ISPRS International Journal of Geo-Information

28 pages, 3425 KB

Open AccessArticle

Multimodal Spatiotemporal Deep Fusion for Highway Traffic Accident Prediction in Toronto: A Case Study and Roadmap

by Danya Qutaishat and Songnian Li

ISPRS Int. J. Geo-Inf. 2025, 14(11), 434; https://doi.org/10.3390/ijgi14110434 - 3 Nov 2025

A proactive traffic safety approach provides a forward-looking method for managing traffic and preventing accidents by identifying high-risk conditions before they occur. Previous studies have often focused on historical crash data or demographic factors, relying on limited single-source inputs and neglecting spatial, temporal, [...] Read more.

A proactive traffic safety approach provides a forward-looking method for managing traffic and preventing accidents by identifying high-risk conditions before they occur. Previous studies have often focused on historical crash data or demographic factors, relying on limited single-source inputs and neglecting spatial, temporal, and environmental interactions. This study develops a multimodal spatiotemporal deep fusion framework for predicting traffic accidents in Toronto, Canada, by integrating spatial, temporal, environmental, and lighting features within a proactive modeling structure. Three fusion approaches were investigated: (1) environmental feature fusion, (2) extended fusion incorporating lighting and road surface conditions, and (3) a double-stage fusion combining all feature types. The double-stage fusion achieved the best performance, reducing RMSE from 0.50 to 0.41 and outperforming conventional models across multiple error metrics. The framework supports fine-grained hotspot analysis, improves proactive traffic safety management, and provides a transferable roadmap for applying deep fusion in real-world intelligent transportation and urban planning systems. Full article

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28 pages, 5513 KB

Open AccessArticle

An Agent-Based System for Location Privacy Protection in Location-Based Services

by Omar F. Aloufi, Ahmed S. Alfakeeh and Fahad M. Alotaibi

ISPRS Int. J. Geo-Inf. 2025, 14(11), 433; https://doi.org/10.3390/ijgi14110433 - 3 Nov 2025

Abstract

Location-based services (LBSs) are a crucial element of the Internet of Things (IoT) and have garnered significant attention from both researchers and users, driven by the rise of wireless devices and a growing user base. However, the use of LBS-enabled applications carries several [...] Read more.

Location-based services (LBSs) are a crucial element of the Internet of Things (IoT) and have garnered significant attention from both researchers and users, driven by the rise of wireless devices and a growing user base. However, the use of LBS-enabled applications carries several risks, as users must provide their real locations with each query. This can expose them to potential attacks from the LBS server, leading to serious issues like the theft of personal information. Consequently, protecting location privacy is a vital concern. To address this, location dummy-based methods are employed to safeguard the location privacy of LBS users. However, location dummy-based approaches also suffer from problems such as low resistance against inference attacks and the generation of strong dummy locations, an issue that is considered an open problem. Moreover, generating many location dummies to achieve a high privacy protection level leads to high network overhead and requires high computational capabilities on the mobile devices of the LBS users, and such devices are limited. In this paper, we introduce the Caching-Aware Double-Dummy Selection (CaDDSL) algorithm to protect the location privacy of LBS users against homogeneity location and semantic location inference attacks, which may be applied by the LBS server as a malicious party. Then, we enhance the CaDDSL algorithm via encapsulation with agents to solve the tradeoff between generating many dummies and large network overhead by proposing the Cache-Aware Overhead-Aware Dummy Selection (CaOaDSL) algorithm. Compared to three well-known approaches, namely GridDummy, CirDummy, and Dest-Ex, our approach showed better performance in terms of communication cost, cache hit ratio, resistance against inference attacks, and network overhead. Full article

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17 pages, 11914 KB

Open AccessArticle

Evaluation of Metro Station Accessibility Based on Combined Weights and GRA-TOPSIS Method

by Tao Wu, Yichong Shi, Ye Zhou and Zhihan Chen

ISPRS Int. J. Geo-Inf. 2025, 14(11), 432; https://doi.org/10.3390/ijgi14110432 - 3 Nov 2025

Abstract

Assessing the accessibility of urban metro stations is essential for optimizing metro system planning and improving travel efficiency for residents. This study proposes an innovative evaluation framework—the CWM-GRA-TOPSIS model—for comprehensive metro station accessibility assessment. First, a multi-dimensional indicator system is established, encompassing three [...] Read more.

Assessing the accessibility of urban metro stations is essential for optimizing metro system planning and improving travel efficiency for residents. This study proposes an innovative evaluation framework—the CWM-GRA-TOPSIS model—for comprehensive metro station accessibility assessment. First, a multi-dimensional indicator system is established, encompassing three key dimensions, to-metro accessibility, by-metro accessibility, and land use accessibility, which are further refined into 14 secondary indicators for detailed analysis. A Combination Weighting Method (CWM) is then introduced, integrating the Analytic Hierarchy Process (AHP) for subjective weighting and the Criteria Importance Through Intercriteria Correlation (CRITIC) method for objective weighting, with their integration optimized through Game Theory. Subsequently, the overall accessibility of metro stations is evaluated and ranked using a hybrid Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The proposed method is applied to Wuhan, China, to demonstrate its effectiveness and applicability. Results show that the CWM-GRA-TOPSIS model, by balancing objectivity and practical relevance, provides a more reliable and systematic approach for identifying accessibility disparities and formulating targeted improvement strategies for urban metro systems. Full article

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

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22 pages, 1208 KB

Open AccessArticle

Geo-MRC: Dynamic Boundary Inference in Machine Reading Comprehension for Nested Geographic Named Entity Recognition

by Yuting Zhang, Jingzhong Li, Pengpeng Li, Tao Liu, Ping Du and Xuan Hao

ISPRS Int. J. Geo-Inf. 2025, 14(11), 431; https://doi.org/10.3390/ijgi14110431 - 2 Nov 2025

Abstract

Geographic Named Entity Recognition (Geo-NER) is a crucial task for extracting geography-related entities from unstructured text, and it plays an essential role in geographic information extraction and spatial semantic understanding. Traditional approaches typically treat Geo-NER as a sequence labeling problem, where each token [...] Read more.

Geographic Named Entity Recognition (Geo-NER) is a crucial task for extracting geography-related entities from unstructured text, and it plays an essential role in geographic information extraction and spatial semantic understanding. Traditional approaches typically treat Geo-NER as a sequence labeling problem, where each token is assigned a single label. However, this formulation struggles to handle nested entities effectively. To overcome this limitation, we propose Geo-MRC, an improved model based on a Machine Reading Comprehension (MRC) framework that reformulates Geo-NER as a question-answering task. The model identifies entities by predicting their start positions, end positions, and lengths, enabling precise detection of overlapping and nested entities. Specifically, it constructs a unified input sequence by concatenating a type-specific question (e.g., “What are the location names in the text?”) with the context. This sequence is encoded using BERT, followed by feature extraction and fusion through Gated Recurrent Units (GRU) and multi-scale 1D convolutions, which improve the model’s sensitivity to both multi-level semantics and local contextual information. Finally, a feed-forward neural network (FFN) predicts whether each token corresponds to the start or end of an entity and estimates the span length, allowing for dynamic inference of entity boundaries. Experimental results on multiple public datasets demonstrate that Geo-MRC consistently outperforms strong baselines, with particularly significant gains on datasets containing nested entities. Full article

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22 pages, 11351 KB

Open AccessArticle

InSAR Reveals Coseismic Deformation and Coulomb Stress Changes of the 2025 Tingri Earthquake: Implications for Regional Hazard Assessment

by Anan Chen, Zhen Wu, Huiwen Zhang, Jianjian Wu, Zifei Ping and Jiayan Liao

ISPRS Int. J. Geo-Inf. 2025, 14(11), 430; https://doi.org/10.3390/ijgi14110430 - 1 Nov 2025

Abstract

Normal faults play a key role in accommodating extensional deformation within the South Tibet Rift. The M_S 6.8 Tingri earthquake of 7 January 2025 therefore provides a rare opportunity to investigate how these normal faults accommodate east–west extension driven by India–Eurasia convergence. [...] Read more.

Normal faults play a key role in accommodating extensional deformation within the South Tibet Rift. The M_S 6.8 Tingri earthquake of 7 January 2025 therefore provides a rare opportunity to investigate how these normal faults accommodate east–west extension driven by India–Eurasia convergence. Using Sentinel-1 synthetic aperture radar (SAR) imagery, we measured coseismic surface deformation and inverted the slip distribution, revealing a maximum line-of-sight (LOS) displacement of 1.85 m. Combining Bayesian inference with joint fault-slip inversion, we constrain the seismogenic fault as a west-dipping normal fault (strike 183°, dip 42.5°, rake ~–115°), exhibiting a maximum slip of 5.36 m at shallow depth. The derived moment magnitude (M_W 7.12, seismic moment 3.32 × 10¹⁹ N·m) agrees well with the USGS estimate (M_W 7.1). Coulomb stress modeling suggests stress decreases along fault flanks and significant stress loading (>0.01 MPa) at rupture terminations and adjacent north–south trending faults, implying elevated aftershock potential and possible fault triggering. GNSS velocity fields and strain rate inversion indicate a regional stress regime with a principal compressive axis (σ₁) oriented ~341° (NNW) and extensional axis (σ₃) at ~73° (ESE), consistent with east–west extension and north–south shortening. The fault exhibits oblique-normal slip, attributed to the non-orthogonal orientation of the fault plane relative to the stress field, resulting in right-lateral shear. Within the framework of the paired general-shear (PGS) deformation, this oblique slip reflects localized extensional deformation within a distributed dextral shear zone. These findings support a model of strain partitioning under regional shear and provide insights into fault segmentation and kinematics in rift systems. 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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40 pages, 11595 KB

Open AccessArticle

An Automated Workflow for Generating 3D Solids from Indoor Point Clouds in a Cadastral Context

by Zihan Chen, Frédéric Hubert, Christian Larouche, Jacynthe Pouliot and Philippe Girard

ISPRS Int. J. Geo-Inf. 2025, 14(11), 429; https://doi.org/10.3390/ijgi14110429 - 31 Oct 2025

Abstract

Accurate volumetric modeling of indoor spaces is essential for emerging 3D cadastral systems, yet existing workflows often rely on manual intervention or produce surface-only models, limiting precision and scalability. This study proposes and validates an integrated, largely automated workflow (named VERTICAL) that converts [...] Read more.

Accurate volumetric modeling of indoor spaces is essential for emerging 3D cadastral systems, yet existing workflows often rely on manual intervention or produce surface-only models, limiting precision and scalability. This study proposes and validates an integrated, largely automated workflow (named VERTICAL) that converts classified indoor point clouds into topologically consistent 3D solids served as materials for land surveyor’s cadastral analysis. The approach sequentially combines RANSAC-based plane detection, polygonal mesh reconstruction, mesh optimization stage that merges coplanar faces, repairs non-manifold edges, and regularizes boundaries and planar faces prior to CAD-based solid generation, ensuring closed and geometrically valid solids. These modules are linked through a modular prototype (called P2M) with a web-based interface and parameterized batch processing. The workflow was tested on two condominium datasets representing a range of spatial complexities, from simple orthogonal rooms to irregular interiors with multiple ceiling levels, sloped roofs, and internal columns. Qualitative evaluation ensured visual plausibility, while quantitative assessment against survey-grade reference models measured geometric fidelity. Across eight representative rooms, models meeting qualitative criteria achieved accuracies exceeding 97% for key metrics including surface area, volume, and ceiling geometry, with a height RMSE around 0.01 m. Compared with existing automated modeling solutions, the proposed workflow has the ability of dealing with complex geometries and has comparable accuracy results. These results demonstrate the workflow’s capability to produce topologically consistent solids with high geometric accuracy, supporting both boundary delineation and volume calculation. The modular, interoperable design enables integration with CAD environments, offering a practical pathway toward an automated and reliable core of 3D modeling for cadastre applications. Full article

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22 pages, 6748 KB

Open AccessArticle

Automated 3D Reconstruction of Interior Structures from Unstructured Point Clouds

by Youssef Hany, Wael Ahmed, Adel Elshazly, Ahmad M. Senousi and Walid Darwish

ISPRS Int. J. Geo-Inf. 2025, 14(11), 428; https://doi.org/10.3390/ijgi14110428 - 31 Oct 2025

Abstract

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D [...] Read more.

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D models and 2D floor plans from unstructured indoor point clouds. The approach begins with point cloud preprocessing using voxel-based downsampling and robust statistical outlier removal. Room partitions are extracted via DBSCAN applied in the 2D space, followed by structural segmentation using the RandLA-Net deep learning model to classify key building components such as walls, floors, ceilings, columns, doors, and windows. To enhance segmentation fidelity, a density-based filtering technique is employed, and RANSAC is utilized to detect and fit planar primitives representing major surfaces. Wall-surface openings such as doors and windows are identified through local histogram analysis and interpolation in wall-aligned coordinate systems. The method supports complex indoor environments including Manhattan and non-Manhattan layouts, variable ceiling heights, and cluttered scenes with occlusions. The approach was validated using six datasets with varying architectural characteristics, and evaluated using completeness, correctness, and accuracy metrics. Results show a minimum completeness of 86.6%, correctness of 84.8%, and a maximum geometric error of 9.6 cm, demonstrating the robustness and generalizability of the proposed pipeline for automated as-built BIM reconstruction. Full article

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28 pages, 6006 KB

Open AccessArticle

Accelerating Computation for Estimating Land Surface Temperature: An Efficient Global–Local Regression (EGLR) Framework

by Jiaxin Liu, Qing Luo and Huayi Wu

ISPRS Int. J. Geo-Inf. 2025, 14(11), 427; https://doi.org/10.3390/ijgi14110427 - 31 Oct 2025

Abstract

Rapid urbanization elevates land surface temperature (LST) through complex urban spatial relationships, intensifying the urban heat island (UHI) effect. This necessitates efficient methods to analyze surface urban heat island (SUHI) factors to help develop mitigation strategies. In this study, we propose an efficient [...] Read more.

Rapid urbanization elevates land surface temperature (LST) through complex urban spatial relationships, intensifying the urban heat island (UHI) effect. This necessitates efficient methods to analyze surface urban heat island (SUHI) factors to help develop mitigation strategies. In this study, we propose an efficient global–local regression (EGLR) framework by integrating XGBoost-SHAP with global–local regression (GLR), enabling accelerated estimation of LST. In a case study of Wuhan, the EGLR reduces the computation time of GLR by 44.21%. The main contribution of computational efficiency improvement lies in the procedure of Moran eigenvector selecting executed by XGBoost-SHAP. Results of validation experiments also show significant time decrease of the EGLR for a larger sample size; in addition, transplanting the framework of the EGLR to two machine learning models not only reduces the executing time, but also increases model fitting. Furthermore, the inherent merits of XGBoost-SHAP and GLR also enables the EGLR to simultaneously capture nonlinear causal relationships and decompose spatial effects. Results identify population density as the most sensitive LST-increasing factor. Impervious surface percentage, building height, elevation, and distance to the nearest water body are positively correlated with LST, while water area, normalized difference vegetation index, and the number of bus stops have significant negative relationships with LST. In contrast, the impact of the number of points of interest, gross domestic product, and road length on LST is not significant overall. Full article

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27 pages, 24393 KB

Open AccessArticle

FireRisk-Multi: A Dynamic Multimodal Fusion Framework for High-Precision Wildfire Risk Assessment

by Ke Yuan, Zhiruo Zhu, Yutong Pang, Jing Pang, Chunhui Hou and Qian Tang

ISPRS Int. J. Geo-Inf. 2025, 14(11), 426; https://doi.org/10.3390/ijgi14110426 - 31 Oct 2025

Abstract

Wildfire risk assessment requires integrating heterogeneous geospatial data to capture complex environmental dynamics. This study develops a hierarchical multimodal fusion framework combining high-resolution aerial imagery, historical fire data, topography, meteorology, and vegetation indices within Google Earth Engine. We introduce three progressive fusion levels: [...] Read more.

Wildfire risk assessment requires integrating heterogeneous geospatial data to capture complex environmental dynamics. This study develops a hierarchical multimodal fusion framework combining high-resolution aerial imagery, historical fire data, topography, meteorology, and vegetation indices within Google Earth Engine. We introduce three progressive fusion levels: a single-modality baseline (NAIP-WHP), fixed-weight fusion (FIXED), and a novel geographically adaptive dynamic-weight approach (FUSED) that adjusts feature contributions based on regional characteristics like human activity intensity or aridity. Machine learning benchmarking across 49 U.S. regions reveals that Support Vector Machines (SVM) applied to the FUSED dataset achieve optimal performance, with an AUC-ROC of 92.1%, accuracy of 83.3%, and inference speed of 1.238 milliseconds per sample. This significantly outperforms the fixed-weight fusion approach, which achieved an AUC-ROC of 78.2%, and the single-modality baseline, which achieved 73.8%, representing relative improvements of 17.8% and 24.8%, respectively. The 10 m resolution risk heatmaps demonstrate operational viability, achieving an 86.27% hit rate in Carlsbad Caverns, NM. SHAP-based interpretability analysis reveals terrain dominance and context-dependent vegetation effects, aligning with wildfire ecology principles. Full article

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19 pages, 1895 KB

Open AccessArticle

Cross-Context Aggregation for Multi-View Urban Scene and Building Facade Matching

by Yaping Yan and Yuhang Zhou

ISPRS Int. J. Geo-Inf. 2025, 14(11), 425; https://doi.org/10.3390/ijgi14110425 - 31 Oct 2025

Abstract

Accurate and robust feature matching across multi-view urban imagery is fundamental for urban mapping, 3D reconstruction, and large-scale spatial alignment. Real-world urban scenes involve significant variations in viewpoint, illumination, and occlusion, as well as repetitive architectural patterns that make correspondence estimation challenging. To [...] Read more.

Accurate and robust feature matching across multi-view urban imagery is fundamental for urban mapping, 3D reconstruction, and large-scale spatial alignment. Real-world urban scenes involve significant variations in viewpoint, illumination, and occlusion, as well as repetitive architectural patterns that make correspondence estimation challenging. To address these issues, we propose the Cross-Context Aggregation Matcher (CCAM), a detector-free framework that jointly leverages multi-scale local features, long-range contextual information, and geometric priors to produce spatially consistent matches. Specifically, CCAM integrates a multi-scale local enhancement branch with a parallel self- and cross-attention Transformer, enabling the model to preserve detailed local structures while maintaining a coherent global context. In addition, an independent positional encoding scheme is introduced to strengthen geometric reasoning in repetitive or low-texture regions. Extensive experiments demonstrate that CCAM outperforms state-of-the-art methods, achieving up to +31.8%, +19.1%, and +11.5% improvements in AUC@{5°, 10°, 20°} over detector-based approaches and up to 1.72% higher precision compared with detector-free counterparts. These results confirm that CCAM delivers reliable and spatially coherent matches, thereby facilitating downstream geospatial applications. Full article

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24 pages, 1994 KB

Open AccessArticle

Twitter User Geolocation Based on Multi-Graph Feature Fusion with Gating Mechanism

by Qiongya Wei, Yaqiong Qiao, Shuaihui Zhu, Aobo Jiao and Qingqing Dong

ISPRS Int. J. Geo-Inf. 2025, 14(11), 424; https://doi.org/10.3390/ijgi14110424 - 31 Oct 2025

Abstract

Geolocating Twitter users from social media data holds significant value in applications such as targeted advertising, disaster response, and social network analysis. However, existing social network-based geolocation methods tend to focus primarily on mention relations while neglecting other critical interactions like retweet relationships. [...] Read more.

Geolocating Twitter users from social media data holds significant value in applications such as targeted advertising, disaster response, and social network analysis. However, existing social network-based geolocation methods tend to focus primarily on mention relations while neglecting other critical interactions like retweet relationships. Moreover, effectively integrating diverse social features remains a key challenge, which limits the overall performance of geolocation models. To address these issues, this paper proposes a novel Twitter user geolocation method based on multi-graph feature fusion with a gating mechanism, termed MGFGCN, which fully leverages heterogeneous social network information. Specifically, MGFGCN first constructs separate mention and retweet graphs to capture multi-dimensional user relationships. It then incorporates the Information Gain Ratio (IGR) to select discriminative keywords and generates Term Frequency–Inverse Document Frequency (TF-IDF) features, thereby enhancing the semantic representation of user nodes. Furthermore, to exploit complementary information across different graph structures, we propose a Structure-aware Gated Fusion Mechanism (SGFM) that dynamically captures differences and interactions between nodes from each graph, enabling the effective fusion of node representations into a unified representation for subsequent location inference. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art baselines in the Twitter user geolocation task across two public datasets. Full article

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18 pages, 13712 KB

Open AccessArticle

Integrating Multiple Semantics of Street View Imagery for Semi-Supervised Building Function Identification

by Fang Fang, Nan Min, Shengwen Li, Yuxiang Zhao, Sishi Gong, Yu Wang and Shunping Zhou

ISPRS Int. J. Geo-Inf. 2025, 14(11), 423; https://doi.org/10.3390/ijgi14110423 - 29 Oct 2025

Abstract

Building function identification plays a crucial role in providing basic data for urban planning, management, and various intelligent applications. Today, building function identification methods using Street View Images (SVIs) have made significant progress. However, these methods use the visual features of SVIs to [...] Read more.

Building function identification plays a crucial role in providing basic data for urban planning, management, and various intelligent applications. Today, building function identification methods using Street View Images (SVIs) have made significant progress. However, these methods use the visual features of SVIs to infer building functions, which ignores the contributions of the multiple potential semantics of SVIs, resulting in suboptimal identification accuracy. To address this issue, this study proposes a multi-semantic semi-supervised building function identification (MS-SS-BFI) method, which integrates multi-level visual semantics and spatial contextual semantics to improve building function identification from SVIs. Specifically, a location mapping module was designed to align SVIs with buildings. Additionally, a multi-level visual semantic extraction module was developed to integrate the visual semantics and visual-textual semantics of SVIs. In addition, a semi-supervised spatial interaction module was designed to characterize the spatial context of buildings. Extensive experiments on the Brooklyn dataset show that the proposed method achieves 7.98% improvement in F1-score over the state-of-the-art baseline, demonstrating superior performance and robustness. This work explores a novel approach to building function identification and provides a methodological reference for various SVI-based applications. Full article

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20 pages, 2831 KB

Open AccessArticle

Capturing the Footsteps of Mobility: A Machine Learning-Based Study on the Relationship Between Streetscape and Consumption Vitality

by Yiming Hou, Xiaoqing Zhang and Jia Jia

ISPRS Int. J. Geo-Inf. 2025, 14(11), 422; https://doi.org/10.3390/ijgi14110422 - 29 Oct 2025

Abstract

Urban streets serve as essential spaces for commercial activities and social interaction, yet the mechanisms through which their landscape elements influence consumption vitality remain insufficiently explored. Focusing on Lixia District, Jinan, China, this study integrates street-view image semantic segmentation with machine learning techniques [...] Read more.

Urban streets serve as essential spaces for commercial activities and social interaction, yet the mechanisms through which their landscape elements influence consumption vitality remain insufficiently explored. Focusing on Lixia District, Jinan, China, this study integrates street-view image semantic segmentation with machine learning techniques to capture the nonlinear interactions between streetscape features and consumption vitality, thereby establishing an analytical framework for examining their associations. The results show that: (1) pedestrian-friendly facilities are significantly associated with higher street-level consumption vitality, with benches and streetlights showing marked effects once their visual proportions exceed 10% and 12%, respectively; (2) the visual proportion of parking space becomes positively associated with consumption vitality when exceeding 0.15, whereas carriageway proportion shows an overall negative association; (3) the marginal effect of advertising density gradually diminishes, with billboard visibility ratios above 25% exhibiting saturated impacts; (4) when green-view visibility exceeds 30%, consumption vitality increases substantially, peaking within the 35–40% range; (5) potential synergies or trade-offs exist among streetscape elements—compared with individual factors, the combinations of benches and parking spaces, benches and billboards, as well as parking spaces and billboards, are associated with higher street-level consumption vitality. In contrast, combinations involving a larger sky view ratio are often linked to lower consumption vitality, suggesting that overly open spaces may weaken consumer attractiveness. This study not only extends the methodological toolkit for analyzing consumption vitality but also provides theoretical and practical guidance for the refined design and experiential construction of urban street environments. Full article

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22 pages, 17354 KB

Open AccessArticle

Remote Sensing-Based Spatiotemporal Assessment of Heat Risk in the Guangdong–Hong Kong–Macao Greater Bay Area

by Zhoutong Yuan, Guotao Cui and Zhiqiang Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(11), 421; https://doi.org/10.3390/ijgi14110421 - 29 Oct 2025

Abstract

Under the dual pressures of climate change and rapid urbanization, extreme heat events pose growing risks to densely populated megaregions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a densely populated and economically vital region, serves as a critical hotspot for heat risk aggregation. [...] Read more.

Under the dual pressures of climate change and rapid urbanization, extreme heat events pose growing risks to densely populated megaregions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a densely populated and economically vital region, serves as a critical hotspot for heat risk aggregation. This study develops a high-resolution multi-dimensional framework to assess the spatiotemporal evolution of its heat risk profile from 2000 to 2020. A Heat Risk Index (HRI) integrating heat hazard and vulnerability components to measure potential heat-related impacts is calculated as the product of the Heat Hazard Index (HHI) and Heat Vulnerability Index (HVI) for 1 km grids in GBA. The HHI integrates the frequency of hot days and hot nights. HVI incorporates population density, GDP, remote-sensing nighttime light data, and MODIS-based landscape indicators (e.g., NDVI, NDWI, and NDBI), with weights determined objectively using the static Entropy Weight Method to ensure spatiotemporal comparability. The findings reveal an escalation of heat risk, expanding at an average rate of 342 km² per year (p = 0.008), with the proportion of areas classified as high-risk or above increasing from 21.8% in 2000 to 33.3% in 2020. This trend was characterized by (a) a pronounced asymmetric warming pattern, with nighttime temperatures rising more rapidly than daytime temperatures; (b) high vulnerability dominated by the concentration of population and economic assets, as indicated by high EWM-based weights; and (c) isolated high-risk hotspots (Guangzhou and Hong Kong) in 2000, which have expanded into a high-risk belt across the Pearl River Delta’s industrial heartland, like Foshan seeing their high-risk area expand from 3.4% to 27.0%. By combining remote sensing and socioeconomic data, this study provides a transferable framework that moves beyond coarse-scale assessments to identify specific intra-regional risk hotspots. The resulting high-resolution risk maps offer a quantitative foundation for developing spatially explicit climate adaptation strategies in the GBA and other rapidly urbanizing megaregions. 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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23 pages, 6892 KB

Open AccessArticle

Built-Up Surface Ensemble Model for Romania Based on OpenStreetMap, Microsoft Building Footprints, and Global Human Settlement Layer Data Sources Using Triple Collocation Analysis

by Zsolt Magyari-Sáska and Ionel Haidu

ISPRS Int. J. Geo-Inf. 2025, 14(11), 420; https://doi.org/10.3390/ijgi14110420 - 28 Oct 2025

Abstract

Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and [...] Read more.

Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and Global Human Settlement Layer Built-up surface (GHS)—onto a 10 m resolution raster grid and applied this consistently at the national scale across 3181 settlement polygons to produce a more accurate, unified ensemble model for Romania. The methodological basis was Triple Collocation Analysis (TCA), extended with ETC/CTC to estimate per-settlement scale factors, enabling the quantification and optimal weighting of the relative errors and accuracy in the absence of independent reference data. Weight patterns vary by settlement type: OSM receives relatively higher weights in smaller rural settlements with less redundant error; in municipalities the stronger OSM–MSBF correlation reduces both of their weights and increases the GHS share; cities exhibit a more balanced weighting. At cell level, the ensemble provides uncertainty quantification via confidence intervals that typically range from 2% to 14% at settlement scale. The resulting model—like any model—does not perfectly reflect reality; however, the ensemble improves the accuracy and timeliness of the available data. The resulting model is replicable and updatable with newer data, making it suitable for numerous practical applications, especially in spatial development and risk analysis. Full article

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22 pages, 3673 KB

Open AccessArticle

Massively Parallel Lagrangian Relaxation Algorithm for Solving Large-Scale Spatial Optimization Problems Using GPGPU

by Ting L. Lei, Rongrong Wang and Zhen Lei

ISPRS Int. J. Geo-Inf. 2025, 14(11), 419; https://doi.org/10.3390/ijgi14110419 - 26 Oct 2025

Abstract

Lagrangian Relaxation (LR) is an effective method for solving spatial optimization problems in geospatial analysis and GIS. Among others, it has been used to solve the classic p-median problem that served as a unified local model in GIS since the 1990s. Despite [...] Read more.

Lagrangian Relaxation (LR) is an effective method for solving spatial optimization problems in geospatial analysis and GIS. Among others, it has been used to solve the classic p-median problem that served as a unified local model in GIS since the 1990s. Despite its efficiency, the LR algorithm has seen limited usage in practice and is not as widely used as off-the-shelf solvers such as OPL/CPLEX or GPLK. This is primarily because of the high cost of development, which includes (i) the cost of developing a full gradient descent algorithm for each optimization model with various tricks and modifications to improve the speed, (ii) the computational cost can be high for large problem instances, (iii) the need to test and choose from different relaxation schemes, and (iv) the need to derive and compute the gradients in a programming language. In this study, we aim to solve the first three issues by utilizing the computational power of GPGPU and existing facilities of modern deep learning (DL) frameworks such as PyTorch. Based on an analysis of the commonalities and differences between DL and general optimization, we adapt DL libraries for solving LR problems. As a result, we can choose from the many gradient descent strategies (known as “optimizers”) in DL libraries rather than reinventing them from scratch. Experiments show that implementing LR in DL libraries is not only feasible but also convenient. Gradient vectors are automatically tracked and computed. Furthermore, the computational power of GPGPU is automatically used to parallelize the optimization algorithm (a long-term difficulty in operations research). Experiments with the classic p-median problem show that we can solve much larger problem instances (of more than 15,000 nodes) optimally or nearly optimally using the GPU-based LR algorithm. Such capabilities allow for a more fine-grained analysis in GIS. Comparisons with the OPL solver and CPU version of the algorithm show that the GPU version achieves speedups of 104 and 12.5, respectively. The GPU utilization rate on an RTX 4090 GPU reaches 90%. We then conclude with a summary of the findings and remarks regarding future work. Full article

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19 pages, 2598 KB

Open AccessArticle

DOCB: A Dynamic Online Cross-Batch Hard Exemplar Recall for Cross-View Geo-Localization

by Wenchao Fan, Xuetao Tian, Long Huang, Xiuwei Zhang and Fang Wang

ISPRS Int. J. Geo-Inf. 2025, 14(11), 418; https://doi.org/10.3390/ijgi14110418 - 26 Oct 2025

Abstract

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference [...] Read more.

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference database. The challenge comes from the perspective inconsistency between matched objects. In this work, we propose a novel metric learning scheme for hard exemplar mining to improve the performance of cross-view geo-localization. Specifically, we introduce a Dynamic Online Cross-Batch (DOCB) hard exemplar mining scheme that solves the problem of the lack of hard exemplars in mini-batches in the middle and late stages of training, which leads to training stagnation. It mines cross-batch hard negative exemplars according to the current network state and reloads them into the network to make the gradient of negative exemplars participating in back-propagation. Since the feature representation of cross-batch negative examples adapts to the current network state, the triplet loss calculation becomes more accurate. Compared with methods only considering the gradient of anchors and positives, adding the gradient of negative exemplars helps us to obtain the correct gradient direction. Therefore, our DOCB scheme can better guide the network to learn valuable metric information. Moreover, we design a simple Siamese-like network called multi-scale feature aggregation (MSFA), which can generate multi-scale feature aggregation by learning and fusing multiple local spatial embeddings. The experimental results demonstrate that our DOCB scheme and MSFA network achieve an accuracy of 95.78% on the CVUSA dataset and 86.34% on the CVACT_val dataset, which outperforms those of other existing methods in the field. Full article

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

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31 pages, 8186 KB

Open AccessArticle

The Threshold Effect in the Street Vitality Formation Mechanism

by Yilin Ke, Jiawen Wang, Shiping Lin, Jilong Li, Niuniu Kong, Jie Zeng, Jiacheng Chen and Ke Ai

ISPRS Int. J. Geo-Inf. 2025, 14(11), 417; https://doi.org/10.3390/ijgi14110417 - 24 Oct 2025

Abstract

Street vitality has become a crucial metric for smart city management. Classical theories qualitatively explain that street vitality originates from the dynamic interaction between people and spatial carriers, yet the threshold effect within this process has not been addressed, leaving a gap in [...] Read more.

Street vitality has become a crucial metric for smart city management. Classical theories qualitatively explain that street vitality originates from the dynamic interaction between people and spatial carriers, yet the threshold effect within this process has not been addressed, leaving a gap in urban research. This study selects South China, one of China’s most vibrant and globally influential regions, introduces dissipative structure theory based on classical theories, and constructs a threshold effect hypothesis model for the vitality formation mechanism. Through energy efficiency conversion of data and a slope-based method for identifying balanced time periods, the periods of supply–demand balance in energy efficiency were identified, the threshold effect in vitality formation was captured, and critical thresholds were measured. The results indicate the following: (1) the hypothesis model is valid; (2) the threshold effect is inevitable and periodic, primarily occurring on workdays from 12:00 to 13:00 and 18:00 to 19:00, and on rest days from 08:00 to 09:00 and 18:00 to 19:00; and (3) the activation threshold is quantifiable and exhibits volatility, ranging from 0.40 to 1.56, varying specifically by city, season, day type, and street type. This study advances the translation of street vitality research from theory into practice and provides theoretical support and strategic guidance for smart city management globally, particularly in developing countries. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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26 pages, 2890 KB

Open AccessReview

A Review of Google Earth Engine for Land Use and Land Cover Change Analysis: Trends, Applications, and Challenges

by Bader Alshehri, Zhenyu Zhang and Xiaoye Liu

ISPRS Int. J. Geo-Inf. 2025, 14(11), 416; https://doi.org/10.3390/ijgi14110416 - 24 Oct 2025

Abstract

Google Earth Engine (GEE) has become one of the most widely used platforms for Land Use and Land Cover (LULC) research, offering cloud-based access to petabyte-scale datasets and scalable analytical tools. While earlier reviews provided valuable overviews of data and applications, this study [...] Read more.

Google Earth Engine (GEE) has become one of the most widely used platforms for Land Use and Land Cover (LULC) research, offering cloud-based access to petabyte-scale datasets and scalable analytical tools. While earlier reviews provided valuable overviews of data and applications, this study synthesizes 72 selected articles published between 2016 and February 2025 to examine the evolution of GEE–LULC research. Results show exponential growth in publications, with Landsat and Sentinel imagery dominating datasets and Random Forest (RF) and Support Vector Machine (SVM) remaining the most common classifiers. Geographically, output is concentrated in China and India, reflecting regional leadership in GEE adoption. Despite its strengths, GEE faces persistent challenges, including memory limits, restricted support for advanced Deep Learning (DL), and reliance on labeled data. Promising directions include integrating few-shot semantic segmentation and hybrid workflows combining GEE scalability with local Graphics Processing Unit (GPU) computing. By bridging platform-focused and application-focused studies, this review provides a comprehensive synthesis of GEE–LULC research and outlines actionable pathways for advancing scalable and Artificial Intelligence (AI)-enabled geospatial analysis. Full article

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Journal Description

ISPRS International Journal of Geo-Information

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