ISPRS International Journal of Geo-Information

34 pages, 61375 KB

Open AccessArticle

Fine-Scale Assessment of Inequalities in Emergency Shelter Accessibility Using the 3SFCA Method: A Focus on Distributional Balance and Lower-Tail Welfare

by Hanyu Xu, Chaoyong Zhang, Yuhang Xiong, Nan Jiang and Huige Xing

ISPRS Int. J. Geo-Inf. 2026, 15(9), 422; https://doi.org/10.3390/ijgi15090422 - 16 Sep 2026

Abstract

With climate change and urbanization accelerating, cities face increasing disaster risks. As critical facilities for safeguarding residents’ safety, emergency shelters have attracted growing attention due to spatial supply–demand mismatches. From the perspective of the 15-min community life circle, this study aims to develop [...] Read more.

With climate change and urbanization accelerating, cities face increasing disaster risks. As critical facilities for safeguarding residents’ safety, emergency shelters have attracted growing attention due to spatial supply–demand mismatches. From the perspective of the 15-min community life circle, this study aims to develop a fine-scale analytical framework for assessing emergency shelter accessibility and inequality, using Pidu District, Chengdu, China, as a case study. The three-step floating catchment area (3SFCA) method is applied to assess emergency shelter accessibility, while the Gini coefficient and Kolm–Pollak equally distributed equivalent (EDE) are introduced to evaluate accessibility inequality across subdistricts and communities. The results show that: (1) compared with the traditional Gaussian two-step floating catchment area (G2SFCA) method, 3SFCA captures clearer accessibility differentiation and stronger local sensitivity; (2) emergency shelter accessibility shows marked spatial differentiation, with pronounced local clustering in the walking mode and a polycentric pattern in the cycling mode; (3) the Gini coefficient reveals distributional imbalance in emergency shelter accessibility, showing a clear “center–periphery” pattern in which urban areas generally exhibit lower inequality than suburban and rural areas; and (4) the Kolm–Pollak EDE emphasizes lower-tail welfare and helps distinguish “low but even” areas from “high but polarized” areas. These findings support equity-oriented strategies for emergency shelter planning and resource allocation. The study provides methodological and practical implications for fine-scale emergency shelter planning and resilient urban governance, contributing to more equitable, inclusive, resilient, and sustainable urban environments. Full article

(This article belongs to the Topic Innovative Approaches in Geospatial Analysis and Modeling of Urban Environments)

► Show Figures

Figure 1

21 pages, 8784 KB

Open AccessArticle

Large Language Model-Derived Spatial Embedding for Revealing Electric Vehicle Energy Consumption: An Urban–Rural Case

by Can Yin, Lifu Jin and Zhe Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 421; https://doi.org/10.3390/ijgi15090421 - 15 Sep 2026

Abstract

Understanding spatial variation in electric vehicle energy consumption (EC) can support more targeted urban transport analysis, but observational data do not identify the causal effects of urban design. This study analyzes approximately 1700 private EVs operating in Shanghai from October to December 2020, [...] Read more.

Understanding spatial variation in electric vehicle energy consumption (EC) can support more targeted urban transport analysis, but observational data do not identify the causal effects of urban design. This study analyzes approximately 1700 private EVs operating in Shanghai from October to December 2020, integrating about 29 million high-resolution GPS records with multi-source built-environment data. Segment-level EC was aggregated to 500 m grids and communities. Community functional zones were derived by encoding POI sequences with Qwen2.5-1.5B-Instruct and clustering the resulting embeddings, while Random Forest models and SHAP were used to characterize model-attributed associations with spatial EC variation. At the grid level, mean speed and speed variability received approximately 84% of the native Random Forest importance, whereas the 5D-inspired indicators received the remaining 16%, led by destination accessibility, road density, and bus-stop density. At the community level, the Urban core zone had approximately 9% higher fleet-weighted EC than the Rural zone, while within-zone importance rankings differed between dense and sparse zones. These results describe conditional, location-level associations for the sampled fleet and season. Full article

► Show Figures

Figure 1

30 pages, 43250 KB

Open AccessArticle

Spatial Mapping and Attribution of Wheelchair Users’ Accessibility Decay Under an “Ideal-Realistic” Scenario Comparison

by Qian Li, Ying Sun, Tianqi Guo and Xiangfeng Li

ISPRS Int. J. Geo-Inf. 2026, 15(9), 420; https://doi.org/10.3390/ijgi15090420 - 14 Sep 2026

Abstract

Within the “15-min community life circle”, spatial accessibility of basic services is critical to ensuring social equity for people with disabilities. However, existing wheelchair accessibility assessments predominantly focus on static “origin-destination” node configurations, treating the road network as homogeneous and failing to capture [...] Read more.

Within the “15-min community life circle”, spatial accessibility of basic services is critical to ensuring social equity for people with disabilities. However, existing wheelchair accessibility assessments predominantly focus on static “origin-destination” node configurations, treating the road network as homogeneous and failing to capture how micro-level physical barriers induce macro-level accessibility decay through network connectivity transmission. This study addresses this gap by introducing an “ideal-realistic” scenario comparison framework combined with weighted edge betweenness centrality. This approach backward-traces resource accessibility loss to two dimensions of micro-level road-segment failure, namely topological function loss and service resource loss, shifting the analytical paradigm from resource configuration assessment to network connectivity diagnosis. Taking the central Xinhai Subdistrict of Lianyungang, China, as a case study, this paper constructs a dual-scenario network based on field-surveyed barrier data and establishes a two-dimensional measurement system centered on segment-level function loss and resource loss. The findings reveal that realistic physical barriers cause significant deprivation in wheelchair users’ access to life-circle services. Moreover, this decay is not homogeneously distributed but is highly contingent on the topological role of road segments, exhibiting observed spatial patterns such as skeletal network collapse, critical entrance blockages, and isolated terminal resource clusters. The analysis elucidates how micro-level physical barriers are propagated through spatial connectivity, offering a new perspective for understanding impedance dynamics in existing urban slow-traffic environments. The results can inform precision interventions in pre-standard neighborhood renewal: critical bottleneck segments provide spatial targets for prioritizing resource allocation and context-specific strategies across communities. Full article

(This article belongs to the Topic Innovative Approaches in Geospatial Analysis and Modeling of Urban Environments)

► Show Figures

Figure 1

29 pages, 15952 KB

Open AccessArticle

Learning Deformation-Induced Shape Representations for Building Footprints via Self-Supervision

by Shuqi Cao and Guohua Ji

ISPRS Int. J. Geo-Inf. 2026, 15(9), 419; https://doi.org/10.3390/ijgi15090419 - 14 Sep 2026

Abstract

Effective shape representation of building footprints is essential for many geospatial analysis and applications, such as building retrieval, cartographic generalization, and urban morphology analysis. Constrained by scarce and coarse annotations of building shapes, self-supervised learning (SSL) is regarded as a promising paradigm. However, [...] Read more.

Effective shape representation of building footprints is essential for many geospatial analysis and applications, such as building retrieval, cartographic generalization, and urban morphology analysis. Constrained by scarce and coarse annotations of building shapes, self-supervised learning (SSL) is regarded as a promising paradigm. However, existing SSL approaches primarily infer invariance from augmented views of the same instance without explicitly defined similarity, limiting their ability to capture multi-layered relationships—from geometric regularity to structural layout—across building shapes. Providing reliable and interpretable similarity supervision remains challenging. In this work, we propose Deformation-Induced Self-Supervised Learning (DI-SSL), a framework that explicitly defines similarity through Geometric-Aware Deformations (GAD). GAD constrains deformations along global topology, geometric variation and structural layout, jointly characterizing structural comparability between shapes and ensuring geometrically valid, structurally coherent variants under controlled form deviations. Consequently, similarity is explicitly defined through these constrained deformation processes, yielding an embedding space where structurally similar shapes are consistently organized. Experiments demonstrate that DI-SSL achieves strong performance in retrieval and few-shot generalization. The learned embedding space is well-structured, exhibiting global separability of structural prototypes and local continuity consistent with morphologically meaningful similarities. Ablation studies further highlight the critical role of deformation-induced supervision in shaping this space. Full article

► Show Figures

Figure 1

31 pages, 7415 KB

Open AccessArticle

A Spatio-Temporal Conceptual Model for Sound Representation in Geographic Information Systems

by Chamseddine Zaki, Houssein Taleb, Mostafa Rizk, Hussein Sheaib, Chadia Sawaya, Bilel Neji and Abbass Nasser

ISPRS Int. J. Geo-Inf. 2026, 15(9), 418; https://doi.org/10.3390/ijgi15090418 - 11 Sep 2026

Abstract

Geographic information systems have traditionally focused on the representation of visual and geometric aspects of geographic phenomena, while auditory information has largely been ignored or reduced to simple numerical attributes. Although numerous spatio-temporal data models have been proposed to describe dynamic geographic processes, [...] Read more.

Geographic information systems have traditionally focused on the representation of visual and geometric aspects of geographic phenomena, while auditory information has largely been ignored or reduced to simple numerical attributes. Although numerous spatio-temporal data models have been proposed to describe dynamic geographic processes, few of them provide explicit semantic constructs for representing sound and its evolution over space and time. This paper extends the Modeling of Application Data with Spatio-Temporal Features (MADS) conceptual model by introducing an explicitly defined acoustic semantic dimension. The extension separates source-intrinsic acoustic emission (soundfeature), source–receiver propagation and exposure (AFFECTS and associated EXPOSUREZONE instances), and receiver-centered contextual perception (PERCEPTIONASSESSMENT). These complementary constructs enable acoustic emission, propagation, temporal dynamics, spatial exposure, and perceptual assessment to be represented within a unified spatio-temporal semantic framework. The applicability of the proposed approach is illustrated through a proof-of-concept scenario and representative database queries, demonstrating how the proposed acoustic constructs can be mapped, stored, and queried using standard GIS database infrastructure. Full article

29 pages, 16925 KB

Open AccessArticle

Spatial Allocation of Veterinary Healthcare Facilities in Beijing Using Random Forest and Priority-Constrained Covering Optimisation

by Boting Xi, Shaohua Wang, Siyu Zhao, Wenda Wang, Liang Long, Chang Liu, Jingyi Zhou and Haojian Liang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 417; https://doi.org/10.3390/ijgi15090417 - 11 Sep 2026

Abstract

Veterinary hospitals are increasingly important neighborhood services, but citywide evidence on their strategic allocation remains limited. Existing GIS-based suitability studies identify favorable areas, but do not determine which limited set of planning nodes to prioritise when existing supply is clustered and underserved areas [...] Read more.

Veterinary hospitals are increasingly important neighborhood services, but citywide evidence on their strategic allocation remains limited. Existing GIS-based suitability studies identify favorable areas, but do not determine which limited set of planning nodes to prioritise when existing supply is clustered and underserved areas require explicit protection. This study develops a transparent allocation framework for Beijing by integrating Random Forest (RF)-based observed clinic-location probability, E2SFCA-derived service-gap indicators, and a priority-constrained covering optimisation model. The RF output is interpreted as a proxy for location attractiveness, rather than a direct estimate of demand for veterinary healthcare. The optimisation then protects predefined priority areas and maximises weighted coverage under alternative facility budget scenarios. Additional analyses include a threshold-derived priority set, class-imbalance metrics, catchment-radius sensitivity, key-area-count sensitivity, systematic candidate/key-score coefficient perturbation, district-grouped spatial validation, raw versus calibrated RF allocation sensitivity, and pet-service endogeneity checks. Under the main 5 km catchment and P = 40 scenario, the model achieved complete priority-area coverage and weighted coverage of 0.656. Coverage increased further at higher budgets, so P = 40 is not regarded as a practical optimum, but rather as a diminishing-return scenario. At P = 20 and P = 40, priority-area coverage increased by 5.0 and 2.5 percentage points relative to MCLP, respectively, while aggregate weighted coverage remained comparable. The framework supports strategic planning priority analysis, not direct commercial site selection. Full article

► Show Figures

Figure 1

35 pages, 32944 KB

Open AccessReview

Data-Assimilation-Driven Geohazard Monitoring and Early Warning Along Railways: A Review and the PAD Framework

by Yan Du, Anqi Zhang, Mowen Xie, Yujing Jiang, Hongda Zhang and Jingnan Liu

ISPRS Int. J. Geo-Inf. 2026, 15(9), 416; https://doi.org/10.3390/ijgi15090416 - 11 Sep 2026

Abstract

Conventional early-warning methods for geohazards along railways rely largely on single observations, empirical criteria or static analysis, and struggle to meet the demands of corridor-scale screening and dynamic tracking. To address this gap, a railway-oriented Perception–Assimilation–Decision (PAD) closed-loop early-warning framework is proposed. The [...] Read more.

Conventional early-warning methods for geohazards along railways rely largely on single observations, empirical criteria or static analysis, and struggle to meet the demands of corridor-scale screening and dynamic tracking. To address this gap, a railway-oriented Perception–Assimilation–Decision (PAD) closed-loop early-warning framework is proposed. The evolutionary patterns of typical geohazards along railways, including landslides, rockfalls, debris flows and settlement, are reviewed together with the application scope and limitations of multi-source monitoring techniques. Additionally, differentiated assimilation strategies are clarified, with continuous deformation and hydro-mechanical state updating for plastic failure and damage-sensitive evidence and critical-state identification for brittle failure. On this basis, a mechanism–data dual-driven assimilation paradigm and a two-scale PAD organisation, comprising corridor-scale spatial screening and site-scale state updating, are introduced. Recent applications show that data assimilation has shifted from correcting a single monitoring variable toward the dynamic coupling of multi-source observations with physical models. By establishing the logical chain from multi-source perception to state updating and finally to railway engineering response, the PAD framework transforms the traditional anomaly-identification-based warning mode into closed-loop risk management. The results provide a reference for building geohazard early-warning systems and engineering-oriented response along railways. Full article

► Show Figures

Figure 1

22 pages, 1203 KB

Open AccessArticle

Metadata Quality and Discoverability in the NCCU CoDE Open Data Hub: A FAIR- and FGDC CSDGM-Informed Assessment

by Chima Okoli, Timothy Mulrooney, Tony Bawo Esimaje and Jasmine Allen

ISPRS Int. J. Geo-Inf. 2026, 15(9), 415; https://doi.org/10.3390/ijgi15090415 - 10 Sep 2026

Abstract

University geospatial hubs support research dissemination, teaching, public data access, and community engagement, but their value depends on whether resources can be found, accessed, interpreted, integrated, and reused. This study evaluates 747 publicly listed items in North Carolina Central University’s CoDE Open Data [...] Read more.

University geospatial hubs support research dissemination, teaching, public data access, and community engagement, but their value depends on whether resources can be found, accessed, interpreted, integrated, and reused. This study evaluates 747 publicly listed items in North Carolina Central University’s CoDE Open Data Hub using FAIR principles as an interpretive framework and the Federal Geographic Data Committee Content Standard for Digital Geospatial Metadata (FGDC CSDGM) as the domain-specific measurement basis. Nineteen criteria were scored on explicit criterion-specific 0–2 rules using live ArcGIS item properties, formal metadata XML, and available layer properties. Discoverability was evaluated for a proportionally stratified sample of 153 items through exact-title, keyword, location, item-type, category-browsing, and click-depth tests. The equal-criterion benchmark averaged 28.57 of 38 (75.19%); equal FAIR-dimension weighting produced a mean of 73.26%, and 95.85% of items retained the same descriptive performance band. Independent rescoring of 50 stratified items produced 76.74% exact criterion-level agreement, a linear weighted Cohen’s kappa of 0.587, and an absolute-agreement ICC of 0.715 for total scores. FAIR-aligned scores were highest for Findability (87.25%) and Accessibility (85.96%), followed by Reusability (67.26%) and Interoperability (52.59%). Exact-title and item-type searches retrieved all sampled items, while keyword search was successful for 88.24%. The results distinguish public availability from technical reuse readiness and identify spatial reference, attribute definitions, lineage, limitations, category structure, and navigation depth as priorities. The performance bands are study-specific descriptive summaries rather than FGDC compliance levels or FAIR certification. The study provides an evidence-preserving, type-aware method for applying FAIR principles alongside geospatial metadata standards in heterogeneous ArcGIS Hub catalogs. Full article

► Show Figures

Figure 1

29 pages, 6309 KB

Open AccessArticle

Evidence-Aware Data Model for Explainable Post-Hurricane Building Damage Assessment

by Ahram Song, Jinha Jung, Anjin Chang and Seula Park

ISPRS Int. J. Geo-Inf. 2026, 15(9), 414; https://doi.org/10.3390/ijgi15090414 - 10 Sep 2026

Abstract

Recent learning-based damage assessment methods using remote sensing data have greatly contributed to the rapid identification and classification of damaged buildings from pre- and post-disaster imagery. However, most outputs are provided in the form of final damage labels or scores. To address this [...] Read more.

Recent learning-based damage assessment methods using remote sensing data have greatly contributed to the rapid identification and classification of damaged buildings from pre- and post-disaster imagery. However, most outputs are provided in the form of final damage labels or scores. To address this limitation, this study proposes an evidence-aware data model for explainable post-disaster building damage assessment. The proposed model is derived from a component-level roof-damage assessment workflow using optical imagery and post-disaster surface-height data, and is designed to describe the observational evidence, intermediate indices, and decision rules leading to a specific damage grade, as well as its interpretation as a post-disaster change event. Based on the proposed logical data model, a hybrid spatial–graph database was implemented to support both spatial retrieval and the semantic explanation of damage assessment results. The proposed model was evaluated through six competency questions derived from different damage assessment requirements. The results confirmed that the proposed model can support component-level retrieval of damage results, tracing of damage evidence and assessment rules, spatial visualization of damaged building components, and integrated spatial–semantic explanation for individual objects. The results provide a foundation for managing disaster damage assessment processes and results as explainable and reusable structured data. Full article

► Show Figures

Figure 1

29 pages, 4655 KB

Open AccessArticle

A BERT-CRF and Case-Crossover Framework for Evaluating Tourism Risk and Warning System Performance in Sichuan, China

by Aike Kan, Nengsheng Li, Xiao Yang, Yichen Li and Hongzhou Deng

ISPRS Int. J. Geo-Inf. 2026, 15(9), 413; https://doi.org/10.3390/ijgi15090413 - 10 Sep 2026

Abstract

Improving the precise perception of disaster risk and evaluating the effectiveness of warning systems are essential for natural-hazard prevention, mitigation, and spatial risk management. Using Sichuan Province, China, as a case study, this study treats government-issued disaster warning texts within 72 h before [...] Read more.

Improving the precise perception of disaster risk and evaluating the effectiveness of warning systems are essential for natural-hazard prevention, mitigation, and spatial risk management. Using Sichuan Province, China, as a case study, this study treats government-issued disaster warning texts within 72 h before accident occurrence as formal risk signals and spatiotemporal information sources. Drawing on the protective action decision model and risk communication theory, we propose an integrated framework combining BERT-CRF-based information extraction, spatiotemporal analysis, and a case-crossover design to evaluate a multi-level warning system through the chain of risk identification, risk communication, protective action, and risk outcome. The results show a clear gradient across warning levels. High-level warnings are significantly associated with lower accident risk, whereas lower-level warnings can identify risky contexts but have limited intervention effects. These findings indicate that disaster warning systems have dual functions of risk identification and intervention, while medium-level warnings may reveal a weak link in the transition from risk perception to protective action. The study provides a geospatial analytical framework for linking official warning information with tourism safety outcomes and offers empirical support for optimizing warning systems and strengthening hazard risk governance in high-risk regions. Full article

► Show Figures

Figure 1

22 pages, 24780 KB

Open AccessArticle

Site Suitability Analysis for Electric Vehicle Charging Stations Using a GIS-Based Multi-Criteria Decision Model: A Case Study of Islamabad

by Hafiz Abdul Wajid, Mehtab Khan, Asim Farooq, Muhammad Abid, Danish Farooq and Huzaifa Qadeer

ISPRS Int. J. Geo-Inf. 2026, 15(9), 412; https://doi.org/10.3390/ijgi15090412 - 8 Sep 2026

Abstract

Electric vehicles (EVs) are attracting choice and adoption as a travel mode in global transportation systems, driven by technological innovation, economic considerations, and advancing sustainable urban development. The planning, development, design, and location of EV charging stations are challenging tasks for underdeveloped countries [...] Read more.

Electric vehicles (EVs) are attracting choice and adoption as a travel mode in global transportation systems, driven by technological innovation, economic considerations, and advancing sustainable urban development. The planning, development, design, and location of EV charging stations are challenging tasks for underdeveloped countries such as Pakistan. To propose the site location for an EV charging station, this study comprises Multi-Criteria Decision Analysis and Geographic Information Systems. The study adopts a mixed-methods design, combining qualitative expert input with quantitative spatial analysis to investigate technical, social, and infrastructural criteria. The study considers indicators, including site suitability, population density, interaction between land use and transport, road network, transportation interactions, existing electricity grid infrastructure, and existing fueling and charging stations. This research presents pairwise expert comparisons, indicating that grid capacity (0.28) and demand (0.30) are important factors to consider. A composite suitability Index (CSI) through GIS-weighted overlay was used to classify the area into low, medium, and high suitability zones. According to the CSI, 26% of the area falls in highly suitable areas for EV infrastructure in Islamabad, and 41% falls in medium-to-high suitable areas. The remaining 33% area lies in low-suitability peripheral zones near the hilly region of Islamabad city, which is not considered suitable for EV infrastructure. A total of 67% of the city’s area is suitable for EV infrastructure. Full article

► Show Figures

Figure 1

33 pages, 4667 KB

Open AccessArticle

A Spatial Decision Support Framework for Winter-Rapeseed Expansion on Stable Winter–Fallow Cropland Using Multi-Source Remote Sensing, Satellite Embedding, and MaxEnt

by Yinlan Huang, Jingqiao Fang, Shi Chen and Tianshuo Xie

ISPRS Int. J. Geo-Inf. 2026, 15(9), 411; https://doi.org/10.3390/ijgi15090411 - 8 Sep 2026

Abstract

Under increasing cropland constraints and pressure to secure oilseed supplies, using winter–fallow cropland for winter rapeseed production can improve annual cropland-use efficiency. Taking the Wanjiang Plain, China, as the study area, this study integrated 10 m winter–fallow cropland maps (2019–2024), 30 m winter [...] Read more.

Under increasing cropland constraints and pressure to secure oilseed supplies, using winter–fallow cropland for winter rapeseed production can improve annual cropland-use efficiency. Taking the Wanjiang Plain, China, as the study area, this study integrated 10 m winter–fallow cropland maps (2019–2024), 30 m winter rapeseed maps (2000–2022), annual Satellite Embedding features, cropland data, and administrative boundaries. Multi-year occurrence frequency and Getis–Ord Gi* statistics characterized temporal persistence and spatial clustering. A MaxEnt model calibrated with 394 occurrence records from long-term high-frequency rapeseed areas and 26 screened embedding dimensions delineated cropland with present-day land-surface characteristics similar to historically persistent rapeseed locations. This layer was progressively intersected with the historical winter–fallow union and stable winter–fallow cropland. Stable winter–fallow cropland covered approximately 4437 km², whereas long-term high-frequency winter rapeseed covered only 568 km², revealing a marked spatial mismatch. Under five-fold spatial cross-validation, the selected linear-feature model with a regularization multiplier of 4 achieved a mean test AUC of 0.904 ± 0.015 and a 10% training-omission rate of 0.108 ± 0.022. Using the model-specific threshold of 0.3096, the final estimates were 6017 km² of potentially suitable cropland, 2943 km² of general expansion potential, and 1137 km² of candidate spatial priority areas for field verification. The last tier was concentrated mainly in Xuanzhou District, Lujiang County, urban Wuhu, Wuwei City, Nanling County, and He County. The framework provides a cautious spatial-screening basis for optimizing winter–fallow cropland use and guiding subsequent field and feasibility assessments. Full article

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

► Show Figures

Figure 1

21 pages, 7948 KB

Open AccessArticle

Spatial Differentiation and Quantitative Assessment of Disaster Risk for Socialist Built Heritage Driven by Meteorological–Environmental Factors in Henan, China

by Zhong Sun, Yukun Zhang and Wei Wang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 410; https://doi.org/10.3390/ijgi15090410 - 8 Sep 2026

Abstract

In response to the escalating challenges of global climate change and extreme meteorological events, this study addresses the critical issue of low disaster resistance in socialist-built heritage (SBH, i.e., built cultural heritage constructed during China’s socialist construction stage from 1949 to 1978). Focusing [...] Read more.

In response to the escalating challenges of global climate change and extreme meteorological events, this study addresses the critical issue of low disaster resistance in socialist-built heritage (SBH, i.e., built cultural heritage constructed during China’s socialist construction stage from 1949 to 1978). Focusing on Henan Province, China, with meteorological observation datasets spanning 1990–2023 and 1362 SBH sites constructed from 1949 to 1978, we innovatively construct a four-dimensional risk assessment model integrating Meteorological–Environmental Hazard (H), Exposure (E), Vulnerability (V), and Ecological Resilience (R). By synthesizing Spearman correlation analysis, Variance Inflation Factor (VIF) diagnostics, and piecewise regression, this research elucidates the spatial differentiation and driving mechanisms of meteorology-induced compound disaster risks. Key findings identify Zhengzhou and Kaifeng as core high-risk zones and precisely quantify a critical hazard threshold of 0.4 with a 95% breakpoint confidence interval [0.372, 0.428], passing the Chow test p < 0.001 and validated via historical heritage damage records (R² = 0.78). Variable-importance analysis proves that extreme precipitation (32.7%) and annual strong-wind days (28.1%) are the dominant meteorological–environmental drivers, alongside the significant buffering effect of ecological resilience. Ultimately, this study refines the disaster risk assessment framework for SBH, providing robust theoretical support and a decision-making basis for adaptive conservation strategies in similar regions. The average disaster risk of SBH in Henan is 0.421 ± 0.153; for each 0.1 growth of H-E-V coupling term above the threshold of 0.4, disaster risk rises by 37.2%. Full article

► Show Figures

Figure 1

29 pages, 3196 KB

Open AccessArticle

Integrating Satellite Data with Ground-Based Low-Cost Sensors for Hourly Fine-Scale Land Surface Temperature Mapping: A Case Study in Bentley, Western Australia

by Ratovoson Robert Andriambololonaharisoamalala, Petra Helmholz, Ivana Ivánová, Dimitri Bulatov, Eriita Jones, Susannah Soon and Yongze Song

ISPRS Int. J. Geo-Inf. 2026, 15(9), 409; https://doi.org/10.3390/ijgi15090409 - 7 Sep 2026

Abstract

Climate change and rapid urbanisation are intensifying the urban heat island effect, increasing thermal stress, degrading air quality, and leading to rising energy demand. Monitoring neighbourhood-scale heat requires Land Surface Temperature (LST) observations at fine spatial and temporal resolutions, yet satellite thermal products [...] Read more.

Climate change and rapid urbanisation are intensifying the urban heat island effect, increasing thermal stress, degrading air quality, and leading to rising energy demand. Monitoring neighbourhood-scale heat requires Land Surface Temperature (LST) observations at fine spatial and temporal resolutions, yet satellite thermal products are limited by revisit frequency, acquisition time, and cloud cover. This study developed a novel approach integrating satellite-derived land cover characteristics with continuous contact-based temperature measurements from low-cost LoRaWAN sensors and geostatistical modelling to generate hourly LST maps at 10 m resolution. The technique provides communities with simpler, affordable methods for measuring heat islands and supporting mitigation strategies. Observations from 52 locations across Curtin University’s Bentley campus in Perth, Western Australia, were combined with land cover indices. Empirical Bayesian Kriging captured spatial and temporal urban heat patterns with a root mean square error of approximately 3 °C, representing a bias of near 1 °C. Predictions were consistent with Landsat-derived LST, revealing persistent heat retention over asphalt and cooler conditions associated with vegetation. Integrating satellite-derived predictors with ground measurements provides continuous fine-scale information to identify local heat hotspots and inform targeted mitigation. Unlike satellite data, these low-cost ground measurements could be collected with the help of urban practitioners, developers, and academic institutions. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces (2nd Edition))

► Show Figures

Figure 1

20 pages, 3925 KB

Open AccessArticle

Multiscale Spatial Associations Between the Built Environment and Urban Vitality: Evidence from Changchun, China

by Haishan Liang, Haoran Chen and Chunlin Wang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 408; https://doi.org/10.3390/ijgi15090408 - 7 Sep 2026

Abstract

Urban vitality matters for urban regeneration, yet built-environment–vitality associations vary spatially and by variable. We examined 2621 regular 500 m grid cells across Changchun’s built-up area. PCA combined 2024 population, 2024 nighttime light, and 2018 mean building height into a composite Urban Vitality [...] Read more.

Urban vitality matters for urban regeneration, yet built-environment–vitality associations vary spatially and by variable. We examined 2621 regular 500 m grid cells across Changchun’s built-up area. PCA combined 2024 population, 2024 nighttime light, and 2018 mean building height into a composite Urban Vitality index. Five built-environment indicators—Functional Density, POI Diversity, Mean NDVI, Transit Proximity, and Commercial Proximity—were analyzed using Global Moran’s I, ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). PC1 explained 82.15% of the variance, and Urban Vitality showed strong spatial autocorrelation (Moran’s I = 0.8332). Model fit increased from OLS (R² = 0.7767) to GWR (R² = 0.9225) and MGWR (R² = 0.9296). MGWR bandwidths were localized for Functional Density (61), POI Diversity (62), and Mean NDVI (71), but broader for Transit Proximity (2548) and Commercial Proximity (2619). Adjusted local inference identified significant positive associations for Functional Density, POI Diversity, and Transit Proximity, significant negative associations for Mean NDVI, and no significant local association for Commercial Proximity. Although sensitivity analyses generally preserved fit and median directions, excluding building height changed the Transit Proximity bandwidth from 2548 to 70 neighbors, indicating that its estimated association scale was sensitive to vitality-index specification. Commercial Proximity also remained sensitive to indicator operationalization. The findings identify where local diagnostic follow-up is warranted, while intervention effects remain outside the scope of the analysis. Full article

(This article belongs to the Special Issue Innovative Mobility Services for Smart Cities)

► Show Figures

Figure 1

27 pages, 4433 KB

Open AccessArticle

Evaluating the Strategic Pathways of Senegal’s National Spatial Data Infrastructure Using the United Nations Integrated Geospatial Information Framework (UN-IGIF)

by Alla Manga, Amadou Tahirou Diaw, Johannes Van Geertsom, Nicolas S. E. S. Sagna, Joep Crompvoets and Mouhammad Abdallah Diallo

ISPRS Int. J. Geo-Inf. 2026, 15(9), 407; https://doi.org/10.3390/ijgi15090407 - 7 Sep 2026

Abstract

In the context of growing international recognition of geospatial information as a strategic driver of evidence-based decision-making, digital transformation, and sustainable development, robust assessment frameworks are essential for guiding the development of National Spatial Data Infrastructures (NSDIs). This paper presents the first baseline [...] Read more.

In the context of growing international recognition of geospatial information as a strategic driver of evidence-based decision-making, digital transformation, and sustainable development, robust assessment frameworks are essential for guiding the development of National Spatial Data Infrastructures (NSDIs). This paper presents the first baseline assessment of Senegal’s NSDI using the United Nations Integrated Geospatial Information Framework (UN-IGIF). Drawing on the UN-IGIF Diagnostic Tool, supplemented by surveys, semi-structured interviews, and multi-stakeholder consultations, the analysis reveals a low overall maturity level (33/100), despite relatively better performance in institutional governance and geospatial data availability. Significant weaknesses persist in the areas of legislation, funding, standards implementation, innovation, partnerships, communication, and capacity building. The findings confirm the relevance of the UN-IGIF for assessing challenges related to geospatial governance in Francophone African countries and identify priority areas for reform, including strengthening coordination mechanisms, legal and regulatory modernization, sustainable financing, and strategic planning. By providing empirical evidence on both the NSDI maturity level and the operational applicability of the UN-IGIF, this study contributes to the international literature on geospatial governance and offers a solid foundation for future reforms in Senegal and other low- and middle-income countries. Full article

► Show Figures

Figure 1

22 pages, 12551 KB

Open AccessReview

Knowledge Structure, Technical Pathways, and Spatial Decision-Making Challenges in the Digital-Intelligent Transformation of Territorial Spatial Governance: An Artificial Intelligence Perspective

by Wei Shan, Xiaobin Jin, Honghui Zhang, Hanbing Li, Yuhua Wang, Bo Han and Yinkang Zhou

ISPRS Int. J. Geo-Inf. 2026, 15(9), 406; https://doi.org/10.3390/ijgi15090406 - 7 Sep 2026

Abstract

As artificial intelligence (AI) becomes increasingly integrated into spatial planning and governance, a central challenge is translating AI-enabled technical capabilities into institutionally usable spatial decision-making capacity. Taking territorial spatial governance (TSG) in China as a context-specific governance setting, this study combines bibliometric analysis [...] Read more.

As artificial intelligence (AI) becomes increasingly integrated into spatial planning and governance, a central challenge is translating AI-enabled technical capabilities into institutionally usable spatial decision-making capacity. Taking territorial spatial governance (TSG) in China as a context-specific governance setting, this study combines bibliometric analysis and structured review to examine its knowledge evolution, technical pathways, and spatial decision-making constraints. The results show that: (1) research has progressively shifted from digital support and spatial monitoring toward intelligent analysis and governance-oriented applications; (2) four recurrent pathways of AI embedding are synthesized from the literature—sensing and identification, analytical simulation, decision support, and platform coordination; (3) the translation of these technical capabilities into spatial decision-making capacity is constrained by heterogeneous data foundations, limited model interpretability and transferability, institutional misalignment, and uneven organizational implementation; and (4) spatial decision-making capacity emerges through the interaction of technical pathways and constraint mechanisms under the multi-scale, differentiated, and rule-bound spatial logic of TSG. These findings provide an analytical basis for understanding how AI-enabled technical capabilities are translated from technical feasibility into governance usability under institutionally and spatially specific conditions. Full article

(This article belongs to the Topic AI and Multi-Source Geospatial Observation for Global Change, Ecological Sustainability and Land System Science)

► Show Figures

Figure 1

32 pages, 4282 KB

Open AccessArticle

A Pilot Study on a GPU-Accelerated Voxel Simulation Framework for 3D Indoor and Urban-Scale Gas Dispersion and Aerosol Transport

by Haowen Xu, Sisi Zlatanova, Ben Gorte, Rabindra Lamsal, David Heslop, Ruiyu Liang and Ismet Canbulat

ISPRS Int. J. Geo-Inf. 2026, 15(9), 405; https://doi.org/10.3390/ijgi15090405 - 5 Sep 2026

Abstract

The increasing complexity of environmental analysis requires new approaches for interactive-scale simulation across indoor and urban spaces. While computational fluid dynamics (CFD) models provide detailed representations of gas dispersion and aerosol transport, they are often computationally intensive for interactive environmental analysis and integration [...] Read more.

The increasing complexity of environmental analysis requires new approaches for interactive-scale simulation across indoor and urban spaces. While computational fluid dynamics (CFD) models provide detailed representations of gas dispersion and aerosol transport, they are often computationally intensive for interactive environmental analysis and integration into digital twin platforms. This pilot study presents a GPU-accelerated voxel simulation framework for modeling three-dimensional gas dispersion and aerosol transport using structured voxel representations derived from BIM, LiDAR, GIS, and other 3D built-environment datasets. The framework provides physically informed, CFD-inspired simulation at sub-meter to meter-scale spatial resolutions while maintaining interactive runtime performance suitable for building management, ventilation analysis, environmental monitoring, hazard assessment, and emergency response applications. Transport dynamics are modeled using a discretized advection–diffusion formulation incorporating airflow-driven advection, diffusion, source emissions, and voxel-level sink mechanisms. A key contribution is the development of a voxel-native GPU-parallel computational architecture implemented in Python 3.10.20 using Taichi kernels. Prototype simulations and comparative validation against a benchmark ANSYS Fluent 20.1 simulation demonstrate stable transport behavior, encouraging agreement with the CFD solution, browser-based three-dimensional visualization, and efficient execution on commodity GPU hardware. Experimental scenarios include a voxelized three-story Industry Foundation Classes (IFC) building model comprising approximately 34.5 million active voxels (

582 \times 382 \times 155

voxels) and an urban-scale 3D city model spanning approximately

300 \times 300 \times 150

m and containing up to 13.9 million active voxels. Simulations containing tens of millions of voxels were completed within minutes on a single consumer-grade GPU, demonstrating the scalability of the framework. These results demonstrate that the proposed framework provides an efficient voxel-based approximation of gas dispersion suitable for interactive environmental analysis and can support future integration with digital twin and AI-assisted environmental simulation systems. Full article

(This article belongs to the Topic The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications)

► Show Figures

Figure 1

39 pages, 7781 KB

Open AccessArticle

Integrating Photogrammetry and SLAM for the 3D Geometric Documentation of Cultural Heritage Monuments: A Reproducible Multi-Sensor Workflow Supported by an Open Dataset

by Styliani Verykokou, Konstantinos Nikolitsas, George Piniotis, Regina Chliverou and Efi Dimopoulou

ISPRS Int. J. Geo-Inf. 2026, 15(9), 404; https://doi.org/10.3390/ijgi15090404 - 5 Sep 2026

Abstract

The 3D geometric documentation of cultural heritage monuments requires spatial datasets that are accurate, complete and suitable for conservation, monitoring, visualization and heritage management. However, complex geometries, occlusions, limited accessibility, vegetation and other field-acquisition constraints often prevent a single surveying technique from providing [...] Read more.

The 3D geometric documentation of cultural heritage monuments requires spatial datasets that are accurate, complete and suitable for conservation, monitoring, visualization and heritage management. However, complex geometries, occlusions, limited accessibility, vegetation and other field-acquisition constraints often prevent a single surveying technique from providing a complete and metrically reliable representation. In this context, photogrammetry and simultaneous localization and mapping (SLAM)-based mapping provide complementary capabilities, with each method offering advantages and limitations regarding metric accuracy, spatial coverage, detail representation, acquisition flexibility and operational efficiency. This work develops, applies and evaluates a reproducible end-to-end workflow for the metric 3D documentation of complex cultural heritage monuments through multi-sensor integration. The proposed approach combines the metric robustness and visual richness of photogrammetric reconstruction with the rapid acquisition and spatial coverage enabled by SLAM-based mapping, while producing reusable datasets for conservation planning, comparative studies, education and broader heritage applications. The workflow integrates unmanned aerial vehicle (UAV) and close-range photogrammetry, SLAM-based mapping and geodetic control within a common reference system and is demonstrated through the documentation of a historic monastery. Both datasets showed centimetre-level agreement with geodetic observations, while photogrammetry yielded fuller exterior coverage and higher-quality texture, and SLAM enabled rapid interior coverage. The CH-PhotoSLAM3D dataset is released to support reproducibility and further research. Full article

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

► Show Figures

Figure 1

Journal Description

ISPRS International Journal of Geo-Information

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Further Information

Guidelines

MDPI Initiatives

Follow MDPI