ISPRS International Journal of Geo-Information

17 pages, 4366 KiB

Open AccessArticle

Large-Scale Point Cloud Semantic Segmentation with Density-Based Grid Decimation

by Liangcun Jiang, Jiacheng Ma, Han Zhou, Boyi Shangguan, Hongyu Xiao and Zeqiang Chen

ISPRS Int. J. Geo-Inf. 2025, 14(7), 279; https://doi.org/10.3390/ijgi14070279 - 17 Jul 2025

Accurate segmentation of point clouds into categories such as roads, buildings, and trees is critical for applications in 3D reconstruction and autonomous driving. However, large-scale point cloud segmentation encounters challenges such as uneven density distribution, inefficient sampling, and limited feature extraction capabilities. To [...] Read more.

Accurate segmentation of point clouds into categories such as roads, buildings, and trees is critical for applications in 3D reconstruction and autonomous driving. However, large-scale point cloud segmentation encounters challenges such as uneven density distribution, inefficient sampling, and limited feature extraction capabilities. To address these issues, this paper proposes RT-Net, a novel framework that incorporates a density-based grid decimation algorithm for efficient preprocessing of outdoor point clouds. The proposed framework helps alleviate the problem of uneven density distribution and improves computational efficiency. RT-Net also introduces two modules: Local Attention Aggregation, which extracts local detailed features of points using an attention mechanism, enhancing the model’s recognition ability for small-sized objects; and Attention Residual, which integrates local details of point clouds with global features by an attention mechanism to improve the model’s generalization ability. Experimental results on the Toronto3D, Semantic3D, and SemanticKITTI datasets demonstrate the superiority of RT-Net for small-sized object segmentation, achieving state-of-the-art mean Intersection over Union (mIoU) scores of 86.79% on Toronto3D and 79.88% on Semantic3D. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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19 pages, 3338 KiB

Open AccessArticle

Researching Stylistic Neutrality for Map Evaluation

by Rita Viliuviene and Sonata Vdovinskiene

ISPRS Int. J. Geo-Inf. 2025, 14(7), 278; https://doi.org/10.3390/ijgi14070278 - 16 Jul 2025

Viewed by 31

Abstract

Stylistic neutrality is the basis for the stylistic evaluation of maps. Furthermore, the stylistic neutrality of a map as a cartographic text may be related to objectivity. However, what constitutes stylistic neutrality is not clearly stated in the field of cartography. The problem [...] Read more.

Stylistic neutrality is the basis for the stylistic evaluation of maps. Furthermore, the stylistic neutrality of a map as a cartographic text may be related to objectivity. However, what constitutes stylistic neutrality is not clearly stated in the field of cartography. The problem is complicated by the fact that the stylistically neutral image is a hypothetical image. The aim of this research is to investigate stylistic neutrality by exploring the peculiarities of cartographic language functioning in different fields of social activity. The research combines descriptive analysis, stylistic analysis, cartographic and interpretative methods. Firstly, the research reveals the concept of cartographic stylistic neutrality, in line with the cartographic linguistic paradigm. Secondly, an analysis of the characteristics of cartographic language in different fields of social activity from the point of view of stylistic neutrality is carried out. Thirdly, an example is developed to illustrate stylistic cartographic neutrality. Stylistic neutrality is characterised by the stylistic features of cartographic language: clarity, accuracy, conciseness, calmness, abstractness, temperance, neutrality and moderateness. The style of cartographic production for inventory and research activities is closest to stylistic neutrality, while the style of reflective activity is the most expressive and acts as a source of concreteness for stylistic neutrality. Full article

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20 pages, 9405 KiB

Open AccessArticle

Developing a Hybrid Model to Enhance the Robustness of Interpretability for Landslide Susceptibility Assessment

by Xiao Yan, Dongshui Zhang, Yongshun Han, Tongsheng Li, Pin Zhong, Zhe Ning and Shirou Tan

ISPRS Int. J. Geo-Inf. 2025, 14(7), 277; https://doi.org/10.3390/ijgi14070277 - 16 Jul 2025

Viewed by 52

Abstract

Landslide is one of the most damaging natural hazards, causing extensive damage to the infrastructure and threatening human life. Although advances have been made in landslide susceptibility assessment by objective explainable machine learning, the interpretability robustness of traditional single landslide susceptibility model is [...] Read more.

Landslide is one of the most damaging natural hazards, causing extensive damage to the infrastructure and threatening human life. Although advances have been made in landslide susceptibility assessment by objective explainable machine learning, the interpretability robustness of traditional single landslide susceptibility model is still low. The proposed interpretable hybrid model in this study overcomes these challenges and aims to enhance the stability of landslide susceptibility interpretability. The model integrates three base machine learning models—LightGBM, XGBoost, and Random Forest—using a heterogeneous category strategy, thereby enhancing the robustness of model interpretability. The hybrid model is interpreted using SHAP (Shapley Additive Explanations) values, which quantify feature contributions. A 10-fold cross-validation with the coefficient of variation (CV) metric reveals that the hybrid model outperforms individual base models in terms of interpretive robustness, yielding a lower CV value of 0.175 compared to 0.208 for LightGBM, 0.240 for XGBoost, and 0.207 for the Random Forest model. Although predictive accuracy remains comparable to the baseline models, the hybrid model provides more stable and reliable interpretability results for landslide susceptibility. It identifies the slope, elevation, and LS factor as the three most important factors for landslide susceptibility in Xi’an city. Furthermore, the quantitative nonlinear relationships between these predisposing factors and susceptibility were identified, providing empowering knowledge for the landslides risk prevention and urban planning in the regions vulnerable to landslides. 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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26 pages, 39229 KiB

Open AccessArticle

Local–Linear Two-Stage Estimation of Local Autoregressive Geographically and Temporally Weighted Regression Model

by Dan Xiang and Zhimin Hong

ISPRS Int. J. Geo-Inf. 2025, 14(7), 276; https://doi.org/10.3390/ijgi14070276 - 16 Jul 2025

Viewed by 48

Abstract

A geographically and temporally weighted regression (GTWR) model is an effective tool for dealing with spatial heterogeneity and temporal non-stationarity simultaneously. As an important characteristic of spatiotemporal data, spatiotemporal autocorrelation should be considered when constructing spatiotemporally varying coefficient models. The proposed local autoregressive [...] Read more.

A geographically and temporally weighted regression (GTWR) model is an effective tool for dealing with spatial heterogeneity and temporal non-stationarity simultaneously. As an important characteristic of spatiotemporal data, spatiotemporal autocorrelation should be considered when constructing spatiotemporally varying coefficient models. The proposed local autoregressive geographically and temporally weighted regression (GTWRLAR) model can simultaneously handle spatiotemporal autocorrelations among response variables and the spatiotemporal heterogeneity of regression relationships. The two-stage weighted least squares (2SLS) estimation can effectively reduce computational complexity. However, the weighted least squares estimation is essentially a Nadaraya–Watson kernel-smoothing approach for nonparametric regression models, and it suffers from a boundary effect. For spatiotemporally varying coefficient models, the three-dimensional spatiotemporal coefficients (longitude, latitude, and time) inherently exhibit larger boundaries than one-dimensional intervals. Therefore, the boundary effect of the 2SLS estimation of GTWRLAR will be more serious. A local–linear geographically and temporally weighted 2SLS (GTWRLAR-L) estimation is proposed to correct the boundary effect in both the spatial and temporal dimensions of GTWRLAR and simultaneously improve parameter estimation accuracy. The simulation experiment shows that the GTWRLAR-L method reduces the root mean square error (RMSE) of parameter estimates compared to the standard GTWRLAR approach. Empirical analyses of carbon emissions in China’s Yellow River Basin (2017–2021) show that GTWRLAR-L enhances the adjusted

R^{2}

from 0.888 to 0.893. Full article

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22 pages, 4636 KiB

Open AccessArticle

SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks

by Chenghao Zheng, Yunfei Qiu, Jian Yang, Bianying Zhang, Zeyuan Li, Zhangxiang Lin, Xianglin Zhang, Yang Hou and Li Fang

ISPRS Int. J. Geo-Inf. 2025, 14(7), 275; https://doi.org/10.3390/ijgi14070275 - 15 Jul 2025

Viewed by 119

Abstract

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature [...] Read more.

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. Full article

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25 pages, 8409 KiB

Open AccessArticle

Airborne Lidar Refines Georeferencing Austro-Hungarian Maps from the First and Second Military Surveys

by Tibor Lieskovský, Tadeáš Kotleba, Jakub Šperka and Renata Ďuračiová

ISPRS Int. J. Geo-Inf. 2025, 14(7), 274; https://doi.org/10.3390/ijgi14070274 - 15 Jul 2025

Viewed by 83

Abstract

This paper explores ways to improve the coordinate transformation of maps from the First and Second Military Surveys of the Austro-Hungarian Monarchy using airborne laser scanning (ALS) data. The paper analyses the current positional accuracy of georeferenced maps from the first two military [...] Read more.

This paper explores ways to improve the coordinate transformation of maps from the First and Second Military Surveys of the Austro-Hungarian Monarchy using airborne laser scanning (ALS) data. The paper analyses the current positional accuracy of georeferenced maps from the first two military mappings from available spatial data sources. Several areas of interest with different terrain ruggedness (plain, undulated terrain, mountains) were selected for analysis to investigate whether terrain ruggedness has an impact on the accuracy of these maps. The next part of the paper deals with the georeferencing of military mapping maps using current, mid-20th-century maps and ALS data using affine and second-degree polynomial transformations. The paper concludes with a statistical analysis and evaluation of the potential of ALS data for solving this type of problem. The results obtained in the paper indicate that ALS data can be a suitable source for finding control points to transform early topographic maps. Full article

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24 pages, 8776 KiB

Open AccessArticle

Incremental Updating of 3D Indoor Data Considering Topological Linkages

by Qun Sun and Xinwu Zhan

ISPRS Int. J. Geo-Inf. 2025, 14(7), 273; https://doi.org/10.3390/ijgi14070273 - 10 Jul 2025

Viewed by 199

Abstract

Indoor location-based services and applications are heavily dependent on the currentness of indoor data. Therefore, it is crucial to update indoor spatial information promptly and efficiently to ensure its relevance and reliability. Maintaining the topological consistency of geometric objects presents a significant challenge [...] Read more.

Indoor location-based services and applications are heavily dependent on the currentness of indoor data. Therefore, it is crucial to update indoor spatial information promptly and efficiently to ensure its relevance and reliability. Maintaining the topological consistency of geometric objects presents a significant challenge in updating indoor data. Consequently, this paper introduces an incremental updating method for 3D indoor data that considers topological linkages. The first step involves categorizing different types of building component changes and their corresponding indoor space alterations, followed by a detailed analysis of the topological linkage types for indoor features. On the basis of these identified changes, a set of updating operators is developed to handle various types of indoor space alterations. The experimental results demonstrate that the proposed updating operations effectively maintain the topological relationships of solids and the topological adjacency relationships of adjacent solids. This method facilitates efficient querying of indoor spatial information and topological adjacencies, thereby providing a robust data foundation for indoor location-based services and applications. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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29 pages, 8640 KiB

Open AccessArticle

A Multi-Objective Optimization and Decision Support Framework for Natural Daylight and Building Areas in Community Elderly Care Facilities in Land-Scarce Cities

by Fang Wen, Lu Zhang, Ling Jiang, Wenqi Sun, Tong Jin and Bo Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(7), 272; https://doi.org/10.3390/ijgi14070272 - 10 Jul 2025

Viewed by 181

Abstract

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make [...] Read more.

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

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27 pages, 3868 KiB

Open AccessArticle

Spatiotemporal Evolution and Driving Factors of Coupling Coordination Degree Between New Urbanization and Urban Resilience: A Case of Huaihai Economic Zone

by Heng Zhang, Shuang Li and Jiang Chang

ISPRS Int. J. Geo-Inf. 2025, 14(7), 271; https://doi.org/10.3390/ijgi14070271 - 9 Jul 2025

Viewed by 348

Abstract

Rapid urbanization and climate extremes expose cities to multi-dimensional risks, necessitating the coordinated development of new urbanization and urban resilience for achieving urban sustainability. While existing studies focus on core economic zones like the Yangtze River Delta, secondary economic cooperation regions remain understudied. [...] Read more.

Rapid urbanization and climate extremes expose cities to multi-dimensional risks, necessitating the coordinated development of new urbanization and urban resilience for achieving urban sustainability. While existing studies focus on core economic zones like the Yangtze River Delta, secondary economic cooperation regions remain understudied. This study examined the Huaihai Economic Zone (HEZ)—a quadri-provincial border area—by constructing the evaluation systems for new urbanization and urban resilience. The development indices of the two systems were measured using the entropy weight-CRITIC method. The spatiotemporal evolution characteristics of their coupling coordination degree (CCD) were analyzed through a CCD model, while key driving factors influencing the CCD were investigated using a grey relational analysis model. The results indicated that both the new urbanization construction and urban resilience development indices in the HEZ exhibited a steady upward trend during the study period, with the urban resilience development index surpassing the new urbanization construction index. The new urbanization index increased from 0.3026 (2013) to 0.4702 (2023), and the urban resilience index increased from 0.3520 (2013) to 0.6366 (2023). The CCD between new urbanization and urban resilience reached 0.7368 by 2023, with 80% of cities in the HEZ achieving good coordination types. The variation of the CCD among cities was minimal, revealing a spatially clustered coordinated development pattern. In terms of driving factors, economic development level, public service capacity, and municipal resilience level were identified as core drivers for enhancing coupling coordination. Infrastructure construction, digital capabilities, and spatial intensification served as important supports, while ecological governance capacity remained a weakness. This study establishes a transferable framework for the coordinated development of secondary economic cooperation region, though future research should integrate diverse data sources and expand indicator coverage for higher precision. Moreover, the use of linear models to analyze the key driving factors of the CCD has limitations. The incorporation of non-linear techniques can better elucidate the complex interactions among factors. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—2nd Edition)

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29 pages, 6616 KiB

Open AccessArticle

Forecasting Carbon Emissions by Considering the Joint Influences of Urban Form and Socioeconomic Development—An Empirical Study in Guangdong, China

by Zhijie Rao, Jiapei Li and Jinyao Lin

ISPRS Int. J. Geo-Inf. 2025, 14(7), 270; https://doi.org/10.3390/ijgi14070270 - 9 Jul 2025

Viewed by 182

Abstract

Carbon emission forecasting is a critical step in addressing climate change and effective environmental management. However, previous studies have concentrated mainly on socioeconomic factors, with less attention directed toward the significant impact of urban form. To address the shortcomings of previous studies, this [...] Read more.

Carbon emission forecasting is a critical step in addressing climate change and effective environmental management. However, previous studies have concentrated mainly on socioeconomic factors, with less attention directed toward the significant impact of urban form. To address the shortcomings of previous studies, this study introduced three types of landscape indices that can characterize urban form and combined them with conventional socioeconomic factors to create a new carbon emission forecasting method. The enhanced STIRPAT and PLUS models were employed to forecast future changes in various socioeconomic factors and urban form, with the aim of forecasting carbon emissions in 21 cities of Guangdong during 2025–2060. The results confirm that urban form has an obvious influence on carbon emissions. In comparison to the baseline model, which considered only socioeconomic factors, the incorporation of urban form into the carbon emission forecast resulted in a reduction in the mean absolute percentage error from 7.16% to 6.18%. Moreover, carbon emissions were found to be positively correlated with GDP per capita, energy intensity, permanent population, share of secondary sector, LSI, and PLADJ but negatively correlated with PD. Furthermore, Guangdong will not be able to accomplish its “carbon peaking” objective around 2030, except in a low-carbon situation. Our proposed method could enhance the rationality of carbon emission forecasting, thereby providing a reasonable decision-making basis for low-carbon management. Full article

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23 pages, 4005 KiB

Open AccessArticle

Exploring Unconventional 3D Geovisualization Methods for Land Suitability Assessment: A Case Study of Jihlava City

by Oldrich Bittner, Jakub Zejdlik, Jaroslav Burian and Vit Vozenilek

ISPRS Int. J. Geo-Inf. 2025, 14(7), 269; https://doi.org/10.3390/ijgi14070269 - 8 Jul 2025

Viewed by 218

Abstract

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability [...] Read more.

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

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18 pages, 1537 KiB

Open AccessArticle

HierLabelNet: A Two-Stage LLMs Framework with Data Augmentation and Label Selection for Geographic Text Classification

by Zugang Chen and Le Zhao

ISPRS Int. J. Geo-Inf. 2025, 14(7), 268; https://doi.org/10.3390/ijgi14070268 - 8 Jul 2025

Viewed by 187

Abstract

Earth observation data serve as a fundamental resource in Earth system science. The rapid advancement of remote sensing and in situ measurement technologies has led to the generation of massive volumes of data, accompanied by a growing body of geographic textual information. Efficient [...] Read more.

Earth observation data serve as a fundamental resource in Earth system science. The rapid advancement of remote sensing and in situ measurement technologies has led to the generation of massive volumes of data, accompanied by a growing body of geographic textual information. Efficient and accurate classification and management of these geographic texts has become a critical challenge in the field. However, the effectiveness of traditional classification approaches is hindered by several issues, including data sparsity, class imbalance, semantic ambiguity, and the prevalence of domain-specific terminology. To address these limitations and enable the intelligent management of geographic information, this study proposes an efficient geographic text classification framework based on large language models (LLMs), tailored to the unique semantic and structural characteristics of geographic data. Specifically, LLM-based data augmentation strategies are employed to mitigate the scarcity of labeled data and class imbalance. A semantic vector database is utilized to filter the label space prior to inference, enhancing the model’s adaptability to diverse geographic terms. Furthermore, few-shot prompt learning guides LLMs in understanding domain-specific language, while an output alignment mechanism improves classification stability for complex descriptions. This approach offers a scalable solution for the automated semantic classification of geographic text for unlocking the potential of ever-expanding geospatial big data, thereby advancing intelligent information processing and knowledge discovery in the geospatial domain. Full article

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25 pages, 2294 KiB

Open AccessArticle

Visualising Spatial Dispersion in Cultural Heritage Data

by Laya Targa, Esperanza Villuendas, Cristina Portalés and Jorge Sebastián

ISPRS Int. J. Geo-Inf. 2025, 14(7), 267; https://doi.org/10.3390/ijgi14070267 - 8 Jul 2025

Viewed by 248

Abstract

The digitisation of cultural heritage has transformed how GLAM (Galleries, Libraries, Archives and Museums) institutions manage and share collections. Digital catalogues are indispensable for documenting and granting public access to cultural assets. However, integrating spatial data remains challenging due to the ambiguity, uncertainty, [...] Read more.

The digitisation of cultural heritage has transformed how GLAM (Galleries, Libraries, Archives and Museums) institutions manage and share collections. Digital catalogues are indispensable for documenting and granting public access to cultural assets. However, integrating spatial data remains challenging due to the ambiguity, uncertainty, granularity, and heterogeneity of historical data. This study addresses these issues through a case study on the Museo de América’s “Place of Provenance” data, proposing a methodology for data cleaning and evaluating geocoding accuracy using Nominatim, ArcGIS, and GeoNames APIs. We assess these APIs by quantifying geocoding errors through a “balance sheet” method, identifying instances of over-representation, under-representation, or neutral results for geographical regions. The effectiveness of each API is analysed using confusion matrices and interactive cartograms, offering insights into misallocations. Our findings reveal varying accuracy among the APIs in processing heterogeneous historical spatial data. Nominatim achieved a 40.91% neutral result in correctly geocoding countries, underscoring challenges in spatial data representation. This research provides valuable methodological experiences and insights for researchers and GLAM institutions working with cultural heritage datasets. By enhancing spatial dispersion visualisation, this work contributes to understanding cultural circulations and historical patterns. This interdisciplinary work was developed as part of the ClioViz project, integrating Data Science, data Visualisation, and art history. Full article

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25 pages, 11288 KiB

Open AccessArticle

Evaluation of Urban Street Historical Appearance Integrity Based on Street View Images and Transfer Learning

by Jiarui Xu, Yunxuan Dai, Jiatong Cai, Haoliang Qian, Zimu Peng and Teng Zhong

ISPRS Int. J. Geo-Inf. 2025, 14(7), 266; https://doi.org/10.3390/ijgi14070266 - 7 Jul 2025

Viewed by 251

Abstract

The challenges of globalization and urbanization increasingly impact the Historic Urban Landscape (HUL), yet fine-grained and quantitative methods for evaluating HUL remain limited. Adopting a human-centered perspective, this study introduces a novel framework to quantitatively evaluate HUL through the lens of Historical Appearance [...] Read more.

The challenges of globalization and urbanization increasingly impact the Historic Urban Landscape (HUL), yet fine-grained and quantitative methods for evaluating HUL remain limited. Adopting a human-centered perspective, this study introduces a novel framework to quantitatively evaluate HUL through the lens of Historical Appearance Integrity (HAI). An evaluation system comprising four key dimensions (building materials, building colors, decorative details, and streetscape morphology) was constructed using the Analytic Hierarchy Process (AHP). An Elo rating system was subsequently applied to quantify the scores of the indicators. A prediction model was developed based on transfer learning and feature fusion to estimate the scores of the indicators. The model achieved accuracies above 93% and loss values below 0.2 for all four indicators. The framework was applied to the Inner Qinhuai Historical Character Area in Nanjing for validation. Results show that the spatial distribution of HAI in the area exhibits significant spatial heterogeneity. On a 0–100 scale, the average HAI scores were 23.17 for primary roads, 27.73 for secondary roads, and 46.93 for branch roads. This study offers a fine-grained, automated approach to evaluate HAI along urban streets and provides a quantitative reference for heritage conservation and urban renewal strategies. Full article

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

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26 pages, 33866 KiB

Open AccessArticle

Three-Dimensional Multitemporal Game Engine Visualizations for Watershed Analysis, Lighting Simulation, and Change Detection in Built Environments

by Heikki Kauhanen, Toni Rantanen, Petri Rönnholm, Osama Bin Shafaat, Kaisa Jaalama, Arttu Julin and Matti Vaaja

ISPRS Int. J. Geo-Inf. 2025, 14(7), 265; https://doi.org/10.3390/ijgi14070265 - 5 Jul 2025

Viewed by 390

Abstract

This study explores the reuse of high-resolution 3D spatial datasets for multiple urban analyses within a game engine environment, aligning with circular economy principles in sustainable urban planning. The work is situated in two residential test areas in Finland, where watershed analysis, lighting [...] Read more.

This study explores the reuse of high-resolution 3D spatial datasets for multiple urban analyses within a game engine environment, aligning with circular economy principles in sustainable urban planning. The work is situated in two residential test areas in Finland, where watershed analysis, lighting simulation, and change detection were conducted using data acquired through drone photogrammetry and terrestrial laser scanning. These datasets were processed and visualized using Unreal Engine 5.5, enabling the interactive, multitemporal exploration of urban phenomena. The results demonstrate how a single photogrammetric dataset—originally captured for visual or structural purposes—can serve a broad range of analytical functions, such as simulating seasonal lighting conditions, modeling stormwater runoff, and visualizing spatial changes over time. The study highlights the importance of capturing data at a resolution that satisfies the most demanding intended use, while allowing simpler analyses to benefit simultaneously. Reflections on game engine capabilities, data quality thresholds, and user interactivity underline the feasibility of integrating such tools into citizen participation, housing company decision making, and urban governance. The findings advocate for a circular data approach in urban planning, reducing redundant fieldwork and supporting sustainable data practices through multi-purpose digital twins and spatial simulations. Full article

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27 pages, 110289 KiB

Open AccessArticle

Automated Digitization Approach for Road Intersections Mapping: Leveraging Azimuth and Curve Detection from Geo-Spatial Data

by Ahmad M. Senousi, Wael Ahmed, Xintao Liu and Walid Darwish

ISPRS Int. J. Geo-Inf. 2025, 14(7), 264; https://doi.org/10.3390/ijgi14070264 - 5 Jul 2025

Viewed by 270

Abstract

Effective maintenance and management of road infrastructure are essential for community well-being, economic stability, and cost efficiency. Well-maintained roads reduce accident risks, improve safety, shorten travel times, lower vehicle repair costs, and facilitate the flow of goods, all of which positively contribute to [...] Read more.

Effective maintenance and management of road infrastructure are essential for community well-being, economic stability, and cost efficiency. Well-maintained roads reduce accident risks, improve safety, shorten travel times, lower vehicle repair costs, and facilitate the flow of goods, all of which positively contribute to GDP and economic development. Accurate intersection mapping forms the foundation of effective road asset management, yet traditional manual digitization methods remain time-consuming and prone to gaps and overlaps. This study presents an automated computational geometry solution for precise road intersection mapping that eliminates common digitization errors. Unlike conventional approaches that only detect intersection positions, our method systematically reconstructs complete intersection geometries while maintaining topological consistency. The technique combines plane surveying principles (including line-bearing analysis and curve detection) with spatial analytics to automatically identify intersections, characterize their connectivity patterns, and assign unique identifiers based on configurable parameters. When evaluated across multiple urban contexts using diverse data sources (manual digitization and OpenStreetMap), the method demonstrated consistent performance with mean Intersection over Union greater than 0.85 and F-scores more than 0.91. The high correctness and completeness metrics (both more than 0.9) confirm its ability to minimize both false positive and omission errors, even in complex roadway configurations. The approach consistently produced gap-free, overlap-free outputs, showing strength in handling interchange geometries. The solution enables transportation agencies to make data-driven maintenance decisions by providing reliable, standardized intersection inventories. Its adaptability to varying input data quality makes it particularly valuable for large-scale infrastructure monitoring and smart city applications. Full article

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21 pages, 5894 KiB

Open AccessArticle

A Reversible Compression Coding Method for 3D Property Volumes

by Zhigang Zhao, Jiahao Qiu, Han Guo, Wei Zhu and Chengpeng Li

ISPRS Int. J. Geo-Inf. 2025, 14(7), 263; https://doi.org/10.3390/ijgi14070263 - 5 Jul 2025

Viewed by 267

Abstract

3D (three-dimensional) property volume is an important data carrier for 3D land administration by using 3D cadastral technology, which can be used to express the legal space (property rights) scope matching with physical entities such as buildings and land. A 3D property volume [...] Read more.

3D (three-dimensional) property volume is an important data carrier for 3D land administration by using 3D cadastral technology, which can be used to express the legal space (property rights) scope matching with physical entities such as buildings and land. A 3D property volume is represented by a dense set of 3D coordinate points arranged in a predefined order and is displayed alongside the parcel map for reference and utilization by readers. To store a 3D property volume in the database, it is essential to record the connectivity relationships among the original 3D coordinate points, the associations between points and lines for representing boundary lines, and the relationships between lines for defining surfaces. Only by preserving the data structure that represents the relationships among points, lines, and surfaces can the 3D property volume in a parcel map be fully reconstructed. This approach inevitably results in the database storage volume significantly exceeding the original size of the point set, thereby causing storage redundancy. Consequently, this paper introduces a reversible 3D property volume compression coding method (called 3DPV-CC) to address this issue. By analyzing the distribution characteristics of the coordinate points of the 3D property volume, a specific rule for sorting the coordinate points is designed, enabling the database to have the ability of data storage and recovery by merely storing a reordered point set. The experimental results show that the 3DPV-CC method has excellent support capabilities for 3D property volumes of the vertical and slopped types, and can compress and restore the coordinate point set of the 3D property volume for drawing 3D parcel maps. The compression capacity of our method in the test is between 23.66% and 38.42%, higher than the general data compression methods (ZIP/7Z/RAR: 8.37–10.32%). By means of this method, land or real estate administrators from government departments can store 3D property volume data at a lower cost. This is conducive to enhancing the informatization level of land management. Full article

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20 pages, 2669 KiB

Open AccessArticle

Exploring the Impact of Multi-Source Gridded Population Datasets on Flood-Exposed Population Estimates in Gangnam, Seoul

by Julieber T. Bersabe and Byong-Woon Jun

ISPRS Int. J. Geo-Inf. 2025, 14(7), 262; https://doi.org/10.3390/ijgi14070262 - 4 Jul 2025

Viewed by 298

Abstract

Accurate demographic data are essential for evaluating flood exposure in urban areas, where heterogeneous environment and localized risks complicate modeling efforts. Gridded population datasets serve as valuable resources for such assessments; however, differences in spatial resolution and methodology can significantly affect flood-exposed population [...] Read more.

Accurate demographic data are essential for evaluating flood exposure in urban areas, where heterogeneous environment and localized risks complicate modeling efforts. Gridded population datasets serve as valuable resources for such assessments; however, differences in spatial resolution and methodology can significantly affect flood-exposed population estimates. This study evaluates how various gridded population datasets influence the sensitivity and accuracy of flood exposure estimates in Gangnam District, Seoul. Seven datasets from Statistical Geographic Information Service (SGIS), National Geographic Information Institute (NGII), and Intelligent Dasymetric Mapping (IDM), ranging from 30 m to 1 km in resolution, were evaluated against census data to assess their accuracy and variability in flood exposure estimates. The results indicate that multi-source gridded population datasets with different spatial resolutions and modeling approaches strongly affect both the accuracy and variability of flood-exposed population estimates. IDM 30 m outperformed other datasets, showing the lowest variability (CV = 0.310) and the highest agreement with census data (RMSE = 193.51; R² = 0.9998). Coarser datasets showed greater estimation errors and variability. These findings demonstrate that fine-resolution IDM population dataset yields reliable results for flood exposure estimation in Gangnam, Seoul. They also highlight the need for further comparative evaluations across different hazard and spatial contexts. Full article

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17 pages, 7452 KiB

Open AccessArticle

A Spatial-Network Approach to Assessing Transportation Resilience in Disaster-Prone Urban Areas

by Francesco Rouhana and Dima Jawad

ISPRS Int. J. Geo-Inf. 2025, 14(7), 261; https://doi.org/10.3390/ijgi14070261 - 3 Jul 2025

Viewed by 331

Abstract

Critical transportation networks in developing countries often lack structural robustness and functional redundancy due to insufficient planning and preparedness. These deficiencies increase vulnerability to disruptions and impede effective post-disaster response and recovery. Understanding how such networks perform under stress is essential to improving [...] Read more.

Critical transportation networks in developing countries often lack structural robustness and functional redundancy due to insufficient planning and preparedness. These deficiencies increase vulnerability to disruptions and impede effective post-disaster response and recovery. Understanding how such networks perform under stress is essential to improving resilience in hazard-prone urban environments. This paper presents an integrated predictive methodology for assessing the operational resilience of urban transportation networks under extreme events, specifically tailored to data-scarce and high-risk contexts. By combining Geographic Information Systems (GISs) with complex network theory, the framework captures both spatial and topological dependencies. The methodology is applied to Beirut, the capital of Lebanon, a densely populated and disaster-prone Mediterranean city, through scenario-based simulations that account for interdependent stressors such as traffic dynamics, structural fragility, and geophysical hazards. Results reveal that the network exhibits low redundancy and high sensitivity to even minor disruptions, leading to rapid performance degradation. These findings indicate that the network should be classified as highly vulnerable. The study offers a robust framework for assessing infrastructure resilience and supporting evidence-based decision-making in critical urban network management. Full article

(This article belongs to the Topic Geotechnics for Hazard Mitigation)

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25 pages, 10430 KiB

Open AccessArticle

Investigating the Impact of Inter-City Patient Mobility on Local Residents’ Equity in Access to High-Level Healthcare: A Case Study of Beijing

by Zhiqing Li and Zhenbao Wang

ISPRS Int. J. Geo-Inf. 2025, 14(7), 260; https://doi.org/10.3390/ijgi14070260 - 2 Jul 2025

Viewed by 278

Abstract

The equitable allocation of healthcare resources reflects social equity. Previous studies of healthcare accessibility have overlooked the impact of inter-city patient mobility on local residents’ and local residents’ multi-mode travel choices, distorting accessibility calculation outcomes. Taking the area within Beijing’s Sixth Ring Road [...] Read more.

The equitable allocation of healthcare resources reflects social equity. Previous studies of healthcare accessibility have overlooked the impact of inter-city patient mobility on local residents’ and local residents’ multi-mode travel choices, distorting accessibility calculation outcomes. Taking the area within Beijing’s Sixth Ring Road as an example, this study established a Multi-Mode Accessibility Model for Local Residents (MMALR) to tertiary hospitals, using the proportion of non-local patients to adjust hospital supply capacity and considering the various travel mode shares from residential communities to hospitals to calculate the number of potential patients. We compared the changes in geospatial accessibility under different travel modes and employed the Gini coefficient to evaluate the geospatial equity of accessibility for different regions when using different accessibility methods. The results indicate that the spatial distribution of healthcare accessibility via different methods is similar, and it gradually decreases along subway lines from the urban center to the periphery. We found that the equities in access to high-level healthcare for Dongcheng District, Xicheng District, the area between the Third and Fourth Ring Road, and the area between the Fourth and Fifth Ring Road, display different ranking results across different methods, revealing that an unreasonable analysis framework could mislead the placement decisions for new hospitals or the allocation of medical resources. These findings emphasize the impact of inter-city patient mobility and the diversity of travel mode choices on accessibility. Our model can assist stakeholders in more accurately evaluating the accessibility and equity of local residents in terms of tertiary hospitals, which is crucial for cities with abundant medical resources and superior conditions. Our analytical findings provide a scientific basis for the location decisions of tertiary hospitals. Full article

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19 pages, 2601 KiB

Open AccessArticle

An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata

by Ahmet Emir Yakup and Ismail Ercument Ayazli

ISPRS Int. J. Geo-Inf. 2025, 14(7), 259; https://doi.org/10.3390/ijgi14070259 - 1 Jul 2025

Viewed by 419

Abstract

Monitoring urban growth through simulation models is becoming increasingly vital for the sustainable management of cities. Although various raster-based models have been developed over the past three decades, the irregular, fragmented, and heterogeneous geometric structure of urban areas poses significant challenges to effectively [...] Read more.

Monitoring urban growth through simulation models is becoming increasingly vital for the sustainable management of cities. Although various raster-based models have been developed over the past three decades, the irregular, fragmented, and heterogeneous geometric structure of urban areas poses significant challenges to effectively modeling complex land use and land cover (LULC) transitions. To address these limitations, this study proposes a novel urban growth simulation model based on vector cellular automata (VCA). In this model, dynamic neighborhood relationships are flexibly established using an algorithm called growth vectors (GVs). Open-access data from four time periods between 1990 and 2018 were utilized for three major European metropolitan areas: Istanbul, Berlin, and Madrid. During the calibration phase, the model was trained using three machine learning algorithms: Random forest, support vector machine, and multi-layer perceptron. For the simulation phase, an adaptive VCA-based urban growth model was developed to predict LULC changes through to 2040. The results demonstrate that the proposed algorithm can achieve a satisfactory level of accuracy in modeling urban growth. Full article

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

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22 pages, 9599 KiB

Open AccessArticle

The Impact of New Subway Construction on the Commuting Methods and Time Utilization of Residents Along the Line: A Comparison Before and After the Completion of the Subway in Kunming City

by Kun Zhao and Baohong He

ISPRS Int. J. Geo-Inf. 2025, 14(7), 258; https://doi.org/10.3390/ijgi14070258 - 1 Jul 2025

Viewed by 321

Abstract

Subway construction changes land use patterns, disrupting the balance between traffic supply and demand and influencing residents’ activity and travel behaviors along the route. Previous studies have often overlooked the multiple attributes of the subway and their varying impacts on daily activities. To [...] Read more.

Subway construction changes land use patterns, disrupting the balance between traffic supply and demand and influencing residents’ activity and travel behaviors along the route. Previous studies have often overlooked the multiple attributes of the subway and their varying impacts on daily activities. To understand how subway construction affects travel and activity patterns, this study analyzed travel data from two years before and after the opening of the Kunming Subway. Propensity score matching was used to control for socioeconomic changes unrelated to the subway. The findings show the following: (1) The subway’s introduction leads to the development of commercial and civic amenities around its stations, attracting wealthier and more educated residents, which contributes to gentrification. (2) Overall, subway construction extends urban residential and employment areas, increasing residents’ dependence on cars and promoting a more motor-centric lifestyle. As a result, the subway’s ability to reduce car usage is limited. (3) The subway alters the impact of the built environment on travel behavior, with residents closer to the subway experiencing shorter travel distances and reduced activity spaces, while those further away maintain longer travel distances and greater activity areas. Full article

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35 pages, 72080 KiB

Open AccessReview

Submarine Terrain Generalization in Nautical Charts: A Survey of Traditional Methods and Graph Neural Network Solutions

by Taoning Dong, Ruifu Wang, Pengxv Chen, Chenyue Sun, Chaohua Gan, Jiayi Liu and Anmin Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(7), 257; https://doi.org/10.3390/ijgi14070257 - 30 Jun 2025

Viewed by 417

Abstract

The generalization of nautical charts remains crucial in geographic information science and cartography. Traditional geometry-based methods have contributed to the advancement of automated generalization to a certain extent, but they still exhibit significant limitations in handling complex marine spatial relationships. This paper proposes [...] Read more.

The generalization of nautical charts remains crucial in geographic information science and cartography. Traditional geometry-based methods have contributed to the advancement of automated generalization to a certain extent, but they still exhibit significant limitations in handling complex marine spatial relationships. This paper proposes the Graph Neural Network (GNN) as a transformative solution. GNN excels at processing non-Euclidean geospatial data, addressing the following three critical problems in the generalization of submarine terrain data: geographic feature representation, data processing, and the generalization process. The review first systematically outlines the main operators and fundamental methods of chart generalization. It analyzes their specific performance in various elements such as soundings, depth contours, islands, and coastlines. Subsequently, the potential of GNN is explored in addressing the limitations of traditional generalization methods. Although GNN is not a panacea, it shows advantages through horizontal and vertical comparisons. Finally, the challenges encountered in applying GNN to cartographic generalization are discussed. Full article

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30 pages, 4736 KiB

Open AccessArticle

AutoGEEval: A Multimodal and Automated Evaluation Framework for Geospatial Code Generation on GEE with Large Language Models

by Huayi Wu, Zhangxiao Shen, Shuyang Hou, Jianyuan Liang, Haoyue Jiao, Yaxian Qing, Xiaopu Zhang, Xu Li, Zhipeng Gui, Xuefeng Guan and Longgang Xiang

ISPRS Int. J. Geo-Inf. 2025, 14(7), 256; https://doi.org/10.3390/ijgi14070256 - 30 Jun 2025

Viewed by 331

Abstract

Geospatial code generation is emerging as a key direction in the integration of artificial intelligence and geoscientific analysis. However, there remains a lack of standardized tools for automatic evaluation in this domain. To address this gap, we propose AutoGEEval, the first multimodal, unit-level [...] Read more.

Geospatial code generation is emerging as a key direction in the integration of artificial intelligence and geoscientific analysis. However, there remains a lack of standardized tools for automatic evaluation in this domain. To address this gap, we propose AutoGEEval, the first multimodal, unit-level automated evaluation framework for geospatial code generation tasks on the Google Earth Engine (GEE) platform powered by large language models (LLMs). Built upon the GEE Python API, AutoGEEval establishes a benchmark suite (AutoGEEval-Bench) comprising 1325 test cases that span 26 GEE data types. The framework integrates both question generation and answer verification components to enable an end-to-end automated evaluation pipeline—from function invocation to execution validation. AutoGEEval supports multidimensional quantitative analysis of model outputs in terms of accuracy, resource consumption, execution efficiency, and error types. We evaluate 18 state-of-the-art LLMs—including general-purpose, reasoning-augmented, code-centric, and geoscience-specialized models—revealing their performance characteristics and potential optimization pathways in GEE code generation. This work provides a unified protocol and foundational resource for the development and assessment of geospatial code generation models, advancing the frontier of automated natural language to domain-specific code translation. Full article

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28 pages, 56125 KiB

Open AccessArticle

Capturing Built Environment and Automated External Defibrillator Resource Interplay in Tianjin Downtown

by Sara Grigoryan, Yike Hu and Nadeem Ullah

ISPRS Int. J. Geo-Inf. 2025, 14(7), 255; https://doi.org/10.3390/ijgi14070255 - 30 Jun 2025

Viewed by 325

Abstract

Automated external defibrillator resources (AEDRs) are the crux of out-of-hospital cardiac arrest (OHCA) responses, enhancing safe and sustainable urban environments. However, existing studies failed to consider the nexus between built environment (BE) features and AEDRs. Can explainable machine-learning (ML) methods reveal the BE-AEDR [...] Read more.

Automated external defibrillator resources (AEDRs) are the crux of out-of-hospital cardiac arrest (OHCA) responses, enhancing safe and sustainable urban environments. However, existing studies failed to consider the nexus between built environment (BE) features and AEDRs. Can explainable machine-learning (ML) methods reveal the BE-AEDR nexus? This study applied an Optuna-based extreme gradient boosting (OP_XGBoost) decision tree model with SHapely Additive exPlanations (SHAP) and partial dependence plots (PDPs) aiming to scrutinize the spatial effects, relative importance, and non-linear impact of BE features on AEDR intensity across grid and block urban patterns in Tianjin Downtown, China. The results indicated, that (1) marginally, the AEDR intensity was most influenced by the service coverage (SC) at grid scale and nearby public service facility density (NPSF_D) at block scale, while synergistically, it was shaped by comprehensive accessibility and land-use interactions with the prioritized block pattern; (2) block-level granularity and (3) non-linear interdependencies between BE features and AEDR intensity existed as game-changers. The findings suggested an effective and generalizable approach to capture the complex interplay of the BE-AEDR and boost the AED deployment by setting health at the heart of the urban development framework. Full article

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

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9 pages, 222 KiB

Open AccessEditorial

Geographic Information Systems and Cartography for a Sustainable World

by Andriani Skopeliti, Anastasia Stratigea, Vassilios Krassanakis and Apostolos Lagarias

ISPRS Int. J. Geo-Inf. 2025, 14(7), 254; https://doi.org/10.3390/ijgi14070254 - 30 Jun 2025

Viewed by 426

Abstract

This article summarizes the scope and content of the Special Issue (SI) entitled “Geographic Information Systems (GIS) and Cartography for a Sustainable World” and its contribution to the global discourse regarding sustainability concerns. At the heart of the discussion in this SI lies: [...] Read more.

This article summarizes the scope and content of the Special Issue (SI) entitled “Geographic Information Systems (GIS) and Cartography for a Sustainable World” and its contribution to the global discourse regarding sustainability concerns. At the heart of the discussion in this SI lies: (i) GIS, a valuable tool and a means for modeling, designing, and analyzing (spatial) data and processes related to the pursuance of sustainability objectives at both local and global scales; and (ii) Cartography as a discipline, which through maps and visualizations can convey the present state. The latter can play a vital role in educating, empowering, and raising public awareness with regard to sustainability concerns on the one hand, and can form a basis for policy-makers, scientists, and citizens for articulating effective sustainability strategies on the other. The fulfillment of the SI goals is attained through a collection of 26 papers that delve into and attempt to visualize sustainability achievements or concerns on a variety of themes in different parts of the world. More specifically, the content of this collection of papers can be categorized into the following sustainability-related themes: Urbanization, Transportation, Carbon Emissions Management, Infrastructure, Rural Development, and Climate Change. The main conclusion is that planning and implementing sustainability policies is a challenging and multi-level task, and must be carried out within a fully dynamic decision environment. Although some progress has already been made, more intensive and collective efforts from scientists, governments, the entrepreneurial community, and citizens are needed in order for the ambitious goals of Agenda 2030 to be reached. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

18 pages, 16726 KiB

Open AccessArticle

Spatial Accessibility to Healthcare Facilities: GIS-Based Public–Private Comparative Analysis Using Floating Catchment Methods

by Onel Pérez-Fernández and Gregorio Rosario Michel

ISPRS Int. J. Geo-Inf. 2025, 14(7), 253; https://doi.org/10.3390/ijgi14070253 - 29 Jun 2025

Viewed by 638

Abstract

Healthcare accessibility is among the most critical challenges affecting millions, reflecting profound geospatial disparities in Latin America. This study aims to evaluate healthcare service geospatial accessibility patterns, comparing the geospatial coverage between public and private healthcare facilities in Santiago district, Panama. We first [...] Read more.

Healthcare accessibility is among the most critical challenges affecting millions, reflecting profound geospatial disparities in Latin America. This study aims to evaluate healthcare service geospatial accessibility patterns, comparing the geospatial coverage between public and private healthcare facilities in Santiago district, Panama. We first apply the Two-Step Floating Catchment Area (2SFCA) method and its extended variant (E2SFCA) to calculate geospatial accessibility indexes at public and private healthcare facilities. We then use Getis–Ord Gi* and Local Moran geospatial statistical analysis to identify significant clusters of high and low accessibility. The results reveal that public healthcare facilities still offer higher geospatial coverage than private healthcare facilities, with higher geospatial accessibility in the central zone and lower geospatial accessibility in the south zone of Santiago. These findings highlighted the location of new healthcare facilities in zones with lower geospatial accessibility coverage. This study provides reproducible methodological tools for other geographical contexts. It also contributes to improving decision-making and formulating public policies to reduce spatial disparities in healthcare services in Panama and other Caribbean and Latin American countries. Full article

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

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29 pages, 9378 KiB

Open AccessArticle

Representing the Spatiotemporal State Evolution of Geographic Entities as a Multi-Level Graph

by Feng Yuan, Penglin Zhang, Qi Zhang, Yu Zhang and Anni Wang

ISPRS Int. J. Geo-Inf. 2025, 14(7), 252; https://doi.org/10.3390/ijgi14070252 - 28 Jun 2025

Viewed by 255

Abstract

The geographic knowledge graph offers a structured framework for mining and discovering spatiotemporal knowledge, which is of great significance for understanding geographic dynamics. However, existing geographic knowledge graphs still encounter significant challenges in comprehensive expression of spatiotemporal elements and understanding the intricate relationships [...] Read more.

The geographic knowledge graph offers a structured framework for mining and discovering spatiotemporal knowledge, which is of great significance for understanding geographic dynamics. However, existing geographic knowledge graphs still encounter significant challenges in comprehensive expression of spatiotemporal elements and understanding the intricate relationships and dynamic evolution among geographic entities, space, and time. Therefore, a Spatiotemporal Evolution Hierarchical Representation Graph (STEHRG) is proposed, which consists of three layers: a spatiotemporal ontology layer, a spatiotemporal evolution layer, and a feature situation layer. The STEHRG characterizes the multidimensional state transitions of spatiotemporal entities across various scales and abstraction levels, enabling a comprehensive representation of geographic spatiotemporal evolution. Additionally, this paper introduces a graph data structure-based approach for managing the state features of spatiotemporal entities and their lifecycle dependencies. Finally, through comparative experiments with existing knowledge graphs (GeoKG, GEKG, and STOKG), the results indicate that the STEHRG has significant advantages in accuracy, completeness, and reproducibility. Full article

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21 pages, 3449 KiB

Open AccessArticle

Rural Local Landscape Perception Evaluation: Integrating Street View Images and Machine Learning

by Suning Gong, Lin Zhang, Jie Zhang and Yuxi Duan

ISPRS Int. J. Geo-Inf. 2025, 14(7), 251; https://doi.org/10.3390/ijgi14070251 - 27 Jun 2025

Viewed by 302

Abstract

Rural landscape perception is of great significance in understanding the emotional connection between people and rural local environments. Seeking to rectify the problem of incomplete or biased results owing to the separation of objective and subjective landscape perception in previous studies, this study [...] Read more.

Rural landscape perception is of great significance in understanding the emotional connection between people and rural local environments. Seeking to rectify the problem of incomplete or biased results owing to the separation of objective and subjective landscape perception in previous studies, this study took the village of Chongming District in Shanghai, China, as an example and built an evaluation model that integrated four dimensions and 20 indicators of objective and subjective landscape perception, and used machine learning technology to analyze street view images. Subjective perception has been influenced by landscape color, style, and element perception. Notable spatial disparities have been observed in the distribution of rural landscape indicators across Chongming. This study refines key subjective and objective factors affecting rural landscape perception, and the model provides a new method for the perception evaluation of complex landscapes, providing a theoretical basis and practical reference for rural landscape planning. Full article

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

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19 pages, 1557 KiB

Open AccessArticle

SceneDiffusion: Scene Generation Model Embedded with Spatial Constraints

by Shanshan Yu, Jiaxin Zhu, Jiaqi Li, Xunchun Li, Kai Wang, Jian Tu and Danhuai Guo

ISPRS Int. J. Geo-Inf. 2025, 14(7), 250; https://doi.org/10.3390/ijgi14070250 - 27 Jun 2025

Viewed by 266

Abstract

Spatial scenes, as fundamental units of geospatial cognition, encompass rich objects and spatial relationships, and their generation techniques hold significant application value in disaster simulation and emergency drills, delayed spatial reconstruction and analysis, and other fields. However, existing studies still face limitations in [...] Read more.

Spatial scenes, as fundamental units of geospatial cognition, encompass rich objects and spatial relationships, and their generation techniques hold significant application value in disaster simulation and emergency drills, delayed spatial reconstruction and analysis, and other fields. However, existing studies still face limitations in modeling complex spatial relationships during scene generation, leading to insufficient semantic consistency and geographical accuracy. The advancement of Geospatial Artificial Intelligence (GeoAI) offers a new technical pathway for the intelligent modeling of spatial scenes. Against this backdrop, we propose SceneDiffusion, a scene generation model embedded with spatial constraints, and construct a geospatial scene dataset incorporating spatial relationship descriptions and geographic semantics, aiming to enhance the understanding and modeling capabilities of GeoAI models for spatial information. Specifically, SceneDiffusion employs a spatial scene representation framework to uniformly characterize objects and their topological, directional, and distance relationships, enhances the interactive modeling of objects and relationships through a Spatial relationship Attention-aware Graph (SAG) module, and finally generates high-quality scene images conforming to geographic semantics using a Layout information-guided Conditional Diffusion (LCD) module. Both qualitative and quantitative experiments demonstrate the superiority of SceneDiffusion, achieving a 56.6% reduction in FID and a 35.3% improvement in SSIM compared to baseline methods. Ablation studies confirm the importance of multi-relational modeling with attention mechanisms. By generating scenes that satisfy spatial distribution constraints, this work provides technical support for applications such as emergency scene simulation and virtual scene construction, while also offering insights for theoretical research and methodological innovation in GeoAI. Full article

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

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ISPRS Int. J. Geo-Inf., Volume 14, Issue 7 (July 2025) – 44 articles

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