear Public Works In Figure6a, RAP connections from 2008 to 2013 reveal clear linear sequences of point connections, which could represent large public works such as roads, motorways, railways, or pipeline (water, power, oil, or gas). The challenge of this study is to transform a selection of RAP points into lines. A methodology was developed for this purpose, starting with several screening processes, as outlined in Figure 9, specifically following these steps: • Delete 2008 urban points: Since large linear public works are typically not located in consolidated urban areas, points within a 500 m buffer, using a urban layer defined in 2008 in the DEA100 product, were removed. • Delete isolated points: Point isolated by less than 200 m were excluded. This threshold avoids removing points associated with linear public works, such as power line overhauls, where pylons may be up to 100 m apart. In addition to this, public work implies longterm topographic work, so that the elimination of these points avoids oneoff data collection jobs, which usually last one day (e.g., plot measurement). • Delete points over existing entities: Points intersecting with preexisting linear works in DEA100 (e.g., cycle paths, power lines, motorways) were removed using buffers of 5–50 m depending on the type. This excluded improvements like road resurfacing but preserved changes involving new accesses or roundabout construction. FIGURE 7 | Above, construction of the Lagoh shopping center in Seville: (a) the previous situation, 2016 PNOA with cadastral information of 2022 (red outline polygons) and (b) the 2024 situation, with 2022 PNOA and yearly RAP connections 2017–2020 overlapped. Below, modification of a plot: (c) a detached house in 2016 PNOA and (d) the new construction of terraced houses in 2022 PNOA with RAP connections from 2019 to 2020. TABLE 2 | Information on RAP user connections. Field name Description Id Identifier, primary key User/UserNameaRAP username (acronym) StartDate Start date of connection StartTime Start time of connection EndDate Start date of connection EndTime Start time of connection Duration Connection duration Product/ProductNameaType of correction Lat Geographic latitude in ETRS89 Long Geographic longitude in ETRS89 Height Orthometric height aSome fields changed names between years, so they have two names. 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8 of 16 Transactions in GIS, 2025 • Transform points into valid lines: This step involved seven GIS processes to create lines representing the connection envelope's skeleton. While multiple methods were tested, rasterizing the data using the “ID” field (from Table2) and a 100 m cell size yielded better geometric results. After raster conversion, a “Majority filter” (8 pixels) and “Thins” geoprocessing were applied to simplify the data. Polylines were then generated via “Raster to polyline”, extending line segments to the first intersecting feature within 1500 m by using an “Extend line” and a “Snap” processes. Lines shorter than 1000 m were deleted, leaving significant new linear works (top right image, Figure9). • Intersection with new 2008–2016 entities: These public works were verified against new elements from DEA100 and DERA layers (2008–2016). Given that the DERA features data from 2014 in the case of roads, as indicated by its metadata, this was supplemented with records from the Department of Development, Regional Coordination and Housing of the Regional Government of Andalusia (Road inventory). Buffers of 200 m were created to intersect with each layer of new linear works identified by the methodology. • External verification: The final step involved visual confirmation by comparing old and recent PNOA flights. 4.2 | Identification of Polygonal Works The second and third case studies focused on polygonal works, with an emphasis on large construction projects. These projects are typically found in expanding cities serving purposes such as new housing development, shopping centers, or industrial estates. However, there is also civil work outside of cities, such as the construction of water pond or solar farms. Due to the differing nature of these projects, studies were conducted both in urban areas (12 municipalities) and nonurban areas (province of Seville). The urban study selected municipalities of varying sizes (Figure6a), characterized by population growth that could drive urban expansion, particularly through housing construction. The methodology for this study is outlined in Figure10 (dark blue boxes). Specifically: • Select 2016 urban points: Point connections from defined urban areas were extracted using the urban layer from the 2016 DERA product. • Select municipalities: Twelve municipalities of varying sizes, from small villages to major cities, were selected based on population growth (Table3), which could indicate potential new housing construction. • Land use identification: The 2011 SIOSE project layer was used to identify land use in these municipalities at the time. • Delete linear works: Points identified as roads or railways (linear public works) were discarded. • Select 2008–2016 urban growth: Urban area expansion was identified as the difference between the urban boundaries of the DEA100 and DERA layers, narrowing the focus of the study. FIGURE 8 | General methodology with the three detailed studies, highlighting their main differences in input data and processes: The linear Public Works study (orange boxes), Nonurban Polygonal Works study (light blue) and Urban Polygonal Works study (dark blue). FIGURE 9 | Detail methodology for the identification of linear civil works in 2008–2016 from RAP users' connections. 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 9 of 16 • External verification: The remaining unclassified points, after removing linear works, were analyzed by comparing PNOA orthophotos from 2007 and from 2010 (not completed flight) with the complete flight from 2016. All points were reviewed, with particular attention to urban expansion areas. Points were classified into four categories: Plots, Urban Blocks, Services/Facilities, Urbanizations, and Others. Examples of these categories can be seen in Figure11. These are mainly linked to housing, which is expected to be the primary construction activity in these growing municipalities. The nonurban polygon works study focused on the province of Seville (Figure6b) and followed the methodology outlined in Figure10 (light blue boxes): • Delete 2008 urban points: The first step was to eliminate point connections within urban areas as defined in 2008. • Remove existing linear infrastructure: Point connections intersecting with existing linear infrastructure, such as roads, railways, or pipelines from 2008, were removed. • Multipoint grouping: To identify point connections related to the same construction work, they were grouped by truncated geographic coordinates and the user “ID” field, producing a multipoint layer. • Polygon generation: A convex hull was defined for each work site, enabling the premapping of the relevant area of change (see top left of Figure10). • New fields: Additional information, such as the area of the convex hull and the number of connections within each polygon, was added as new fields. • Select Seville province: Given the diverse types of works and the large number of polygons to check, only polygon within the province of Seville were selected and reviewed individually to identify the type of construction (see top right of Figure10). • External verification: To determine the potential start and end dates of construction based on the presence of RAP points, all PNOA flights were reviewed. For works initiated after 2015, Sentinel 2 images from the European Space Agency's Copernicus program were also analyzed. The 2019 PNOA data was not available at the time of this study. 5 | Results and Discussion 5.1 | Linear Public Works Table4 presents the results obtained by comparing the new linear mappings, defined by the differences between DEA100 (2008) and DERA (2016), with the areas identified using the described methodology. It is important to note that these works are significant infrastructure projects spanning tens of kilometers, so while the numbers in Table4 may appear small, they represent substantial areas. Additionally, some locations include several converging constructions at the same junction (e.g., intersecting new power lines and new roads). This issue was resolved by using the input layer for each type before raster conversion and identifying the points for each construction using 250m buffers. Typically, such constructions have more than 30 RAP point connections, so two or three points that may have crossed and corresponded to a different entity from the selected ones were not a significant issue. FIGURE 10 | Detailed methodology for the identification of nonurban and urban polygonal works from RAP point connections between 2008 and 2016. 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 16 of 16 Transactions in GIS, 2025 EarthquakeInduced Building Damage Detection Based on Preand PostEvent Orthophoto Images.” Remote Sensing 12, no. 21: 3529. https:// doi. org/ 10. 3390/ RS122 13529 . Karsli, B., F. Yilmazturk, M. Bahadir, F. Karsli, and E. Ozdemir. 2024. “Automatic Building Footprint Extraction From Photogrammetric and LiDAR Point Clouds Using a Novel ImprovedOctree Approach.” Journal of Building Engineering 82: 108281. https:// doi. org/ 10. 1016/J. JOBE. 2023. 108281. Knudsen, T., and B. P. Olsen. 2003. “Automated Change Detection for Updates of Digital Map Databases.” Photogrammetric Engineering and Remote Sensing 69, no. 11: 1289–1296. https:// doi. org/ 10. 14358/ PERS. 69. 11. 1289. Martínez, S. L., J. Carlos, O. Manrique, B. Rodríguezcuenca, and E. González. 2015. “Procesado y distribución de Nubes de Puntos en el Proyecto PNOALiDAR.” 2015(1), 1–4. Mas, J. F. 1999. “Monitoring LandCover Changes: A Comparison of Change Detection Techniques.” International Journal of Remote Sensing 20, no. 1: 139–152. https:// doi. org/ 10. 1080/ 01431 16992 13659 . Mikhail, E. M., J. S. Bethel, and J. C. McGlone. 2001. Introduction to Modern Photogrammetry. New York, NY: Wiley. MorenoNavarro, J. G., E. LópezMagán, and L. AuzAramillo. 2021. “The Evolution of Connectivity in sigT Modeling. The Case of the Spatial Data Infrastructure of Andalusia.” Transportation Research Procedia 55: 1381–1388. https:// doi. org/ 10. 1016/j. trpro. 2021. 07. 123. Niederöst, M. 2001. “Automated update of building information in maps using mediumscale imagery (1:15000).” https:// doi. org/ 10. 3929/ ethza00433 2595. Nunes, D. M., N. D. G. Medeiros, and A. P. de Dos Santos. 2018. “SemiAutomatic Road Network Extraction From Digital Images Using ObjectBased Classification and Morphological Operators.” Boletim de Ciencias Geodesicas 24, no. 4: 485–502. https:// doi. org/ 10. 1590/ S1982 - 21702 01800 0400030. Ordóñez, C., C. Cabo, and E. SanzAblanedo. 2017. “Automatic Detection and Classification of PoleLike Objects for Urban Cartography Using Mobile Laser Scanning Data.” Sensors 17, no. 7: 1465. https:// doi. org/ 10. 3390/ S1707 1465. Páez, R., C. Torrecillas, I. Barbero, and M. Berrocoso. 2017. “Regional Positioning Services as Economic and Construction Activity Indicators: The Case Study of Andalusian Positioning Network (Southern Spain).” Geocarto International 32: 44–58. https:// doi. org/ 10. 1080/ 10106 049. 2015. 1120358. Pande, C. B., K. N. Moharir, S. F. R. Khadri, and S. Patil. 2018. “Study of Land Use Classification in an Arid Region Using Multispectral Satellite Images.” Applied Water Science 8, no. 5: 1–11. https:// doi. org/ 10. 1007/ S1320 101807640. Peerbocus, A., G. Jomier, and T. Badard. 2002. “A Methodology for Updating Geographic Databases Using Map Versions.” Advances in Spatial Data Handling pp, 363–376. https:// doi. org/ 10. 1007/ 978364256094 - 1_ 27. Pikelj, K., S. Ilić, M. R. James, and B. Kordić. 2015. “Application of the SfM Photogrammetry for Morphological Changes in Various Beach Environments: Dugi Rat Case Study (Croatia).” Ferrare, Italie 3: 67–72. https:// doi. org/ 10. 5150/ cmcm. 2015. 014. QGIS.org. 2024. “QGIS Geographic Information System.” Open Source Geospatial Foundation Project. http:// Qgis. Org. Sarp, G., A. Erener, S. Duzgun, and K. Sahin. 2014. “An Approach for Detection of Buildings and Changes in Buildings Using Orthophotos and Point Clouds: A Case Study of Van erciş Earthquake.” European Journal of Remote Sensing 47, no. 1: 627–642. https:// doi. org/ 10. 5721/ EuJRS 20144735. Soares Nascimento, E., A. Alves, L. Ferreira, etal. 2023. “Cartographic Features Extraction Methodology in Remote Sensing Images Aiming at the Cartographic Update of Allotments.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1-W1-2023, 851–856. https:// doi. org/ 10. 5194/ ISPRS - ANNAL SX1W120238512023. Teunissen, P. J. G., and O. Montenbruck. 2021. “Handbook of Global Navigation Satellite Systems.” In Springer Handbook of Global Navigation Satellite Systems, edited by P. J. G. Teunissen and O. Montenbruck. Cham, Switzerland: Springer International Publishing. https:// doi. org/ 10. 1007/ 978331942928 - 1/ COVER . Tucsok, M. D. F. 2023. “Enhancing the Aerial Imagebased Photogrammetric Pipeline Through the Integration of Existing and Novel Deep Neural Networks.” Weis, M., S. Müller, C. E. Liedtke, and M. Pahl. 2005. “A Framework for GIS and Imagery Data Fusion in Support of Cartographic Updating.” Information Fusion 6, no. 4: 311–317. https:// doi. org/ 10. 1016/J. INFFUS. 2004. 08. 001. Ye, Y., and A. Yilmaz. 2017. “An Automatic Pipeline for Mapping Roads from Aerial Images.” https:// doi. org/ 10. 1145/ 31490 92. 3149096. Proceedings of the 1st ACM SIGSPATIAL Workshop on HighPrecision Maps and Intelligent Applications for Autonomous Vehicles - AutonomousGIS ‘17, 1–4. Zaragozí, B., J. Tomàs NavarroCarrión, J. RodríguezSala, S. Trilles, and A. RamónMorte. 2020. “Improving the Usability of the Land Cover and Use Information System of Spain (SIOSE): A Proposal to Distribute New Thematic Layers and Predefined Reclassifications.” https:// doi. org/ 10. 5220/ 00095 79502 940301. 14679671, 2025, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/tgis.13293 by Readcube (Labtiva Inc.), Wiley Online Library on [03/01/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License