ISPRS International Journal of Geo-Information

31 pages, 5820 KB

Open AccessArticle

Identifying Climate and Anthropogenic Risks Along the Beijing–Hangzhou Grand Canal Using GIS-Based Spatiotemporal Analysis

by Junyi Shi, Lijun Yu, Ze Liu, Hui Wang and Yueping Nie

ISPRS Int. J. Geo-Inf. 2026, 15(6), 230; https://doi.org/10.3390/ijgi15060230 - 22 May 2026

Abstract

Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based [...] Read more.

Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based spatiotemporal assessment framework to quantify natural risk, anthropogenic pressure, and their coupled patterns during 1995–2024. Approximately 350 canal segments were constructed as comparable assessment units and linked with 49 heritage sites and 18 World Heritage canal sections through a multi-scale spatial framework integrating canal sections, buffer zones, and heritage sites. Natural risk was characterized using extreme temperature, precipitation, and drought indices, while anthropogenic pressure was represented by nighttime lights, population density, impervious surface, and road density. The results reveal a clear north–south gradient in integrated natural risk, with higher values concentrated in the southern canal sections. Among the three natural-risk modules, temperature, precipitation, and drought contributed weights of 0.594, 0.242, and 0.164, respectively, indicating the dominant role of heat-related processes. The first two principal components of anthropogenic pressure explained 80.8% of the total variance. Four dominant coupling types were identified, among which the dual high-pressure type was concentrated mainly in the southern canal and marked the most critical areas of compound risk. This study provides a geospatial approach for hotspot detection and spatial decision support for the conservation of large linear heritage systems. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives, 2nd Edition)

► Show Figures

Figure 1

24 pages, 4280 KB

Open AccessArticle

Multi-Agent Deep Reinforcement Learning with Contrastive Policy Diversification and Hierarchical Graph Networks for Urban Traffic Signal Control

by Liping Yan, Haojie Jia, Shaofeng Wang, Peiran Wu and Wenzhi Zhao

ISPRS Int. J. Geo-Inf. 2026, 15(6), 229; https://doi.org/10.3390/ijgi15060229 - 22 May 2026

Abstract

Multi-Agent Reinforcement Learning (MARL) provides an effective approach for urban multi-intersection traffic signal control. However, existing methods have faced two fundamental challenges, policy homogenization and inefficient credit assignment. The former led to convergent agent policies that failed to adapt to heterogeneous traffic patterns, [...] Read more.

Multi-Agent Reinforcement Learning (MARL) provides an effective approach for urban multi-intersection traffic signal control. However, existing methods have faced two fundamental challenges, policy homogenization and inefficient credit assignment. The former led to convergent agent policies that failed to adapt to heterogeneous traffic patterns, while the latter prevented agents from accurately evaluating their individual contributions to system performance. To address these issues, this paper proposes a Multi-Agent Hierarchical Contrastive Learning Traffic Signal Control (MAHCL-TSC) model. The model incorporates an unsupervised contrastive learning module that enhances the discriminative power of state representations, thereby alleviating policy homogenization. Additionally, it designs a hierarchical graph convolutional credit allocation network that leverages road network topology and functional characteristics to enable structure-aware collaborative value estimation, significantly improving the precision of credit assignment. Based on these components, a Contrastive QTRAN with Hierarchical Graph Convolution (CQTRAN-HGC) algorithm is proposed, which jointly optimizes contrastive learning loss and QTRAN constraint loss. Experiments conducted in the Simulation of Urban Mobility (SUMO) simulation environment on 4 × 4 and 6 × 6 synthetic grid networks demonstrate that the proposed model improves traffic signal control performance under the tested structured simulation settings and shows potential scalability as the network size increases. Full article

(This article belongs to the Topic Applications of Intelligent Technologies in the Life Cycle of Transportation Infrastructure)

► Show Figures

Figure 1

23 pages, 2709 KB

Open AccessArticle

Marine Geographic Information Systems, Spatial Analysis Tools in the Management Process of Spanish Marine Protected Areas

by Dulce Mata, Paula Gil, Ángela Bellido and Olvido Tello

ISPRS Int. J. Geo-Inf. 2026, 15(6), 228; https://doi.org/10.3390/ijgi15060228 - 22 May 2026

Abstract

Spain’s extensive marine jurisdiction—comprising a continental shelf of approximately 100,000 km² and an Exclusive Economic Zone approaching one million km²—requires robust geospatial frameworks to support ecosystem assessment and marine policy implementation. This study presents GIS-based methodologies developed by the Spanish [...] Read more.

Spain’s extensive marine jurisdiction—comprising a continental shelf of approximately 100,000 km² and an Exclusive Economic Zone approaching one million km²—requires robust geospatial frameworks to support ecosystem assessment and marine policy implementation. This study presents GIS-based methodologies developed by the Spanish Oceanographic Institute (IEO-CSIC) within national initiatives such as LIFE IP INTEMARES project and the implementation of Marine Strategy Framework Directive (European Directive 2008/56/EC). The geospatial workflows developed for these initiatives integrates heterogeneous spatial datasets—such as multibeam bathymetry, acoustic backscatter, Remote Operated Vehicle (ROV) and towed-camera transects, sediment samples, oceanographic profiles, and species-habitat occurrence records—into a unified spatial analysis environment. Applied methods include digital terrain modeling, derivation of geomorphometric indices (e.g., slope, rugosity, curvature), image classification, and spatial statistics to quantify habitat extent, condition, and anthropogenic pressures. An integrated spatial analysis framework combining environmental and anthropogenic data is used to support zoning and management decisions within Marine Protected Areas (MPAs). Additionally, the deployment of WebGIS platforms facilitates data dissemination, iterative review, and stakeholder engagement, thereby enhancing transparency and accessibility. The resulting high-resolution maps, harmonized datasets, and computed spatial indicators—aligned with Marine Strategy Framework Directive (MSFD) descriptors such as habitat distribution (D1C4–C5) and seafloor integrity (D6C2–C3)—demonstrate how GIScience methods provide reproducible, decision-ready information to support the monitoring and management of Spain’s diverse marine ecosystems. Full article

► Show Figures

Figure 1

30 pages, 17940 KB

Open AccessArticle

Enhancing Spatial Orientation and Map-Reading Skills: Using Mental Maps and VR in Field Trips for Geography Students

by Péter Czomba, Klára Czimre, Károly Teperics, Gyöngyi Bujdosó, Ernő Molnár, Gábor Négyesi and Bálint Bence Juhász

ISPRS Int. J. Geo-Inf. 2026, 15(5), 227; https://doi.org/10.3390/ijgi15050227 - 21 May 2026

Abstract

Enhancing spatial orientation and map-reading skills is a cornerstone of geography education, yet the comparative efficacy of physical versus virtual reality learning environments (VRLEs) remains a subject of ongoing debate. This study evaluates the development of navigational competencies through a counterbalanced crossover experimental [...] Read more.

Enhancing spatial orientation and map-reading skills is a cornerstone of geography education, yet the comparative efficacy of physical versus virtual reality learning environments (VRLEs) remains a subject of ongoing debate. This study evaluates the development of navigational competencies through a counterbalanced crossover experimental design involving 20 geography and geography teacher major students. Participants performed standardized spatial tasks, including bearing calculation and distance estimation, in both the volcanic landscape of the Tapolca Basin, Hungary, and its smartphone-based 360-degree virtual reality (VR) counterpart. To assess longitudinal retention and cross-modal transfer, a three-month interval was maintained between the two learning phases, supported by a robust pre-test/post-test framework. Results indicate that while both environments are susceptible to spatial distortions driven by the visual dominance of physiographic landmarks, VR-based training effectively scaffolds the cognitive frameworks required for real-world navigation. The findings confirm that spatial mental models acquired in a virtual setting possess significant cognitive resilience, as navigational accuracy was maintained over the three-month interval. In conclusion, this research justifies a hybrid pedagogical approach, where immersive digital simulations serve as a preparatory tool for physical fieldwork. The synergy of both modalities is essential for cultivating the resilient spatial intelligence required for professional geographic practice. Full article

25 pages, 2470 KB

Open AccessArticle

A Case-Based Reasoning Method for Knowledge Graph Place Name Service Composition Integrating Semantic and Graph Structural Similarity

by Wenjuan Lu, Dongping Ming, Xi Mao, Jizhou Wang and Pengda Wu

ISPRS Int. J. Geo-Inf. 2026, 15(5), 226; https://doi.org/10.3390/ijgi15050226 - 21 May 2026

Abstract

In the contemporary field of geographic information, place name services serve as a core application support in geographic information science, widely applied in public services, cultural tourism, emergency management, and other scenarios. Place name service composition is a critical link in the integration [...] Read more.

In the contemporary field of geographic information, place name services serve as a core application support in geographic information science, widely applied in public services, cultural tourism, emergency management, and other scenarios. Place name service composition is a critical link in the integration of spatiotemporal knowledge and intelligent services for place names, determining the ability to rapidly solve complex place name problems. Traditional case-based reasoning methods are primarily rule-driven, making it difficult to deeply integrate semantic and graph structural features, and they also lack precision in measuring the similarity of multi-type place name service cases. To address this, this paper integrates knowledge graphs and case-based reasoning to propose a place name service composition method that balances semantic and graph structural similarity, aiming to enhance the response efficiency and recognition accuracy of complex natural language queries. The method consists of two steps: the first is constructing a knowledge graph case base. Semantic feature extraction is performed on the standard geographic question-answering standard dataset GeoQuery corpus to build a place name service knowledge graph case base that integrates semantic associations and spatial attributes. The second step is constructing a similarity model. The method combines four similarity measures—DeBERTa, TF-IDF, SimHash, and maximum common subgraph—and employs the Analytic Hierarchy Process for weighting to develop a novel similarity evaluation model for case-based reasoning. Experiments demonstrate that this method achieves a 21% improvement in F1-score compared to traditional rule-based methods. Furthermore, the developed prototype system for the intelligent recommendation of place name service composition achieves a recommendation accuracy of 92.64%. This research holds significant practical implications and application value for advancing the geographic information field toward intelligent and precision-based development. Full article

24 pages, 32774 KB

Open AccessArticle

Exploring the Nonlinear and Interactive Effects of the Built Environment and Air Pollution on Free-Floating Bike-Sharing Usage

by Ziye Liu, Jianyu Li, Shumin Wang, Jingyue Huang and Mingxing Hu

ISPRS Int. J. Geo-Inf. 2026, 15(5), 225; https://doi.org/10.3390/ijgi15050225 - 21 May 2026

Abstract

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution [...] Read more.

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution and its interaction with the built environment remain insufficiently understood. In this study, multisource data from Shenzhen are used, and an XGBoost–SHAP model is employed to comprehensively investigate the nonlinear associations among the FFBS trip volume, built environment, and air pollution while considering the spatial heterogeneity in interaction effects. The results indicate that population density, road density, building density, and PM2.5 are the most influential factors. In addition, significant temporal heterogeneity is observed between weekdays and weekends. The effects of the built environment variables and their interactions are more pronounced on weekdays than on weekends. More importantly, an interaction analysis reveals that the positive influence of compact urban development on cycling is conditional: in high-density areas with elevated pollution exposure, the health risks associated with air pollution can offset or even outweigh the mobility benefits of compactness. Overall, this study identifies the complex, spatially heterogeneous mechanisms through which the built environment and air quality jointly shape FFBS usage. These findings provide important evidence for integrating environmental health considerations into compact city planning and offer practical insights for promoting cycling and sustainable urban mobility in high-density cities. Full article

► Show Figures

Figure 1

20 pages, 405 KB

Open AccessArticle

A Geospatial Dynamic Warning Distance Model for Road Disaster Risks in Mixed-Traffic Flow Considering Vehicle Response Heterogeneity

by Yanbin Hu, Wenhui Zhou, Yi Li and Hongzhi Miao

ISPRS Int. J. Geo-Inf. 2026, 15(5), 224; https://doi.org/10.3390/ijgi15050224 - 21 May 2026

Abstract

Road disasters such as subsidence and bridge failures pose severe threats to traffic safety. Existing warning distance calculation methods typically assume homogeneous traffic flow and overlook the spatial heterogeneity of vehicle responses across different vehicle types, limiting their applicability for geospatial early warning [...] Read more.

Road disasters such as subsidence and bridge failures pose severe threats to traffic safety. Existing warning distance calculation methods typically assume homogeneous traffic flow and overlook the spatial heterogeneity of vehicle responses across different vehicle types, limiting their applicability for geospatial early warning systems. This paper proposes a dynamic warning distance model that integrates mixed-traffic flow composition—comprising human-driven vehicles (HDVs), Level 2 advanced driver-assistance system vehicles (ADASVs), and automated vehicles (AVs) of Level 3 and above—within a geospatial risk propagation framework. The model introduces vehicle-type weighting coefficients to quantify response differences, incorporates interaction delays calibrated through SUMO microsimulations, and accounts for cascading reaction delays caused by abrupt HDV braking. The methodology is illustrated using a counterfactual reconstruction of the 2024 Meizhou–Dapu Expressway collapse in China (52 fatalities). Based on reconstructed traffic conditions (80% HDVs, 15% ADASVs, 5% AVs; average speed 27.5 m/s; flow 1800 veh/h), the calculated dynamic warning distance is 153 m, which is 12% shorter than the speed-matched conventional stopping sight distance of 174 m (computed under consistent wet-pavement assumptions). Sensitivity analyses reveal that warning distance decreases substantially with increasing AV penetration (to 42 m in AV-dominated scenarios, a potential reduction of up to 74% compared with the HDV-dominated baseline, provided that residual HDVs are supported by V2X-based alerting) and varies monotonically with traffic flow, demonstrating the model’s adaptive capability. The proposed framework provides a theoretical foundation for adaptive geospatial disaster warning strategies and offers practical guidance for infrastructure development in the era of mixed-traffic automation. Full article

► Show Figures

Figure 1

54 pages, 2305 KB

Open AccessReview

Crowd Simulation: A Multi-Dimensional Systematic Mapping Study and Taxonomy

by Emad Felemban, Muhammad Hammad and Faizan Ur Rehman

ISPRS Int. J. Geo-Inf. 2026, 15(5), 223; https://doi.org/10.3390/ijgi15050223 - 21 May 2026

Abstract

Crowd simulation is essential for applications in evacuation planning, transportation systems, urban analytics, virtual reality, and intelligent mobility. Despite substantial progress, research in this field remains fragmented across diverse modeling paradigms, behavioral abstractions, simulation settings, implementation tools, and evaluation practices. To provide a [...] Read more.

Crowd simulation is essential for applications in evacuation planning, transportation systems, urban analytics, virtual reality, and intelligent mobility. Despite substantial progress, research in this field remains fragmented across diverse modeling paradigms, behavioral abstractions, simulation settings, implementation tools, and evaluation practices. To provide a unified overview, this study conducts a Systematic Mapping Study (SMS) of 54 peer-reviewed primary studies published between 2021 and 2025. Guided by a structured set of 15 research questions, the SMS examines dominant modeling paradigms, associated modeling techniques, spatial representations, behavioral layers, learning methods, and agent capabilities. The study further analyses simulation characteristics—including behavior types, granularity levels, temporal modes, environment types, and application domains—alongside implementation aspects such as programming tools and simulation platforms. Additionally, the mapping covers evaluation practices by identifying reported performance metrics and methodological approaches. Based on the extracted evidence, we propose a comprehensive taxonomy. The results highlight prevailing trends, gaps, and fragmentation in crowd simulation research, including uneven reporting of metrics, limited integration of learning-based methods, and inconsistencies in behavioral modeling. The study also synthesizes key technical challenges and corresponding solutions proposed in recent literature, offering a structured foundation for future research. Full article

► Show Figures

Figure 1

23 pages, 7126 KB

Open AccessArticle

A Preliminary Study on Mapping Methods of Geographical Features of Archaeological Remains and Ancient Human Behaviors in Prehistoric Settlement Landscape Reconstruction

by Lin Yang, Hui Li, Peng Yu and Weihong Wu

ISPRS Int. J. Geo-Inf. 2026, 15(5), 222; https://doi.org/10.3390/ijgi15050222 - 21 May 2026

Abstract

The reconstruction of ancient geographical scenarios is significant for understanding environmental changes and civilizational evolution. However, human activities, as the main subjects in these scenes, cannot be directly reconstructed due to the lack of written records. Archaeological sites, formed through long-term human activities [...] Read more.

The reconstruction of ancient geographical scenarios is significant for understanding environmental changes and civilizational evolution. However, human activities, as the main subjects in these scenes, cannot be directly reconstructed due to the lack of written records. Archaeological sites, formed through long-term human activities and natural processes, preserve material traces of ancient human behaviors within specific spatiotemporal contexts and provide critical evidence for inferring behaviors lacking written records. However, behavioral processes within site scenarios are difficult to observe and express directly. To address this challenge, we proposed a behavioral inference mapping method based on archaeological remains, integrating geography, archaeology, and behavioral science to support the inference and structured expression of ancient human behaviors. We first analyzed the relationships between behaviors and remain elements, and developed principles for inferring ancient human behaviors from remains. Secondly, combined with spatial analysis of geographic entities, we proposed multiscale geometric representations, methods for extracting and analyzing the geographical features of remains. We constructed a rule-driven mapping method of geographical features of archaeological remains and ancient human behaviors. Finally, the Taixi Site in Hebei Province and the Lingjiatan Site in Anhui Province were used as examples to verify the applicability and effectiveness of this method. This approach bridges remains and ancient human behaviors, demonstrates strong adaptability for behavioral-process inference, and provides new perspectives for settlement landscape reconstruction. Full article

► Show Figures

Figure 1

23 pages, 34582 KB

Open AccessArticle

Semi-Supervised AI for Architectural Heritage Classification and Style Lineage Discovery in Chinese Traditional Settlements

by Qing Han, Zicheng Wang, Chao Yin, Zhiwei Hou and Tianci Yao

ISPRS Int. J. Geo-Inf. 2026, 15(5), 221; https://doi.org/10.3390/ijgi15050221 - 20 May 2026

Abstract

Large-scale classification of architectural styles in Chinese traditional settlements is important for heritage conservation and geospatial documentation, but scalable deployment remains constrained by the high cost of expert annotation because villages are widely distributed, the imagery is captured from heterogeneous viewpoints, and each [...] Read more.

Large-scale classification of architectural styles in Chinese traditional settlements is important for heritage conservation and geospatial documentation, but scalable deployment remains constrained by the high cost of expert annotation because villages are widely distributed, the imagery is captured from heterogeneous viewpoints, and each architectural tradition exhibits substantial intra-class variation. To address this bottleneck, we propose CTSMatch, a label-efficient semi-supervised framework that combines an ImageNet-pretrained EfficientNetV2 backbone with SoftMatch-based adaptive pseudo-label weighting so that ambiguous but informative unlabeled samples can still contribute to training, thereby reducing reliance on costly expert annotation. We also construct SemiCTS, an extension of the original CTS dataset that adds 4360 unlabeled images. Using only 545 labeled samples, CTSMatch achieves 96.93% accuracy on SemiCTS, outperforming the strongest fully supervised baseline (Dense-TL-Aug) by 2.73 percentage points and two standard semi-supervised baselines (FixMatch and FreeMatch) by 3.06 percentage points. Beyond classification, we further analyze the feature space to examine stylistic lineage through intra-style heterogeneity, inter-style transitions, and outlier detection. The results reveal two broad regional groupings, a northern cluster (Jing, Jin, Su) and a southern cluster (Chuan, Min, Wan), connected by gradual transitions rather than rigid boundaries. Approximately 15% of the samples are identified as atypical cases, including 8.7% comprising regional variants and 6.3% comprising hybrid forms. These findings show that CTSMatch provides a practical label-efficient framework for architectural heritage classification while supporting the interpretable analysis of stylistic diversification and convergence in Chinese traditional settlements. Full article

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

► Show Figures

Figure 1

22 pages, 12945 KB

Open AccessFeature PaperArticle

Tourism Risk Prediction and Influencing Factor Analysis on the Qinghai–Tibet Plateau Based on Interpretable Machine Learning

by Ziqiang Li, Jianchao Xi, Sui Ye and Zumilaiti Aihemaitijiang

ISPRS Int. J. Geo-Inf. 2026, 15(5), 220; https://doi.org/10.3390/ijgi15050220 - 20 May 2026

Abstract

Tourism safety in high altitude destinations is strongly affected by the combined effects of environmental constraints, tourism exposure, and safety support capacity. The Qinghai–Tibet Plateau (QTP), characterized by high altitude, complex terrain, sparse settlements, and limited emergency accessibility in remote areas, provides a [...] Read more.

Tourism safety in high altitude destinations is strongly affected by the combined effects of environmental constraints, tourism exposure, and safety support capacity. The Qinghai–Tibet Plateau (QTP), characterized by high altitude, complex terrain, sparse settlements, and limited emergency accessibility in remote areas, provides a representative case for tourism risk assessment in extreme plateau environments. To predict and interpret the spatial pattern of tourism risk on the QTP, this study constructs an assessment framework based on “Hazard–formative factors + Risk exposure + Safety security” and integrates XGBoost with SHAP interpretable machine learning. Eleven indicators representing environmental conditions, tourism exposure, and safety support capacity were used to model tourism risk at a 1 km × 1 km spatial resolution. The optimized XGBoost model achieved an AUC of 0.877, indicating good predictive performance. The results show that tourism risk on the QTP presents a spatial pattern of “high in the northwest and low in the southeast”. High risk and relatively high risk areas account for approximately 74.98% of the study area and are mainly distributed in remote hinterlands and northwestern plateau regions, whereas low risk areas are concentrated around southeastern river valleys, towns, mature scenic areas, and major transport corridors. SHAP analysis indicates that Distance to towns is the most important factor influencing predicted tourism risk, followed by Reception facility kernel density, Relief degree of land surface, and Scenic spot kernel density. Nonlinear and interaction analyses further suggest that remoteness, tourism facilities, terrain relief, and scenic area concentration jointly shape the predicted risk pattern. The findings provide spatial evidence for differentiated tourism risk management, including regular tourism development in relatively safe urban and scenic nodes, controlled management of medium risk tourism corridors, and stricter access management in remote high risk areas. Full article

► Show Figures

Figure 1

19 pages, 6663 KB

Open AccessArticle

Using a Visual Positioning System for a Geolocated Visualization of an Archaeological Site in Augmented Reality

by František Mužík and Lukáš Běloch

ISPRS Int. J. Geo-Inf. 2026, 15(5), 219; https://doi.org/10.3390/ijgi15050219 - 20 May 2026

Abstract

In recent years, augmented reality has become a popular method of spatial data visualization, both via the most popular and basic plane-based method and more advanced automatic positioning of visualizations based on predefined real-world locations. The aim of this study is to provide [...] Read more.

In recent years, augmented reality has become a popular method of spatial data visualization, both via the most popular and basic plane-based method and more advanced automatic positioning of visualizations based on predefined real-world locations. The aim of this study is to provide new insights into geolocated 3D visualizations in AR using a visual positioning system (VPS). VPS technology enables the creation of visualizations that can be displayed with high accuracy directly on a specific area of interest. This approach is especially well-suited to cultural heritage preservation, as it can be used to visualize destroyed buildings or archaeological sites. The result of the study is a mobile application created using the Unity game engine, which allows users to access AR visualizations as well as additional context in the form of pop-up texts or photographs. Thanks to the display of AR visualization directly at the chosen location, the user can better understand the context of the whole scene. This is because it is a more immersive experience than simply viewing a 3D model on a computer or mobile phone screen. Full article

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

► Show Figures

Figure 1

22 pages, 37242 KB

Open AccessArticle

Euclidean–Fractal Measures of Spatial–Temporal Urban Form and Growth with Data Fusion: The Case of Charlotte and Its Environs, USA

by Qiuxiao Chen, Yu Liu, Long Zhou, Yanguang Chen, Heng Chye Kiang, Xiuxiu Chen and Guoqiang Shen

ISPRS Int. J. Geo-Inf. 2026, 15(5), 218; https://doi.org/10.3390/ijgi15050218 - 19 May 2026

Abstract

This research presents a comprehensive spatial–temporal analysis of urban form and growth in Charlotte and Mecklenburg County, North Carolina, USA, from 1900 to 2017 at the land parcel level. Employing a data fusion framework, we integrate diverse datasets—including historical cadastral records, census data, [...] Read more.

This research presents a comprehensive spatial–temporal analysis of urban form and growth in Charlotte and Mecklenburg County, North Carolina, USA, from 1900 to 2017 at the land parcel level. Employing a data fusion framework, we integrate diverse datasets—including historical cadastral records, census data, remote sensing imagery, and infrastructure maps—to examine urban morphology through Euclidean and fractal geometries. Urban growth was reconstructed and visualized by decade and cumulatively, revealing dynamic patterns of expansion, densification, and fragmentation. Using scatterplot matrices and the Hausdorff box-counting algorithm, we quantified urban form across major land use types and temporal intervals. The fusion of socio-physical variables with mathematical functions enabled multi-scale modeling of urban transitions, aligning spatial, temporal, and thematic dimensions. Key findings include: (1) multidirectional spatial expansion resulting in a sprawling urban footprint at different rates over 117 years; (2) exponential growth between 1950 and 2000 with slower rates before and after manifesting a classic S-curve urban development by Northam; (3) a pivotal moment in 1993 when urbanized and rural lands reached parity, reflecting balanced urbanization in terms of population and land area for cities and rural areas for Mecklenburg; and (4) consistent quantitative relationships—linear, polynomial, exponential, logarithmic, and proportional—between urban form and growth metrics. This study’s novelty lies in its integrated spatial–temporal framework not only for combining both Euclidean and fractal geometric analyses with fused multi-source data to uncover the evolving structure of urban landscapes, but also for offering valuable insights into efficient land uses to assess equitable land and population dynamics, all aiming to achieve a good understanding of and sound policies for Charlotte, Mecklenburg and beyond. Full article

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

► Show Figures

Figure 1

16 pages, 2477 KB

Open AccessArticle

Addressing GeoAI Governance: An Automated Gatekeeper for Building Outlines in OpenStreetMap

by Lasith Niroshan and James D. Carswell

ISPRS Int. J. Geo-Inf. 2026, 15(5), 217; https://doi.org/10.3390/ijgi15050217 - 19 May 2026

Abstract

Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating ‘AI slop’, consisting of [...] Read more.

Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating ‘AI slop’, consisting of geometrically inconsistent/unreliable data, into the online map. While the OSM “Code of Conduct for Automated Edits” provides a policy framework for data ingestion, it lacks a machine-enforceable mechanism for real-time quality gating. This paper proposes a GeoAI-Gatekeeper to perform this task—an automated process that applies empirical Acceptable Quality Thresholds (AQT) to address the GeoAI data governance problem. Because the Gatekeeper utilizes an intrinsic, no-reference evaluation of geometric fidelity, it can assess incoming AI-generated data streams in real-time without requiring ground-truth benchmarks. Importantly, it focuses exclusively on the geometric validation of building footprints, acknowledging for now that semantic enrichment, such as tagging, remains a human-centric task. The presented GeoAI-Gatekeeper is a working prototype developed for a specific urban area, systematically triaging incoming AI-generated data into three tiers; Auto-Accept, Manual Review, and Reject. It provides a Web-GIS interface for Human-in-the-Loop (HITL) functionality to ensure the OSM community remains the final arbiter of acceptable data quality. Testing the Gatekeeper in Dublin (Ireland) demonstrates that our solution can auto-ingest 93.6% of features with a 14x reduction in human review effort while still adhering to OSM’s cartographic integrity standards. By implementing qualitative community guidelines into machine-enforceable thresholds, our approach introduces a viable methodology for next-generation hybrid VGI systems. Importantly, it ensures that the transition towards automated data ingestion reinforces, rather than undermines, the reliability of global crowd-source mapping datasets. Full article

(This article belongs to the Special Issue Testing the Quality of GeoAI-Generated Data for VGI Mapping)

► Show Figures

Figure 1

19 pages, 4868 KB

Open AccessArticle

Fifteen Years of Cleaner Air in New York City: Spatial Convergence, Childhood Asthma Burden, and the Equity Implications of Neighborhood-Scale Exposure Integration

by Hai Lan and Frances Currin-Brinkman

ISPRS Int. J. Geo-Inf. 2026, 15(5), 216; https://doi.org/10.3390/ijgi15050216 - 19 May 2026

Abstract

Translating fine-resolution air pollution surfaces into health equity assessments requires aggregating exposure to administrative units, yet the equity implications of this choice are rarely tested. This study links annual 300 m nitrogen dioxide (NO₂) surfaces from the New York City Community [...] Read more.

Translating fine-resolution air pollution surfaces into health equity assessments requires aggregating exposure to administrative units, yet the equity implications of this choice are rarely tested. This study links annual 300 m nitrogen dioxide (NO₂) surfaces from the New York City Community Air Survey (2009–2023) with childhood asthma emergency department (ED) visit rates across 42 neighborhoods, comparing area-weighted, population-weighted, and residential-weighted aggregation throughout. Strong spatial convergence was observed in both NO₂ and ED burden (Pearson correlations between 2009 baseline levels and Theil–Sen slopes of −0.96 and −0.95). Panel first-difference estimation yielded a significant within-neighborhood association between NO₂ decline and ED rate decline (coefficient 0.022, p-value below 0.05). The most deprived fifth of neighborhoods received 47% of the total avoided ED burden, four times the share of the least deprived fifth. However, NO₂ reductions were nearly equal across poverty quintiles. The pro-poor distribution of health benefits was driven by baseline health inequality, not by differential pollution reduction. The three aggregation methods produced near-identical results for all metrics because within-neighborhood exposure variability was uncorrelated with poverty (r = −0.14). In cities where baseline disease burden is concentrated in disadvantaged communities, broad-based air quality improvement may contribute to pro-poor health gains without targeted intervention. Full article

► Show Figures

Figure 1

32 pages, 3279 KB

Open AccessArticle

A 5D Orthogonal Decoupling Framework and 16-Bit State-Word-Driven Scheduling Method for 3D Building Models in WebGIS

by Tong Zhang, Yunfei Shi, Wenjie Jiang, Chunguang Lyu and Shuangshuang Shi

ISPRS Int. J. Geo-Inf. 2026, 15(5), 215; https://doi.org/10.3390/ijgi15050215 - 19 May 2026

Abstract

Large-scale WebGIS visualization of 3D building models is often constrained by large requested payloads, client-side memory pressure, and runtime state-parsing overhead. This study proposes a five-dimensional orthogonal decoupling framework and a 16-bit state-word-driven scheduling method for 3D building models. The Boundary-based Spatial Proxy–Geometric [...] Read more.

Large-scale WebGIS visualization of 3D building models is often constrained by large requested payloads, client-side memory pressure, and runtime state-parsing overhead. This study proposes a five-dimensional orthogonal decoupling framework and a 16-bit state-word-driven scheduling method for 3D building models. The Boundary-based Spatial Proxy–Geometric Detail–Component Complexity–Texture Appearance–Semantic Information (B-D-C-T-S) framework organizes model representations into five separately addressable and schedulable dimensions, covering spatial proxies, geometry, components, textures, and semantics. A compact 16-bit structured state word is used to represent runtime states and reduce dependence on repeated text-based state parsing, supporting fixed-offset bitwise decoding, exclusive-OR (XOR)-based differencing, constraint checking, and incremental updating. A centroid-assigned Home Tile strategy is further introduced to reduce redundant semantic payloads for cross-tile objects. The method was evaluated using a single-building BIM model and an urban-scale photogrammetric mesh dataset. Under the tested initial-view setting, staged decoupled loading reduced the first-screen requested payload by 93.1% compared with monolithic loading. State-word-based C-field extraction achieved an approximately 144-fold speedup over JSON deserialization and C-field lookup. The Home Tile strategy reduced the total semantic payload by 44.1% in the semantic-redundancy test. In the 1.12 GB first-screen memory test, state-word-driven D1 tile scheduling loaded only 22.7 MB of physical payload, with stable resident memory of approximately 88.1 MB. These results indicate that the proposed method supports object-level state representation, selective resource activation and scheduling, Home Tile semantic routing, incremental updating, and first-screen memory control within tiled Web3D pipelines. Full article

► Show Figures

Figure 1

26 pages, 19967 KB

Open AccessArticle

Structural Polarization and the Digital–Physical Misalignment: A Network Evolution Analysis of Citywalk in Internet-Famous Cities

by Yong Wang, Donghua Li, Wenyu Zhou, Linrong Fu, Lin Lu and Chenyang Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(5), 214; https://doi.org/10.3390/ijgi15050214 - 15 May 2026

Abstract

As a novel urban leisure activity, Citywalk is reshaping the spatial organization of urban tourism resources and spatial experience patterns. This phenomenon provides a crucial entry point for understanding new tourist–destination relationships from the perspective of spatial behavior. This paper takes Harbin, an [...] Read more.

As a novel urban leisure activity, Citywalk is reshaping the spatial organization of urban tourism resources and spatial experience patterns. This phenomenon provides a crucial entry point for understanding new tourist–destination relationships from the perspective of spatial behavior. This paper takes Harbin, an Internet-Famous City (IFC), as a case study and integrates multi-source data, including pedestrian trajectories, social media texts, and urban infrastructure. A cross-modal analytical framework for Citywalk networks is constructed to examine the structural evolution of Citywalk networks and the relationship between digital-space and physical-space in the context of IFCs. The results indicate that: (1) During its rise as an IFC, Harbin’s Citywalk network transformed from a single-core agglomeration structure to a multi-nodal radial structure, exhibiting a pattern of core reinforcement and outward expansion. (2) Online visibility was associated with the emergence of new nodes and network expansion, but a structural misalignment was observed between digital-space association and physical-space linkage. (3) Emotional differentiation among newly visible nodes further reflected the uneven development of the Citywalk network, while concentrated digital attention was accompanied by persistent structural imbalance. This study highlights the digital–physical misalignment in urban tourism networks, suggests the important role of social media in shaping tourists’ route imagination and emotional evaluation, and provides references for the spatial optimization and sustainable management of urban tourism resources in the new development stage. Full article

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

► Show Figures

Figure 1

23 pages, 7048 KB

Open AccessArticle

Integrating the Oasis Cooling Effect into a Multidimensional STGP Feature Cube for Cropland Recognition in Xinjiang (2015–2024)

by Ruibo Wang, Weiming Cheng, Xinlong Feng and Wei Li

ISPRS Int. J. Geo-Inf. 2026, 15(5), 213; https://doi.org/10.3390/ijgi15050213 - 14 May 2026

Abstract

Monitoring cropland dynamics in arid regions is critical for balancing food security with water scarcity constraints. However, distinguishing fragmented agricultural oases from spectrally similar desert vegetation remains a persistent challenge due to spectral confusion and landscape heterogeneity. To address these challenges, this study [...] Read more.

Monitoring cropland dynamics in arid regions is critical for balancing food security with water scarcity constraints. However, distinguishing fragmented agricultural oases from spectrally similar desert vegetation remains a persistent challenge due to spectral confusion and landscape heterogeneity. To address these challenges, this study developed the STGP-OCE feature cube on the Google Earth Engine platform (GEE) by integrating the Oasis Cooling Effect (OCE) into the commonly used STGP (Spectral, Textural, Geomorphic, and Phenological) feature space, coupled with the XGBoost ensemble model. Through ablation experiments and feature importance analysis, we quantified the feature construction mechanism for arid regions. Oasis Cooling Intensity emerged as the most influential variable (Gain score: 0.315), demonstrating that the thermal signature of continuous anthropogenic irrigation serves as a robust thermodynamic proxy to resolve the spectral ambiguity between crops and drought-tolerant desert vegetation. By hierarchically coupling this thermal indicator with textural features to suppress fragmentation noise, topographic constraints to filter non-arable terrain, and phenological trajectories, the STGP-OCE feature cube achieved an Overall Accuracy of 95.12% and a Precision of 94.95%, significantly outperforming models built on lower-dimensional cubes as well as existing global land cover products. We generated a 10 m annual cropland dataset for Xinjiang, China, revealing a substantial 32.9% expansion (19,360 km²) from 2015 to 2024, mainly occurring in vulnerable oasis–desert transition zones and coinciding with reported reclamation activities. These highlight the continuous agricultural encroachment into desert margins, while the proposed STGP-OCE cube provides a reliable methodology for high-precision cropland monitoring in arid regions. Full article

(This article belongs to the Topic Global Trends and Local Practices in Land Use and Territorial Spatial Planning: Driving Forces, Innovation Strategies, and Future Challenges)

► Show Figures

Figure 1

25 pages, 17422 KB

Open AccessArticle

Demystifying Geographic “Laws” for Soil Mapping via Interactive Geovisualization

by Guiming Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(5), 212; https://doi.org/10.3390/ijgi15050212 - 12 May 2026

Abstract

“Laws” of geography such as Tobler’s First Law (spatial autocorrelation) and Zhu’s Third Law (environmental similarity) offer fundamental principles for spatial prediction and mapping, yet their implications for digital soil mapping (DSM) are often opaque because the underlying principles and mechanisms of DSM [...] Read more.

“Laws” of geography such as Tobler’s First Law (spatial autocorrelation) and Zhu’s Third Law (environmental similarity) offer fundamental principles for spatial prediction and mapping, yet their implications for digital soil mapping (DSM) are often opaque because the underlying principles and mechanisms of DSM models are rarely inspectable in typical DSM workflows. This study presents an interactive geovisualization portal that demystifies Tobler’s Law, Zhu’s Law, and a combined formulation in spatial prediction processes, using soil organic matter (SOM) concentration prediction in Xuancheng, China, as a case study. The portal integrates multiple DSM frameworks that operationalize two geographic laws—inverse distance weighting (IDW), individual predictive soil mapping (iPSM), an iPSM-IDW hybrid, ordinary kriging (OK), and regression kriging (RK)—and couples them with user-configurable parameters such as neighborhood size, distance-decay factor, and variogram model. The portal provides coordinated, interactive views that link SOM predictions to dynamic map and diagnostic statistical charts for explaining location-level predictions, visualizing the manifestation of geographic laws in constructing local predictions, examining weight allocation patterns, and assessing overall prediction accuracy. Additionally, a built-in sensitivity analysis enables users to investigate and understand the effects of varying the geographic law, modeling framework, and modeling parameters on prediction results. This geovisualization portal advances interpretable DSM by rendering its underlying geographic principles, model mechanics, and parameter influences visually inspectable. Full article

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

► 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