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Three-Dimensional Multitemporal Game Engine Visualizations for Watershed Analysis, Lighting Simulation, and Change Detection in Built Environments
Assessing Accessibility and Equity in Childcare Facilities Through 2SFCA: Insights from Housing Types in Seongbuk-gu, Seoul
Exploring Unconventional 3D Geovisualization Methods for Land Suitability Assessment: A Case Study of Jihlava City

Journal Description

ISPRS International Journal of Geo-Information

ISPRS International Journal of Geo-Information is an international, peer-reviewed, open access journal on geo-information. The journal is owned by the International Society for Photogrammetry and Remote Sensing (ISPRS) and is published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), GeoRef, PubAg, dblp, Astrophysics Data System, Inspec, and other databases.
Journal Rank: JCR - Q2 (Geography, Physical) / CiteScore - Q1 (Earth and Planetary Sciences (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 34.2 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the first half of 2025).
Rejection Rate: a rejection rate of 76% in 2024.
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.8 (2024); 5-Year Impact Factor: 3.3 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2220-9964

Latest Articles

22 pages, 7528 KB

Open AccessArticle

ADAImpact Tool: Toward a European Ground Motion Impact Map

by Nelson Mileu, Anna Barra, Pablo Ezquerro, Sérgio C. Oliveira, Ricardo A. C. Garcia, Raquel Melo, Pedro Pinto Santos, Marta Béjar-Pizarro, Oriol Monserrat and José Luís Zêzere

ISPRS Int. J. Geo-Inf. 2025, 14(10), 389; https://doi.org/10.3390/ijgi14100389 - 6 Oct 2025

This article presents the ADAImpact tool, a QGIS plugin designed to assess the potential impacts of geohazards—such as landslides, subsidence, and sinkholes—using open-access surface displacement data from the European Ground Motion Service (EGMS), which is based on Sentinel-1 satellite observations. Created as part [...] Read more.

This article presents the ADAImpact tool, a QGIS plugin designed to assess the potential impacts of geohazards—such as landslides, subsidence, and sinkholes—using open-access surface displacement data from the European Ground Motion Service (EGMS), which is based on Sentinel-1 satellite observations. Created as part of the European RASTOOL project, ADAImpact integrates InSAR-derived ground movement data with exposure datasets (including population, infrastructure, and buildings) to support civil protection agencies in conducting risk assessments and planning emergency responses. The tool combines “Process Magnitude”, with “Exposure” metrics, quantifying the population and critical infrastructure affected, to generate potential impact maps for ground motion hazards. When applied to case studies along the Portugal–Spain border and the coastal region of Granada, Spain, ADAImpact successfully identified areas of high potential impact. These results underscore the tool’s utility in pre- and post-disaster assessment, highlighting its potential for scalability across Europe. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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20 pages, 2548 KB

Open AccessArticle

STAE-BiSSSM: A Traffic Flow Forecasting Model with High Parameter Effectiveness

by Duoliang Liu, Qiang Qu and Xuebo Chen

ISPRS Int. J. Geo-Inf. 2025, 14(10), 388; https://doi.org/10.3390/ijgi14100388 - 4 Oct 2025

Traffic flow forecasting plays a significant role in intelligent transportation systems (ITSs) and is instructive for traffic planning, management and control.Increasingly complex traffic conditions pose further challenges to the traffic flow forecasting. While improving the accuracy of model forecasting, the parameter effectiveness of [...] Read more.

Traffic flow forecasting plays a significant role in intelligent transportation systems (ITSs) and is instructive for traffic planning, management and control.Increasingly complex traffic conditions pose further challenges to the traffic flow forecasting. While improving the accuracy of model forecasting, the parameter effectiveness of the model is also an issue that cannot be ignored. In addition, existing traffic prediction models have failed to organically integrate data with well-designed model architectures. Therefore, to address the above two issues, we propose the STAE-BiSSSM model as a solution. STAE-BiSSSM consists of Spatio-Temporal Adaptive Embedding (STAE) and Bidirectional Selective State Space Model (BiSSSM), where STAE aims to process features to obtain richer spatio-temporal feature representations. BiSSSM is a novel structural design serving as an alternative to Transformer, capable of extracting patterns of traffic flow changes from both the forward and backward directions of time series with much fewer parameters. Comparative tests between baseline models and STAE-BiSSSM on five real-world datasets illustrates the advance performance of STAE-BiSSSM. This is especially so on METRLA and PeMSBAY datasets, compared with the SOTA model STAEformer. In the short-term forecasting task (horizon: 15min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 1.89%/13.74%, 3.72%/16.19% and 1.46%/17.39%, respectively. In the long-term forecasting task (horizon: 60min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 3.59%/13.83%, 7.26%/16.36% and 2.16%/15.65%, respectively. Full article

12 pages, 3911 KB

Open AccessArticle

Study Area Map Generator: A Web-Based Shiny Application for Generating Country-Level Study Area Maps for Scientific Publications

by Cesar Ivan Alvarez, Juan Gabriel Mollocana-Lara, Izar Sinde-González and Ana Claudia Teodoro

ISPRS Int. J. Geo-Inf. 2025, 14(10), 387; https://doi.org/10.3390/ijgi14100387 - 3 Oct 2025

The increasing demand for high-quality geospatial visualizations in scientific publications has highlighted the need for accessible and standardized tools that support reproducible research. Researchers from various disciplines—often without expertise in Geographic Information Systems (GIS)—frequently require a map figure to locate their study area. [...] Read more.

The increasing demand for high-quality geospatial visualizations in scientific publications has highlighted the need for accessible and standardized tools that support reproducible research. Researchers from various disciplines—often without expertise in Geographic Information Systems (GIS)—frequently require a map figure to locate their study area. This paper presents the Study Area Map Generator, a web-based application developed using Shiny for Python, designed to automate the creation of country- and city-level study area maps. The tool integrates geospatial data processing, cartographic rendering, and user-friendly customization features within a browser-based interface. It enables users—regardless of GIS proficiency—to generate publication-ready maps with customizable titles, basemaps, and inset views. A usability survey involving 92 participants from diverse professional and geographic-based backgrounds revealed high levels of satisfaction, ease of use, and perceived usefulness, with no significant differences across GIS experience levels. The application has already been adopted in academic and policy contexts, particularly in low-resource settings, demonstrating its potential to democratize access to cartographic tools. By aligning with open science principles and supporting reproducible workflows, the Study Area Map Generator contributes to more equitable and efficient scientific communication. The application is freely available online. Future developments include support for subnational units, thematic overlays, multilingual interfaces, and enhanced export options. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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27 pages, 14407 KB

Open AccessArticle

Exploring Factors Behind Weekday and Weekend Variations in Public Space Vitality in Traditional Villages, Using Wi-Fi Sensing Method

by Sheng Liu, Zhenni Zhu, Yichen Gao, Shanshan Wang and Yanchi Zhou

ISPRS Int. J. Geo-Inf. 2025, 14(10), 386; https://doi.org/10.3390/ijgi14100386 - 2 Oct 2025

With the rise in rural tourism, public space use has become more complex, causing significant weekday-weekend vitality imbalances. However, the factors shaping these dynamics in traditional villages remain unclear. This study uses Wi-Fi sensing method to analyze vitality variations across weekdays and weekends, [...] Read more.

With the rise in rural tourism, public space use has become more complex, causing significant weekday-weekend vitality imbalances. However, the factors shaping these dynamics in traditional villages remain unclear. This study uses Wi-Fi sensing method to analyze vitality variations across weekdays and weekends, and it develops a 13-metric evaluation framework to examine how built environment factors, from both internal and external dimensions, differentially influence the vitality of public spaces in traditional villages across various time periods. Using 17 public spaces in Yantou Village, Lishui, China, as a case, it finds: (1) Historical Element Proximity consistently and significantly drives public space vitality across all periods; (2) Leisure Facility Count and Decorative Element Count demonstrate strong positive effects during weekend morning peaks. (3) Retail Facility Count significantly reduces vitality during weekend morning peak but enhances it during midday off-peak, whereas Street Vendor Count shows the opposite pattern—increasing vitality in morning peak and decreasing it in midday off-peak. Using Wi-Fi sensing’s high-resolution, real-time, and non-invasive capabilities, this study provides a scientific method to accurately assess the variations in public space vitality and their impact factors between weekdays and weekends in traditional villages, offering technical support for enhancing public space vitality and sustainably revitalizing rural heritage. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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28 pages, 8858 KB

Open AccessArticle

A Scenario-Based Framework to Optimising Eco-Wellness Tourism Development and Creating Niche Markets: A Case Study of Ardabil, Iran

by Nasrin Kazemi, Zahra Taheri, Jamal Jokar Arsanjani and Mohammad Karimi Firozjaei

ISPRS Int. J. Geo-Inf. 2025, 14(10), 385; https://doi.org/10.3390/ijgi14100385 - 1 Oct 2025

Decision-making and planning in eco-wellness tourism can vary depending on time, resources, and the perspectives of stakeholders, as it is often challenging to generalize the results of decision-making models across different scenarios. Hence, the primary objective of this study was to propose a [...] Read more.

Decision-making and planning in eco-wellness tourism can vary depending on time, resources, and the perspectives of stakeholders, as it is often challenging to generalize the results of decision-making models across different scenarios. Hence, the primary objective of this study was to propose a scenario-based framework for optimising eco-wellness tourism development. For this purpose, maps of 26 factors affecting the evaluation of nature-based eco-wellness tourism, including water, climatic, and kinetic therapies, were used in the Ardabil province of Iran. Weighted criteria maps are integrated into suitability maps for various wellness tourism products under different scenarios, ranging from very pessimistic to very optimistic, using the Ordered Weighted Averaging (OWA) operator. Then, to identify areas of consensus, scenario-based maps for water, climate, and kinetic therapies are combined. In the very pessimistic (optimistic) scenario, climate-only therapy accounts for 0.91% (2.23%), water-only therapy for 1.07% (8.44%), and kinetic-only therapy for 3.5% (5.81%) of the area. The most significant expansion is observed in areas integrating all three therapies—climate, water, and kinetic—which increase from 3.23% in the very pessimistic scenario to 14.5% in the very optimistic scenario. The findings have substantial insights for policymakers, tourism planners, and investors in developing and promoting unique eco-wellness experiences that benefit tourists. The methodical approach and choice of data and parameters in the study can be inspirational and adjustable for relevant studies. Full article

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25 pages, 8613 KB

Open AccessArticle

Evaluation of Underground Space Resources in Ancient Cities from the Perspective of Organic Renewal: A Case Study of Shaoxing Ancient City

by Qiuxiao Chen, Yiduo Qi, Guanjie Xu, Xiuxiu Chen, Xiaoyi Zhang and Hongbo Li

ISPRS Int. J. Geo-Inf. 2025, 14(10), 384; https://doi.org/10.3390/ijgi14100384 - 1 Oct 2025

China has entered a period of urban renewal, with the focus shifting from large-scale incremental construction to both upgrading existing building quality and adjusting incremental structures. There are three main types of urban renewal: demolition and reconstruction, comprehensive improvement, and organic renewal. The [...] Read more.

China has entered a period of urban renewal, with the focus shifting from large-scale incremental construction to both upgrading existing building quality and adjusting incremental structures. There are three main types of urban renewal: demolition and reconstruction, comprehensive improvement, and organic renewal. The latter systematically optimizes and enhances urban functions, spaces, and culture through gradual renovation methods and is, therefore, suitable for use in ancient cities. To promote organic renewal, the problem of limited space resources must first be addressed, which can be resolved to a certain extent by the moderate development of underground spaces; preliminary evaluations of the development potential are also required. In consideration of the demands of organic renewal, we constructed a novel indicator system for evaluating underground space development potential (USDP) in ancient cities that assesses two dimensions: development demand and development suitability. A multi-factor comprehensive evaluation method was adopted to quantify the indicators of USDP, taking Shaoxing Ancient City (SAC) as the case study. According to the USDP evaluation, SAC can be divided into four kinds of areas: high-potential, general-potential, low-potential, and prohibited development areas. High-potential areas accounted for 16.38% of the total evaluation area and were primarily concentrated in or near key locations: train transit stations (Shaoxing Railway Station), public service facilities, evacuated land, and cultural and tourism facilities around historic districts (Shusheng Guli Historical and Cultural Street). The proposed development strategies for these areas included the interconnection of metro stations, redevelopment of relocation-related and vacated land, construction of underground cultural corridors, and supplementation of parking facilities. For developed underground spaces with low utilization efficiency, functional renewal and management improvement measures were put forward. Our method of evaluating the USDP of ancient cities and the strategies proposed to optimize the utilization of underground space can provide reference examples for SAC and other similar ancient cities. Full article

(This article belongs to the Topic 3D Documentation of Natural and Cultural Heritage)

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35 pages, 4758 KB

Open AccessArticle

Automated Detection of Beaver-Influenced Floodplain Inundations in Multi-Temporal Aerial Imagery Using Deep Learning Algorithms

by Evan Zocco, Chandi Witharana, Isaac M. Ortega and William Ouimet

ISPRS Int. J. Geo-Inf. 2025, 14(10), 383; https://doi.org/10.3390/ijgi14100383 - 30 Sep 2025

Remote sensing provides a viable alternative for understanding landscape modifications attributed to beaver activity. The central objective of this study is to integrate multi-source remote sensing observations in tandem with a deep learning (DL) (convolutional neural net or transformer) model to automatically map [...] Read more.

Remote sensing provides a viable alternative for understanding landscape modifications attributed to beaver activity. The central objective of this study is to integrate multi-source remote sensing observations in tandem with a deep learning (DL) (convolutional neural net or transformer) model to automatically map beaver-influenced floodplain inundations (BIFI) over large geographical extents. We trained, validated, and tested eleven different model configurations in three architectures using five ResNet and five B-Finetuned encoders. The training dataset consisted of >25,000 manually annotated aerial image tiles of BIFIs in Connecticut. The YOLOv8 architecture outperformed competing configurations and achieved an F1 score of 80.59% and pixel-based map accuracy of 98.95%. SegFormer and U-Net++’s highest-performing models had F1 scores of 68.98% and 78.86%, respectively. The YOLOv8l-seg model was deployed at a statewide scale based on 1 m resolution multi-temporal aerial imagery acquired from 1990 to 2019 under leaf-on and leaf-off conditions. Our results suggest a variety of inferences when comparing leaf-on and leaf-off conditions of the same year. The model exhibits limitations in identifying BIFIs in panchromatic imagery in occluded environments. Study findings demonstrate the potential of harnessing historical and modern aerial image datasets with state-of-the-art DL models to increase our understanding of beaver activity across space and time. Full article

43 pages, 7808 KB

Open AccessArticle

GeoJSEval: An Automated Evaluation Framework for Large Language Models on JavaScript-Based Geospatial Computation and Visualization Code Generation

by Guanyu Chen, Haoyue Jiao, Shuyang Hou, Ziqi Liu, Lutong Xie, Shaowen Wu, Huayi Wu, Xuefeng Guan and Zhipeng Gui

ISPRS Int. J. Geo-Inf. 2025, 14(10), 382; https://doi.org/10.3390/ijgi14100382 - 28 Sep 2025

With the widespread adoption of large language models (LLMs) in code generation tasks, geospatial code generation has emerged as a critical frontier in the integration of artificial intelligence and geoscientific analysis. This growing trend underscores the urgent need for systematic evaluation methodologies to [...] Read more.

With the widespread adoption of large language models (LLMs) in code generation tasks, geospatial code generation has emerged as a critical frontier in the integration of artificial intelligence and geoscientific analysis. This growing trend underscores the urgent need for systematic evaluation methodologies to assess the generation capabilities of LLMs in geospatial contexts. In particular, geospatial computation and visualization tasks in the JavaScript environment rely heavily on the orchestration of diverse frontend libraries and ecosystems, posing elevated demands on a model’s semantic comprehension and code synthesis capabilities. To address this challenge, we propose GeoJSEval—the first multimodal, function-level automatic evaluation framework for LLMs in JavaScript-based geospatial code generation tasks. The framework comprises three core components: a standardized test suite (GeoJSEval-Bench), a code submission engine, and an evaluation module. It includes 432 function-level tasks and 2071 structured test cases, spanning five widely used JavaScript geospatial libraries that support spatial analysis and visualization functions, as well as 25 mainstream geospatial data types. GeoJSEval enables multidimensional quantitative evaluation across metrics such as accuracy, output stability, resource consumption, execution efficiency, and error type distribution. Moreover, it integrates boundary testing mechanisms to enhance robustness and evaluation coverage. We conduct a comprehensive assessment of 20 state-of-the-art LLMs using GeoJSEval, uncovering significant performance disparities and bottlenecks in spatial semantic understanding, code reliability, and function invocation accuracy. GeoJSEval offers a foundational methodology, evaluation resource, and practical toolkit for the standardized assessment and optimization of geospatial code generation models, with strong extensibility and promising applicability in real-world scenarios. This manuscript represents the peer-reviewed version of our earlier preprint previously made available on arXiv. Full article

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18 pages, 4522 KB

Open AccessArticle

PGTFT: A Lightweight Graph-Attention Temporal Fusion Transformer for Predicting Pedestrian Congestion in Shadow Areas

by Jiyoon Lee and Youngok Kang

ISPRS Int. J. Geo-Inf. 2025, 14(10), 381; https://doi.org/10.3390/ijgi14100381 - 28 Sep 2025

Forecasting pedestrian congestion in urban back streets is challenging due to “shadow areas” where CCTV coverage is absent and trajectory data cannot be directly collected. To address these gaps, we propose the Peak-aware Graph-attention Temporal Fusion Transformer (PGTFT), a lightweight hybrid model that [...] Read more.

Forecasting pedestrian congestion in urban back streets is challenging due to “shadow areas” where CCTV coverage is absent and trajectory data cannot be directly collected. To address these gaps, we propose the Peak-aware Graph-attention Temporal Fusion Transformer (PGTFT), a lightweight hybrid model that extends the Temporal Fusion Transformer by integrating a non-parametric attention-based Graph Convolutional Network, a peak-aware Gated Residual Network, and a Peak-weighted Quantile Loss. The model leverages both physical connectivity and functional similarity between roads through a fused adjacency matrix, while enhancing sensitivity to high-congestion events. Using real-world trajectory data from 38 CCTVs in Anyang, South Korea, experiments show that PGTFT outperforms LSTM, TFT, and GCN-TFT across different sparsity settings. Under sparse 5 m neighbor conditions, the model achieved the lowest MAE (0.059) and RMSE (0.102), while under denser 30 m settings it maintained superior accuracy with standard quantile loss. Importantly, PGTFT requires only 1.54 million parameters—about half the size of conventional Transformer–GCN hybrids—while delivering equal or better predictive performance. These results demonstrate that PGTFT is both parameter-efficient and robust, offering strong potential for deployment in smart city monitoring, emergency response, and transportation planning, as well as a practical approach to addressing data sparsity in urban sensing systems. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))

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26 pages, 5227 KB

Open AccessArticle

The LADM Spatial Plan Information Country Profile for Serbia

by Aleksandra Radulović, Dubravka Sladić, Aleksandar Ristić, Dušan Jovanović, Sead Mašović and Miro Govedarica

ISPRS Int. J. Geo-Inf. 2025, 14(10), 380; https://doi.org/10.3390/ijgi14100380 - 28 Sep 2025

Spatial planning deals with the organization and regulation of space with the goal to improve the quality of life of its inhabitants. Spatial planning plays a vital role in land administration, encompassing land development, management, land use assessment, resource allocation, and environmental protection. [...] Read more.

Spatial planning deals with the organization and regulation of space with the goal to improve the quality of life of its inhabitants. Spatial planning plays a vital role in land administration, encompassing land development, management, land use assessment, resource allocation, and environmental protection. The significance of integrating spatial-planning information into the ISO 19152 Land Administration Domain Model (LADM) framework has been recognized in the LADM second edition, Part 5, where a part for spatial plan information is introduced. The aim of this paper is to analyze the applicability of the LADM Part 5: Spatial Plan Information draft international standard to the Serbian spatial and urban planning system and to develop a country profile for Serbia in alignment with Serbian laws and regulations. An analysis of spatial and urban planning in Serbia will be performed, determining the hierarchy of spatial and urban plans based on an analysis of laws on spatial planning. The created conceptual model for spatial planning for Serbia based on the LADM Part 5: Spatial Plan Information will be harmonized with the previously created LADM country profile for Serbia. Full article

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23 pages, 1306 KB

Open AccessArticle

Mixed-Graph Neural Network for Traffic Flow Prediction by Capturing Dynamic Spatiotemporal Correlations

by Xing Su, Pengcheng Li, Zhi Cai, Limin Guo and Boya Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(10), 379; https://doi.org/10.3390/ijgi14100379 - 27 Sep 2025

Traffic flow prediction is a prominent research area in intelligent transportation systems, significantly contributing to urban traffic management and control. Existing methods or models for traffic flow prediction predominantly rely on a fixed-graph structure to capture spatial correlations within a road network. However, [...] Read more.

Traffic flow prediction is a prominent research area in intelligent transportation systems, significantly contributing to urban traffic management and control. Existing methods or models for traffic flow prediction predominantly rely on a fixed-graph structure to capture spatial correlations within a road network. However, the fixed-graph structure can restrict the representation of spatial information due to varying conditions such as time and road changes. Drawing inspiration from the attention mechanism, a new prediction model based on the mixed-graph neural network is proposed to dynamically capture the spatial traffic flow correlations. This model uses graph convolution and attention networks to adapt to complex and changeable traffic and other conditions by learning the static and dynamic spatial traffic flow characteristics, respectively. Then, their outputs are fused by the gating mechanism to learn the spatial traffic flow correlations. The Transformer encoder layer is subsequently employed to model the learned spatial characteristics and capture the temporal traffic flow correlations. Evaluated on five real traffic flow datasets, the proposed model outperforms the state-of-the-art models in prediction accuracy. Furthermore, ablation experiments demonstrate the strong performance of the proposed model in long-term traffic flow prediction. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

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23 pages, 3631 KB

Open AccessArticle

Modeling Spatial Determinants of Blue School Certification: A Maxent Approach in Mallorca

by Christian Esteva-Burgos and Maurici Ruiz-Pérez

ISPRS Int. J. Geo-Inf. 2025, 14(10), 378; https://doi.org/10.3390/ijgi14100378 - 26 Sep 2025

The Blue Schools initiative integrates the ocean into classroom learning through project-based approaches, cultivating environmental awareness and a deeper sense of responsibility toward marine ecosystems and human–ocean interactions. Although the European Blue School initiative has grown steadily since its launch in 2020, its [...] Read more.

The Blue Schools initiative integrates the ocean into classroom learning through project-based approaches, cultivating environmental awareness and a deeper sense of responsibility toward marine ecosystems and human–ocean interactions. Although the European Blue School initiative has grown steadily since its launch in 2020, its uneven uptake raises important questions about the territorial factors that influence certification. This study examines the spatial determinants of Blue School certification in Mallorca, Spain, where a bottom-up pilot initiative successfully certified 100 schools. Using Maximum Entropy (MaxEnt) modeling, we estimated the spatial probability of certification based on 16 geospatial variables, including proximity to Blue Economy actors, hydrological networks, transport accessibility, and socio-economic indicators. The model achieved strong predictive performance (AUC = 0.84) and revealed that features such as freshwater ecosystems, traditional economic structures, and sustainable public transport play a greater role in school engagement than coastal proximity alone. The resulting suitability map identifies over 30 high-potential, non-certified schools, offering actionable insights for targeted outreach and educational policy. This research highlights the potential of presence-only modeling to guide the strategic expansion of Blue Schools networks. Full article

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29 pages, 21314 KB

Open AccessArticle

Integrating Remote Sensing and Geospatial-Based Comprehensive Multi-Criteria Decision Analysis Approach for Sustainable Coastal Solar Site Selection in Southern India

by Constan Antony Zacharias Grace, John Prince Soundranayagam, Antony Johnson Antony Alosanai Promilton, Shankar Karuppannan, Wafa Saleh Alkhuraiji, Viswasam Stephen Pitchaimani, Faten Nahas and Yousef M. Youssef

ISPRS Int. J. Geo-Inf. 2025, 14(10), 377; https://doi.org/10.3390/ijgi14100377 - 26 Sep 2025

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) [...] Read more.

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

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19 pages, 2731 KB

Open AccessArticle

Exploring the Spatial Relationship Between Severe Depression, COVID-19 Case Rates, and Vaccination Rates in US Counties: A Spatial Analysis Across Two Time Periods

by Yuqing Wang and Wencong Cui

ISPRS Int. J. Geo-Inf. 2025, 14(10), 376; https://doi.org/10.3390/ijgi14100376 - 25 Sep 2025

Severe depression is shaped by complex interactions between public health crises and socioeconomic conditions, yet the spatial and temporal dynamics of these factors remain underexplored. This study investigates the impact of COVID-19 case rates, vaccination rates, and socioeconomic factors on severe depression rates [...] Read more.

Severe depression is shaped by complex interactions between public health crises and socioeconomic conditions, yet the spatial and temporal dynamics of these factors remain underexplored. This study investigates the impact of COVID-19 case rates, vaccination rates, and socioeconomic factors on severe depression rates across 1470 counties in the contiguous USA in 2021 and 2022. We combined Ordinary Least Squares (OLS) regression with Multiscale Geographically Weighted Regression (MGWR) to capture both global associations and local geographic variability. Results show that higher COVID-19 case rates in 2021 were associated with increased rates of severe depression in 2022, while higher vaccination rates during the same period were associated with decreased rates of severe depression. However, these associations weakened when using 2022 data, suggesting a temporal lag in the impact on mental health. MGWR analyses revealed regional disparities: COVID-19 case rates had a stronger impact in the Midwest, while vaccination benefits were more pronounced on the West Coast. Additional factors, such as unemployment, limited sunlight exposure, and the availability of mental health resources, also influenced outcomes. These findings underscore the importance of temporally and geographically nuanced approaches to public mental health interventions and support the need for region-specific strategies to address mental health disparities in the wake of public health crises. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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37 pages, 16383 KB

Open AccessArticle

Generating Realistic Urban Patterns: A Controllable cGAN Approach with Hybrid Loss Optimization

by Amgad Agoub and Martin Kada

ISPRS Int. J. Geo-Inf. 2025, 14(10), 375; https://doi.org/10.3390/ijgi14100375 - 25 Sep 2025

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a [...] Read more.

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a bespoke model architecture that integrates attention mechanisms with visual reasoning through a generalized conditioning layer. A novel mechanism that enables the steering of urban pattern generation through the use of statistical input distributions, the development of a novel and comprehensive training dataset, meticulously derived from open-source geospatial data of Berlin. Our model is trained using a hybrid loss function, combining adversarial, focal and L1 losses to ensure perceptual realism, address challenging fine-grained features, and enforce pixel-level accuracy. Model performance was assessed through a combination of qualitative visual analysis and quantitative evaluation using metrics such as Kullback–Leibler Divergence (KL Divergence), Structural Similarity Index (SSIM), and Dice Coefficient. The proposed approach has demonstrated effectiveness in generating realistic and spatially coherent urban patterns, with promising potential for controllability. In addition to showcasing its strengths, we also highlight the limitations and outline future directions for advancing future work. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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22 pages, 15568 KB

Open AccessArticle

Adversarial Obstacle Placement with Spatial Point Processes for Optimal Path Disruption

by Li Zhou, Elvan Ceyhan and Polat Charyyev

ISPRS Int. J. Geo-Inf. 2025, 14(10), 374; https://doi.org/10.3390/ijgi14100374 - 25 Sep 2025

We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and already discrete spatial grids that form weighted geospatial [...] Read more.

We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and already discrete spatial grids that form weighted geospatial networks using 8-adjacency lattices. Our unified framework integrates OOP with stochastic geometry, modeling obstacle placement via Strauss (regular) and Matérn (clustered) processes, and evaluates traversal using the Reset Disambiguation algorithm. Through extensive Monte Carlo experiments, we show that traversal cost increases by up to 40% under strongly regular placements, while clustered configurations can decrease traversal costs by as much as 25% by leaving navigable corridors compared to uniform random layouts. In mixed (with both true and false obstacles) scenarios, increasing the proportion of true obstacles from 30% to 70% nearly doubles the traversal cost. These findings are further supported by statistical analysis and stochastic ordering, providing rigorous insights into how spatial patterns and obstacle compositions influence navigation under uncertainty. Full article

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23 pages, 2229 KB

Open AccessArticle

Optimization of Electric Vehicle Charging Station Location Distribution Based on Activity–Travel Patterns

by Qian Zhang, Guiwu Si and Hongyi Li

ISPRS Int. J. Geo-Inf. 2025, 14(10), 373; https://doi.org/10.3390/ijgi14100373 - 25 Sep 2025

With the rapid expansion of the electric vehicle (EV) market, optimizing the distribution of charging stations has attracted increasing attention. Unlike internal combustion engine vehicles, EVs are typically charged at the end of a trip rather than during transit. Therefore, analyzing EV users’ [...] Read more.

With the rapid expansion of the electric vehicle (EV) market, optimizing the distribution of charging stations has attracted increasing attention. Unlike internal combustion engine vehicles, EVs are typically charged at the end of a trip rather than during transit. Therefore, analyzing EV users’ charging preferences based on their activity–travel patterns is essential. This study seeks to improve the operational efficiency and accessibility of EV charging stations in Lanzhou City by optimizing their spatial distribution. To achieve this, a novel multi-objective optimization model integrating NSGA-III and TOPSIS is proposed. The methodology consists of two key steps. First, the NSGA-III algorithm is applied to optimize three objective functions: minimizing construction costs, maximizing user satisfaction, and maximizing user convenience, thereby identifying charging station locations that address diverse needs. Second, the TOPSIS method is employed to rank and evaluate various location solutions, ultimately determining the final sitting strategy. The results show that the 232 locations obtained by the optimization model are reasonably distributed, with good operational efficiency and convenience. Most of them are distributed in urban centers and commercial areas, which is consistent with the usage scenarios of EV users. In addition, this study demonstrates the superiority in determining the distribution of charging station locations of the proposed method. In summary, this study determined the optimal distribution of 232 EV charging stations in Lanzhou City using multi-objective optimization and ranking methods. The results are of great significance for improving the operational efficiency and convenience of charging station location optimization and offer valuable insights for other cities in northwestern China in planning their charging infrastructure. Full article

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22 pages, 4522 KB

Open AccessArticle

Mobilities in the Heat: Identifying Travel-Related Urban Heat Exposure and Its Built Environment Drivers Using Remote Sensing and Mobility Data in Chengdu, China

by Yue Zhang, Xiaojiang Xia, Yang Zhang and Ling Jian

ISPRS Int. J. Geo-Inf. 2025, 14(10), 372; https://doi.org/10.3390/ijgi14100372 - 24 Sep 2025

Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure [...] Read more.

Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure index (THEI) that combines ride-hailing trajectories and remote sensing data to capture dynamic human–environment thermal interactions. Using Chengdu, China, as a case study, the THEI is combined with local indicators of spatial association to outline high-exposure risk zones (HERZ). XGBoost with SHAP and partial dependence plot (PDP) methods is also applied to identify the nonlinear effects of built environment factors. Results showed the following: (1) distinct spatial clustering of high travel-related heat exposure in central urban districts and transit hubs; (2) city-wide exposure is primarily driven by transportation accessibility and urban form, such as intersection density and floor area ratio; (3) in contrast, HERZ are more strongly associated with demographic and socioeconomic factors, including population density, housing price and road density; and (4) vegetation, measured by the normalized difference vegetation index, demonstrates a consistent negative effect across scales, highlighting its critical role in mitigating thermal risks. These findings emphasize the necessity of incorporating mobility-based exposure metrics and spatial heterogeneity into climate-resilient urban planning, with differentiated strategies tailored for city-wide versus high-risk zones. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

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35 pages, 7791 KB

Open AccessArticle

Data-Driven Spatial Optimization of Elderly Care Facilities: A Study on Nonlinear Threshold Effects Based on XGBoost and SHAP—A Case Study of Xi’an, China

by Linggui Liu, Han Lyu, Jinghua Dai, Yuheng Tu and Taotao Gao

ISPRS Int. J. Geo-Inf. 2025, 14(10), 371; https://doi.org/10.3390/ijgi14100371 - 24 Sep 2025

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous [...] Read more.

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous demands across different elderly care facility types. This study addresses these gaps by proposing a data-driven framework that integrates machine learning with spatial analysis to optimize elderly care facility distribution in Xi’an City central area, Shaanxi Province, China. Leveraging multi-source datasets encompassing points of interest (POIs), road networks, and demographic statistics, we classify facilities into three categories (service-oriented, activity-oriented, and care-oriented) and employ an XGBoost model with SHAP interpretability to evaluate spatial distributions and influencing factors. The results demonstrate that the XGBoost model outperforms comparative algorithms (Random Forest, CatBoost, LightGBM) with superior performance metrics (accuracy rate of 97%, precision of 95%, and F1-score of 90%), effectively capturing nonlinear thresholds effects. Key findings reveal the following: (1) Accessibility and road density exert threshold effects on care-oriented facilities, with facility attractiveness saturating when these values exceed 6; (2) Land use intensity and medical resources positively correlate with activity-oriented facilities, while excessive retail density inhibits their distribution; (3) Service-oriented facilities thrive in areas with balanced accessibility and moderate commercial diversity. Spatial analysis identifies clustered distribution patterns in urban core areas contrasted with peripheral deficiencies, indicating need for targeted interventions. This research contributes a scalable methodology for equitable facility planning, emphasizing the integration of dynamic built environment variations with model interpretability. The framework provides significant implications for formulating age-friendly urban policies applicable to global cities undergoing rapid urbanization and population aging. Full article

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27 pages, 22665 KB

Open AccessArticle

Assessing Spatial Accessibility Uncertainty with Dempster–Shafer Theory: A Comparison of Potential and Revealed Accessibility

by Roya Esmaeili Tajabadi, Parham Pahlavani, Amin Hosseinpoor Milaghardan and Christophe Claramunt

ISPRS Int. J. Geo-Inf. 2025, 14(10), 370; https://doi.org/10.3390/ijgi14100370 - 23 Sep 2025

This study introduces a framework for comparing and integrating revealed and potential accessibility maps, using the Dempster–Shafer theory to identify regions with varying spatial accessibility while accounting for uncertainty. It presents a method for determining revealed accessibility from individuals’ trajectory data, weighting accessibility [...] Read more.

This study introduces a framework for comparing and integrating revealed and potential accessibility maps, using the Dempster–Shafer theory to identify regions with varying spatial accessibility while accounting for uncertainty. It presents a method for determining revealed accessibility from individuals’ trajectory data, weighting accessibility inversely to the square of uncertainty. This dual approach aids urban planners in making more reliable decisions. The methodology is applied to supply centers, including shops, restaurants, and sports centers, using data from the Mobile Data Challenge (MDC) in Vaud, Switzerland. The results show good access to shops in the northwestern and southeastern regions and good access to restaurants in the eastern regions. The final maps indicate that areas with low access to sports centers form the highest proportion (62.7%) of regions with low access, while those with low access to shopping centers form the lowest (9.3%). The findings suggest the need for more sports centers in Nyon and Jura-Nord Vaudois and more accessible restaurants in Nyon and southern Aigle. Additionally, the analysis reveals that lower station densities correlate with smaller discrepancies between real and expected accessibilities, while higher population densities are linked to lower uncertainty, underscoring the importance of considering density in spatial accessibility assessments. Full article

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