ISPRS International Journal of Geo-Information

38 pages, 9753 KB

Open AccessArticle

Enhancing Land Use Efficiency Assessment Through Built-Up Area–Built-Up Volume Trajectories: Integrating Vertical Urban Growth into SDG 11.3.1 Monitoring

by Jojene Santillan, Mareike Dorozynski and Christian Heipke

ISPRS Int. J. Geo-Inf. 2025, 14(10), 404; https://doi.org/10.3390/ijgi14100404 - 15 Oct 2025

Abstract

SDG Indicator 11.3.1 assesses urban land use efficiency (LUE) through the ratio of the land consumption rate (LCR) to the population growth rate (PGR), or LCRPGR. However, its methodology is restricted to two-dimensional built-up area expansion, excluding vertical development and limiting insight into [...] Read more.

SDG Indicator 11.3.1 assesses urban land use efficiency (LUE) through the ratio of the land consumption rate (LCR) to the population growth rate (PGR), or LCRPGR. However, its methodology is restricted to two-dimensional built-up area expansion, excluding vertical development and limiting insight into the structural mechanisms underlying efficiency outcomes. This study aims to integrate vertical urban growth into SDG 11.3.1 monitoring to improve the interpretation of efficiency outcomes. We introduce the Built-up Area–Built-up Volume (BUA–BUV) trajectory framework, which embeds vertical growth into LUE monitoring. The framework represents urban growth as trajectories in normalized BUA–BUV space and classifies them by prevailing built form (horizontal, balanced, vertical) and growth modality (expansion or intensification). This classification is then coupled with LCRPGR to link efficiency outcomes with spatial structure. We apply the framework to 10,856 urban centres worldwide using Global Human Settlement Urban Centre Database (GHS-UCDB 2025) data from 1980 to 2020. Results show that inefficient growth (LCRPGR > 1) dominated, affecting 69% of centres during 1980–2000 and 52% during 2000–2020, while inefficiency linked to demographic decline (LCRPGR ≤ 0) rose from 9% to 20%. Efficient centres (0 < LCRPGR ≤ 1) increased from 22% to 29%. Across all efficiency classes, BUA–BUV trajectories revealed a prevailing pattern of horizontal expansion, with similar LCRPGR values associated with structurally divergent growth paths. Vertically intensifying development was rare, even among efficient centres. The BUA–BUV framework embeds structural context into efficiency assessments, thereby strengthening SDG 11.3.1 monitoring and informing policies for compact and sustainable urbanization. Full article

22 pages, 12379 KB

Open AccessArticle

Evaluation of Spatial Variability of Soil Nutrients in Saline–Alkali Farmland Using Automatic Machine Learning Model and Hyperspectral Data

by Meiyan Xiang, Qianlong Rao, Xiaohang Yang, Xiaoqian Wu, Dexi Zhan, Jin Zhang, Miao Lu and Yingqiang Song

ISPRS Int. J. Geo-Inf. 2025, 14(10), 403; https://doi.org/10.3390/ijgi14100403 - 15 Oct 2025

Abstract

Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality [...] Read more.

Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality agricultural development and ecological balance, this study took coastal saline–alkali land as a case study. It adopted the extreme gradient boosting (XGB) model optimized by the tree-structured Parzen estimator (TPE) algorithm, combined with in situ hyperspectral (ISH) and spaceborne hyperspectral (SBH) data, to predict and map soil organic matter and four soil nutrients: alkali nitrogen (AN), available phosphorus (AP), and available potassium (AK). From the research outputs, one can deduce that superior predictive efficacy is exhibited by the TPE-XGB construct, employing in situ hyperspectral datasets. Among these, available phosphorus (R² = 0.67) exhibits the highest prediction accuracy, followed by organic matter (R² = 0.65), alkali-hydrolyzable nitrogen (R² = 0.56), and available potassium (R² = 0.51). In addition, the spatial continuity mapping results based on spaceborne hyperspectral data show that SOM, AN, AP, and AK in soil nutrients in the study area are concentrated in the northern, eastern, southern, and riverbank and estuarine delta areas, respectively. The variability of soil nutrients from large to small is phosphorus, potassium, nitrogen, and organic matter. The SHAP (SHapley Additive exPlanations) analysis results reveal that the bands with the greatest contribution to the fitting of SOM, AN, AP, and AK are 612 nm, 571 nm, 1493 nm, and 1308 nm, respectively. Extending into realms of hierarchical partitioning (HP) and variation partitioning (VP), it is discerned that climatic factors (CLI) alongside vegetative aspects (VEG) wield dominant influence upon the spatial differentiation manifest in nutrients. Meanwhile, comparatively diminished are the contributions possessed by terrain (TER) and soil property (SOIL). In summary, this study effectively assessed the significant variation patterns of soil nutrient distribution in coastal saline–alkali soils using the TPE-XGB model, providing scientific basis for the sustainable advancement of agricultural development in saline–alkali coastal regions. Full article

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

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16 pages, 5347 KB

Open AccessArticle

Numerical Assessment of a High-Level Rock Failure Potential Based on a Three-Dimensional Discrete Element Model

by Xin Zhou, Yiding Bao, Weifeng Zhang and Renzhe Zeng

ISPRS Int. J. Geo-Inf. 2025, 14(10), 402; https://doi.org/10.3390/ijgi14100402 - 15 Oct 2025

Abstract

The estimation of the area susceptible to rock failure and the prediction of its movement process are pivotal for hazard mitigation, yet they are also challenging. In this study, we proposed a novel integrated method combining field investigation, remote sensing, and three-dimensional discrete [...] Read more.

The estimation of the area susceptible to rock failure and the prediction of its movement process are pivotal for hazard mitigation, yet they are also challenging. In this study, we proposed a novel integrated method combining field investigation, remote sensing, and three-dimensional discrete element method (DEM) simulation to achieve our goal. The field investigation and remote sensing analysis are used for the purpose of ascertaining the deformation phenomenon and the structure of the rock slope, identifying the potential failure position and area of the slope. Subsequently, a three-dimensional DEM simulation is employed to quantitatively assess the potential rock failure-affected area and movement process, based on the above potential failure information. The simulation results demonstrate that potential rock failure persists for approximately 30 s, and its movement process can be categorized into two distinct stages: acceleration and deceleration. The initial acceleration stage is characterized by a duration of 10 s, culminating in a peak average velocity of 13 m/s. The subsequent deceleration stage extends for a duration of 20 s. Notably, the maximum attainable velocity for the segment of rock mass under consideration is estimated to be 50 m/s. Furthermore, the model demonstrates the variation in fracture energy, friction energy, and kinetic energy over time. The potential affected area is 140,000 m², and approximately 8000 m² of residential construction will be destroyed if a rock failure occurs. It is imperative to implement measures aimed at the prevention of rock failure in order to mitigate the risk of such an occurrence. Full article

(This article belongs to the Topic Applications of Algorithms in Risk Assessment and Evaluation)

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28 pages, 38011 KB

Open AccessArticle

On the Use of LLMs for GIS-Based Spatial Analysis

by Roberto Pierdicca, Nikhil Muralikrishna, Flavio Tonetto and Alessandro Ghianda

ISPRS Int. J. Geo-Inf. 2025, 14(10), 401; https://doi.org/10.3390/ijgi14100401 - 14 Oct 2025

Abstract

This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial analysis tasks. We designed and implemented a system capable of interpreting natural [...] Read more.

This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial analysis tasks. We designed and implemented a system capable of interpreting natural language instructions provided by users and translating them into automated GIS workflows through dynamically generated Python scripts. An interactive graphical user interface (GUI), built using CustomTkinter, was developed to enable intuitive user interaction with GIS data and processes, reducing the need for advanced programming or technical expertise. We conducted an empirical evaluation of this approach through a comparative case study involving typical GIS tasks such as spatial data validation, data merging, buffer analysis, and thematic mapping using urban datasets from Pesaro, Italy. The performance of our automated system was directly compared against traditional manual workflows executed by 10 experienced GIS analysts. The results from this evaluation indicate a substantial reduction in task completion time, decreasing from approximately 1 h and 45 min in the manual approach to roughly 27 min using our LLM-driven automation, without compromising analytical quality or accuracy. Furthermore, we systematically evaluated the system’s factual reliability using a diverse set of geospatial queries, confirming robust performance for practical GIS tasks. Additionally, qualitative feedback emphasized improved usability and accessibility, particularly for users without specialized GIS training. These findings highlight the significant potential of integrating LLMs into GISs, demonstrating clear advantages in workflow automation, user-friendliness, and broader adoption of advanced spatial analysis methodologies. Full article

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

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18 pages, 13398 KB

Open AccessArticle

Surrounding Vitality Reasoning of Attractions Supported by Knowledge Graph

by Yi Liu, Lili Wu and Youneng Su

ISPRS Int. J. Geo-Inf. 2025, 14(10), 400; https://doi.org/10.3390/ijgi14100400 - 13 Oct 2025

Abstract

The vitality of areas around tourist attractions plays a crucial role in promoting the sustainable development of both tourism and the regional economy. However, there is a lack of comprehensive studies on the methods for mining vitality around attraction perimeters, and existing approaches [...] Read more.

The vitality of areas around tourist attractions plays a crucial role in promoting the sustainable development of both tourism and the regional economy. However, there is a lack of comprehensive studies on the methods for mining vitality around attraction perimeters, and existing approaches are often inadequate to meet the evolving needs of contemporary tourism development. To address this gap, we proposed a method for inferring vitality around attractions based on a knowledge graph. Our approach began by analyzing the functional and morphological characteristics of the areas surrounding the attractions, followed by the design of a vitality calculation model for these regions. Next, we developed a knowledge graph structure tailored for vitality reasoning around the attractions and established reasoning rules based on this graph. Finally, we conducted experiments to apply the vitality inference method to the main urban area of Kaifeng City as a case study. The results indicated that our method could effectively reason about vitality around the attractions. Notably, the vitality levels around the attractions in Kaifeng’s main urban area exhibited clear spatial differentiation. Attractions such as the Yang Family’s Tianbo Mansion, the Millennium City Park, and Lord Bao’s Memorial Temple showed higher vitality values, largely due to their advantageous functional integration and synergistic morphological characteristics. Full article

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

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21 pages, 416 KB

Open AccessArticle

Understanding Planning Support Systems Institutionalization in the Planning Process Through Actor–Network Theory: The Case of the Strategic Development Framework Methodology

by Deborah Adeola Oyeku, Luc Boerboom, Ana Mafalda Madureira and Karin Pfeffer

ISPRS Int. J. Geo-Inf. 2025, 14(10), 399; https://doi.org/10.3390/ijgi14100399 - 13 Oct 2025

Abstract

Studies conceptualize planning support systems (PSS) outcomes as post-implementation use (limited or continuous) in the planning process. This paper presents another perspective on PSS implementation outcomes—its institutionalization in the planning process. It combines the sociology of translation (SoT) and actor–network theory (ANT) as [...] Read more.

Studies conceptualize planning support systems (PSS) outcomes as post-implementation use (limited or continuous) in the planning process. This paper presents another perspective on PSS implementation outcomes—its institutionalization in the planning process. It combines the sociology of translation (SoT) and actor–network theory (ANT) as an analytic framework to investigate and explain a country’s PSS institutionalization in the planning process over 8 years. Ethnographic methods aid qualitative data collection and analysis. Results provide insight in the following three ways: (1) how heterogeneous actors create networks for PSS use, (2) to what extent the network(s) shape PSS institutionalization, and (3) why PSS institutionalization in planning processes does or does not happen. This paper argues that if PSS research investigates and documents these three ways, it will provide additional insights into the decisions, actions, and agencies of PSS institutionalization compared to studies that conceptualize PSS outcomes with use. It contributes to PSS research and practice by demonstrating the value of ANT in enhancing our understanding of PSS institutionalization in planning processes. It recommends further studies to validate this research regarding both retrospective understanding of and prospective management for PSS institutionalization. Full article

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22 pages, 7596 KB

Open AccessArticle

Orthographic Video Map Generation Considering 3D GIS View Matching

by Xingguo Zhang, Xiangfei Meng, Li Zhang, Xianguo Ling and Sen Yang

ISPRS Int. J. Geo-Inf. 2025, 14(10), 398; https://doi.org/10.3390/ijgi14100398 - 13 Oct 2025

Abstract

Converting tower-mounted videos from perspective to orthographic view is beneficial for their integration with maps and remote sensing images and can provide a clearer and more real-time data source for earth observation. This paper addresses the issue of low geometric accuracy in orthographic [...] Read more.

Converting tower-mounted videos from perspective to orthographic view is beneficial for their integration with maps and remote sensing images and can provide a clearer and more real-time data source for earth observation. This paper addresses the issue of low geometric accuracy in orthographic video generation by proposing a method that incorporates 3D GIS view matching. Firstly, a geometric alignment model between video frames and 3D GIS views is established through camera parameter mapping. Then, feature point detection and matching algorithms are employed to associate image coordinates with corresponding 3D spatial coordinates. Finally, an orthographic video map is generated based on the color point cloud. The results show that (1) for tower-based video, a 3D GIS constructed from publicly available DEMs and high-resolution remote sensing imagery can meet the spatialization needs of large-scale tower-mounted video data. (2) The feature point matching algorithm based on deep learning effectively achieves accurate matching between video frames and 3D GIS views. (3) Compared with the traditional method, such as the camera parameters method, the orthographic video map generated by this method has advantages in terms of geometric mapping accuracy and visualization effect. In the mountainous area, the RMSE of the control points is reduced from 137.70 m to 7.72 m. In the flat area, it is reduced from 13.52 m to 8.10 m. The proposed method can provide a near-real-time orthographic video map for smart cities, natural resource monitoring, emergency rescue, and other fields. Full article

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25 pages, 10107 KB

Open AccessArticle

An Integrated Framework for Multi-Objective Optimization of Night Lighting in Urban Residential Areas: Synergistic Control of Outdoor Activity Places Lighting and Indoor Light Trespass

by Fang Wen, Wenqi Sun, Ling Jiang, Caixia Yun and Xinzheng Wang

ISPRS Int. J. Geo-Inf. 2025, 14(10), 397; https://doi.org/10.3390/ijgi14100397 - 13 Oct 2025

Viewed by 26

Abstract

In the context of increasing urban night lighting, the phenomenon of light trespass in residential areas is becoming increasingly serious, affecting the night comfort and circadian rhythm of residents. Aiming at this problem, this paper takes the night lighting of activity places in [...] Read more.

In the context of increasing urban night lighting, the phenomenon of light trespass in residential areas is becoming increasingly serious, affecting the night comfort and circadian rhythm of residents. Aiming at this problem, this paper takes the night lighting of activity places in old multi-story residential areas of Shijingshan, Beijing, as the research object, and proposes a research framework integrating parametric modeling, multi-objective optimization, correlation analysis, and scheme decision-making, aiming to trade off the two objectives of maximizing the night lighting of the activity places and minimizing indoor light intrusiveness. The study first establishes a parametric model based on Rhino and Grasshopper, combines the NSGA-II algorithm with multi-objective optimization simulation to obtain the Pareto optimal solution, analyzes the correlation between the design variables and the objective function by the Spearman method, and finally assists in the scheme decision-making by K-means clustering. The results showed that the streetlight heights (SH), distance between buildings and streetlights (DBS), and streetlight matrix types (SMT) were the key factors affecting lighting performance, which should be emphasized in the actual lighting design. Secondly, the Cluster2 solution set optimally performs the two objective functions. The 18th individual of Generation 15 (Gen. 15 Ind. 18) and Gen. 31 Ind. 42 are recommended, providing practical guidance for night lighting design in residential areas. The innovation of this study lies in applying multi-objective optimization and K-means clustering to optimize the night lighting environment in micro-spaces within old multi-story residential areas in cities, offering new insights for lighting design in similar scenarios. Full article

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24 pages, 6626 KB

Open AccessArticle

Harnessing GPS Spatiotemporal Big Data to Enhance Visitor Experience and Sustainable Management of UNESCO Heritage Sites: A Case Study of Mount Huangshan, China

by Jianping Sun, Shi Chen, Yinlan Huang, Huifang Rong and Qiong Li

ISPRS Int. J. Geo-Inf. 2025, 14(10), 396; https://doi.org/10.3390/ijgi14100396 - 12 Oct 2025

Viewed by 220

Abstract

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions [...] Read more.

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

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

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37 pages, 5073 KB

Open AccessArticle

Spatiotemporal Variation and Network Correlation Analysis of Flood Resilience in the Central Plains Urban Agglomeration Based on the DRIRA Model

by Lu Liu, Huiquan Wang and Jixia Li

ISPRS Int. J. Geo-Inf. 2025, 14(10), 394; https://doi.org/10.3390/ijgi14100394 - 12 Oct 2025

Viewed by 116

Abstract

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, [...] Read more.

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions. 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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27 pages, 4254 KB

Open AccessArticle

An Integrated Isochrone-Based Geospatial Analysis of Mobility Policies and Vulnerability Hotspots in the Lazio Region, Italy

by Alessio D’Auria, Irina Di Ruocco and Antonio Gioia

ISPRS Int. J. Geo-Inf. 2025, 14(10), 395; https://doi.org/10.3390/ijgi14100395 - 10 Oct 2025

Viewed by 231

Abstract

Areas characterised by high ecological and cultural value are increasingly exposed to overtourism and intensifying land-use pressures, often exacerbated by mobility policies aimed at enhancing regional accessibility and promoting tourism. These dynamics create spatial tensions, particularly in environmentally sensitive areas such as those [...] Read more.

Areas characterised by high ecological and cultural value are increasingly exposed to overtourism and intensifying land-use pressures, often exacerbated by mobility policies aimed at enhancing regional accessibility and promoting tourism. These dynamics create spatial tensions, particularly in environmentally sensitive areas such as those within the Natura 2000 network and Sites of Community Importance (SCIs), where intensified visitor flows, and infrastructure expansion can disrupt the balance between conservation and development. This study offers a geospatial analysis of the current state (2024) of such dynamics in the Lazio Region (Italy), evaluating the effects of mobility strategies on ecological vulnerability and tourism pressure. By applying isochrone-based accessibility modelling, GIS buffer analysis, and spatial overlays, the research maps the intersection of accessibility, heritage value, and environmental sensitivity. The methodology enables the identification of critical zones where accessibility improvements coincide with heightened ecological risk and tourism-related stress. The original contribution of this work lies in its integrated spatial framework, which combines accessibility metrics with indicators of ecological and heritage significance to visualise and assess emerging risk areas. The Lazio Region, distinguished by its heterogeneous landscapes and ambitious mobility planning initiatives, constitutes a significant case study for examining how policy-driven improvements in transport infrastructure may inadvertently exacerbate spatial disparities and intensify ecological vulnerabilities in peripheral and sensitive territorial contexts. The findings support the formulation of adaptive, place-based policy recommendations aimed at mitigating the unintended consequences of accessibility-led tourism strategies. These include prioritising soft mobility, enhancing regulatory protection in high-risk zones, and fostering coordinated governance across sectors. Ultimately, the study advances a replicable methodology to inform sustainable territorial governance and balance tourism development with environmental preservation. Full article

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22 pages, 37263 KB

Open AccessArticle

Assessing Fire Station Accessibility in Guiyang, a Mountainous City, with Nighttime Light and POI Data: An Application of the Enhanced 2SFCA Approach

by Xindong He, Boqing Wu, Guoqiang Shen, Qianqian Lyu and Grace Ofori

ISPRS Int. J. Geo-Inf. 2025, 14(10), 393; https://doi.org/10.3390/ijgi14100393 - 9 Oct 2025

Viewed by 250

Abstract

Mountainous urban areas like Guiyang face unique fire safety challenges due to rugged terrain and complex road networks, which hinder fire station accessibility. This study proposes a GIS-based framework that integrates nighttime light (NPP/VIIRS) and point of interest (POI) data to assess fire [...] Read more.

Mountainous urban areas like Guiyang face unique fire safety challenges due to rugged terrain and complex road networks, which hinder fire station accessibility. This study proposes a GIS-based framework that integrates nighttime light (NPP/VIIRS) and point of interest (POI) data to assess fire risk and accessibility. Kernel density estimation quantified POI distributions across four risk categories, and the Spatial Appraisal and Valuation of Environment and Ecosystems (SAVEE) model combined these with NPP/VIIRS data to generate a composite fire risk map. Accessibility was evaluated using the enhanced two-step floating catchment area (E2SFCA) method with road network travel times; 80.13% of demand units were covered within the five-minute threshold, while 53.25% of all units exhibited low accessibility. Spatial autocorrelation analysis (Moran’s I) revealed clustered high risk in central basins and service gaps on surrounding hills, reflecting the dominant influence of terrain alongside protected forests and farmlands. The results indicate that targeted road upgrades and station relocations can improve fire service coverage. The approach is scalable and supports more equitable emergency response in mountainous settings. Full article

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20 pages, 7783 KB

Open AccessArticle

Study on Accessibility and Equity of Park Green Spaces in Zhengzhou

by Yafei Wang, Tian Cui, Wenyu Zhong, Yan Ma, Chaoyang Shi, Wenkai Liu, Qingfeng Hu, Bing Zhang, Yunfei Zhang and Hongqiang Liu

ISPRS Int. J. Geo-Inf. 2025, 14(10), 392; https://doi.org/10.3390/ijgi14100392 - 9 Oct 2025

Viewed by 287

Abstract

Urban park green space (UPGS) is a key component of urban green infrastructure, yet it faces multiple contradictions, such as insufficient quantity and uneven distribution. Taking Zhengzhou City as a case study, this research explored the impacts of temporal thresholds and the modifiable [...] Read more.

Urban park green space (UPGS) is a key component of urban green infrastructure, yet it faces multiple contradictions, such as insufficient quantity and uneven distribution. Taking Zhengzhou City as a case study, this research explored the impacts of temporal thresholds and the modifiable areal unit problem (MAUP) on UPGS accessibility and equity. An improved multi-modal Gaussian two-step floating catchment area (G2SFCA) method was employed to measure UPGS accessibility, while the Gini coefficient and Lorenz curve were used to analyze its equity. The results show that (1) UPGS presents a dual-core agglomeration feature, with accessibility blind spots surrounding the edge of the study area and relatively low equity in the western and southern regions; (2) changes in temporal thresholds and spatial scales have a significant impact on UPGS accessibility (p < 0.001), whereas their impact on equity is minor; and (3) UPGS distribution suffers from spatial imbalance, with a huge disparity in resource allocation. This study overcomes the limitations of traditional evaluation methods that rely on a single mode or ignore scale effects and provides a more scientific analytical framework for accurately identifying the spatial heterogeneity of UPGS accessibility and the imbalance between supply and demand. Full article

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36 pages, 39262 KB

Open AccessArticle

Exploration of Differences in Housing Price Determinants Based on Street View Imagery and the Geographical-XGBoost Model: Improving Quality of Life for Residents and Through-Travelers

by Shengbei Zhou, Qian Ji, Longhao Zhang, Jun Wu, Pengbo Li and Yuqiao Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(10), 391; https://doi.org/10.3390/ijgi14100391 - 9 Oct 2025

Viewed by 310

Abstract

Street design quality and socio-economic factors jointly influence housing prices, but their intertwined effects and spatial variations remain under-quantified. Housing prices not only reflect residents’ neighborhood experiences but also stem from the spillover value of public streets perceived and used by different users. [...] Read more.

Street design quality and socio-economic factors jointly influence housing prices, but their intertwined effects and spatial variations remain under-quantified. Housing prices not only reflect residents’ neighborhood experiences but also stem from the spillover value of public streets perceived and used by different users. This study takes Tianjin as a case and views the street environment as an immediate experience proxy for through-travelers, combining street view images and crowdsourced perception data to extract both subjective and objective indicators of the street environment, and integrating neighborhood and location characteristics. We use Geographical-XGBoost to evaluate the relative contributions of multiple factors to housing prices and their spatial variations. The results show that incorporating both subjective and objective street information into the Hedonic Pricing Model (HPM) improves its explanatory power, while local modeling with G-XGBoost further reveals significant heterogeneity in the strength and direction of effects across different locations. The results indicate that incorporating both subjective and objective street information into the HPM enhances explanatory power, while local modeling with G-XGBoost reveals significant heterogeneity in the strength and direction of effects across different locations. Street greening, educational resources, and transportation accessibility are consistently associated with higher housing prices, but their strength varies by location. Core urban areas exhibit a “counterproductive effect” in terms of complexity and recognizability, while peripheral areas show a “barely acceptable effect,” which may increase cognitive load and uncertainty for through-travelers. In summary, street environments and socio-economic conditions jointly influence housing prices via a “corridor-side–community-side” dual-pathway: the former (enclosure, safety, recognizability) corresponds to immediate improvements for through-travelers, while the latter (education and public services) corresponds to long-term improvements for residents. Therefore, core urban areas should control design complexity and optimize human-scale safety cues, while peripheral areas should focus on enhancing public services and transportation, and meeting basic quality thresholds with green spaces and open areas. Urban renewal within a 15 min walking radius of residential areas is expected to collaboratively improve daily travel experiences and neighborhood quality for both residents and through-travelers, supporting differentiated housing policy development and enhancing overall quality of life. Full article

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27 pages, 3999 KB

Open AccessArticle

Spatiotemporal Analysis of Urban Perception Using Multi-Year Street View Images and Deep Learning

by Wen Zhong, Lei Wang, Xin Han and Zhe Gao

ISPRS Int. J. Geo-Inf. 2025, 14(10), 390; https://doi.org/10.3390/ijgi14100390 - 8 Oct 2025

Viewed by 432

Abstract

Spatial perception is essential for understanding residents’ subjective experiences and well-being. However, effective methods for tracking changes in spatial perception over time and space remain limited. This study proposes a novel approach that leverages historical street view imagery to monitor the evolution of [...] Read more.

Spatial perception is essential for understanding residents’ subjective experiences and well-being. However, effective methods for tracking changes in spatial perception over time and space remain limited. This study proposes a novel approach that leverages historical street view imagery to monitor the evolution of urban spatial perception. Using the central urban area of Shanghai as a case study, we applied machine learning techniques to analyze 67,252 street view images from 2013 and 2019, aiming to quantify the spatiotemporal dynamics of urban perception. The results reveal the following: temporally, the average perception scores in 2019 increased by 4.85% compared to 2013; spatially, for every 1.5 km increase in distance from the city center, perception scores increased by an average of 0.0241; among all sampling points, 65.79% experienced an increase in perception, while 34.21% showed a decrease; and in terms of visual elements, natural features such as trees, vegetation, and roads were positively correlated with perception scores, whereas artificial elements like buildings, the sky, sidewalks, walls, and fences were negatively correlated. The analytical framework developed in this study offers a scalable method for measuring and interpreting changes in urban perception and can be extended to other cities. The findings provide valuable time-sensitive insights for urban planners and policymakers, supporting the development of more livable, efficient, and equitable urban environments. Full article

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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

Viewed by 411

Abstract

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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19 pages, 2549 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

Viewed by 373

Abstract

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 [...] 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: 15 min), 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: 60 min), 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

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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

Viewed by 647

Abstract

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

Viewed by 392

Abstract

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

Viewed by 368

Abstract

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

Viewed by 304

Abstract

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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33 pages, 20327 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

Viewed by 195

Abstract

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

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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

Viewed by 531

Abstract

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

Viewed by 307

Abstract

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

Viewed by 678

Abstract

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

Viewed by 642

Abstract

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

Viewed by 656

Abstract

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

Viewed by 507

Abstract

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

Viewed by 379

Abstract

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

Viewed by 533

Abstract

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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