ISPRS International Journal of Geo-Information

51 pages, 9273 KB

Open AccessArticle

Sustainable Urban Forms and Climate Adaptation Policy: A Sparsity-Responsiveness Framework Based on Chinese Cities

by Zhihan Zhang, Junyan Yang, Xilong Chen, Zhixiang Lin, Yuyue Huang, Qingxin Yang and Huaxing Sheng

ISPRS Int. J. Geo-Inf. 2026, 15(8), 347; https://doi.org/10.3390/ijgi15080347 (registering DOI) - 1 Aug 2026

Abstract

Sustainability is recognized as a key driver for the formation and evolution of sustainable urban forms, serving as a critical approach for guiding and assessing urban sustainability. In the context of global warming, climate adaptation has become a pressing concern in shaping sustainable [...] Read more.

Sustainability is recognized as a key driver for the formation and evolution of sustainable urban forms, serving as a critical approach for guiding and assessing urban sustainability. In the context of global warming, climate adaptation has become a pressing concern in shaping sustainable urban forms. However, the complex mechanisms linking sustainability, urban form, and climate adaptation are difficult to identify and track due to variations in data precision, research scales, and stakeholder needs. This study first re-views the relevant literature to clarify the current state and trends in this field. It then employs locally weighted regression to analyze the relationships between sustainability principles, urban form, and climate adaptation from 2005 to 2024. Based on the “mitigation-adaptation” framework, sparsity-responsiveness indicators are constructed to define four types of climate adaptation. These types are used to classify 31 representative cities in China. Considering the cities’ developmental stages, the study proposes design strategies that prioritize sustainability. Full article

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

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39 pages, 11124 KB

Open AccessArticle

Multi-Hazard Coastal Susceptibility Mapping Using Machine Learning and Deep Learning in Deltaic Louisiana

by Tanvir Hossain and Michael Leitner

ISPRS Int. J. Geo-Inf. 2026, 15(8), 346; https://doi.org/10.3390/ijgi15080346 (registering DOI) - 1 Aug 2026

Abstract

Compound coastal hazards such as flooding, land subsidence, storm surge, and salinity intrusion impose accelerating risks on deltaic communities. This study presents a unified multi-hazard susceptibility mapping framework for Terrebonne Parish, Louisiana, modeling all four hazards from a common 30 m predictor stack, [...] Read more.

Compound coastal hazards such as flooding, land subsidence, storm surge, and salinity intrusion impose accelerating risks on deltaic communities. This study presents a unified multi-hazard susceptibility mapping framework for Terrebonne Parish, Louisiana, modeling all four hazards from a common 30 m predictor stack, with per-hazard exclusion of label-related predictors. Eight Machine Learning and Deep Learning algorithms were benchmarked per hazard against an ensemble meta-learner. Generalizability was assessed under three designs of increasing spatial rigor: blocked holdout, interleaved block cross-validation, and a strict contiguous-zone design with a 5 km buffer. Best holdout F1-macro ranged from 0.644 (salinity) to 0.923 (flood). Interleaved-block cross-validation was statistically indistinguishable from holdout; only the buffered contiguous-zone design revealed genuine transfer limits, with F1-macro declining 12–54 percentage points by hazard. Ensemble stacking did not improve upon cross-validation-guided single-model selection despite roughly five times the training cost. Salinity labels were derived from 21 kriged monitoring stations (RMSE = 3.40 PSU; R² = 0.82). A composite Multi-Hazard Susceptibility Index (mean = 0.675 parish-wide; 0.674 land-masked) identifies southern coastal Terrebonne as the priority zone for risk reduction, robust to reweighting of any single hazard. To our knowledge, this is the first framework to jointly map these four hazards while quantifying how validation design governs apparent model transferability. Full article

25 pages, 12836 KB

Open AccessArticle

Dynamic Supply–Demand Matching and Spatial Mismatch Diagnosis of Emergency Beds in Designated Hospitals During Public Health Emergencies: A SEIQRDP-SG and 3SFCA-SMI Framework

by Ying Zhong, Sheng Jiao, Qingqing Zhang and Yizhe Ying

ISPRS Int. J. Geo-Inf. 2026, 15(8), 345; https://doi.org/10.3390/ijgi15080345 (registering DOI) - 1 Aug 2026

Abstract

In the context of public health emergencies (PHEs), conventional static indicators are insufficient for capturing the dynamic supply–demand relationship of emergency medical care. Grounded in adaptive cycle theory, this study proposes a integrated analytical framework that sequentially integrates supply baseline identification, disturbance impact [...] Read more.

In the context of public health emergencies (PHEs), conventional static indicators are insufficient for capturing the dynamic supply–demand relationship of emergency medical care. Grounded in adaptive cycle theory, this study proposes a integrated analytical framework that sequentially integrates supply baseline identification, disturbance impact simulation, mismatch diagnosis, and zoning-based response optimization. Taking 475 communities and 18 major designated hospitals in the Changsha metropolitan area as the empirical case, we integrate AHP-CRITIC evaluation, the SEIQRDP-SG model, and the 3SFCA-SMI method to identify the spatial mismatch of emergency-bed supply and demand and to delineate planning response zones. The results show that: (1) emergency-bed supply exhibits marked agglomeration and quasi-Pareto polarization, with the top three districts accounting for 75.83% of effective emergency beds; (2) under the core scenario of R₀ = 5 with moderate intervention, peak bed demand in the metropolitan area reaches approximately 7835 beds around day 21, with high-demand communities emerging in high-density and high-mobility areas; and (3) although the overall supply–demand ratio is 1.34, 78% of communities cannot reach any designated hospital within 15 min, and the supply–demand pattern forms a compound spatial structure characterized by “central carrying, transitional mismatch, and peripheral weakness”. These findings indicate that the primary constraint on emergency medical resilience lies not in aggregate bed shortages alone, but in structural spatial mismatch jointly shaped by effective supply, dynamic demand, and transfer-time constraints. This study extends emergency medical facility evaluation from static assessment to dynamic matching and provides evidence for the layout of resilient “dual-use” medical facilities for routine and emergency conditions. Full article

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

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18 pages, 7463 KB

Open AccessArticle

From Geodata to Immersive Heritage Experiences: A Virtual Reality Case Study in Gorzów Wielkopolski, Poland

by Natalia Wicińska, Beata Medyńska-Gulij, Łukasz Halik and Anna Markowska

ISPRS Int. J. Geo-Inf. 2026, 15(8), 344; https://doi.org/10.3390/ijgi15080344 (registering DOI) - 1 Aug 2026

Abstract

Virtual reality and geovisualization offer new ways to present, study, and experience geographic space, including urban cultural heritage. This article presents the process of creating a gamified VR application focused on selected preserved monuments in the centre of Gorzów Wielkopolski, Poland, a city [...] Read more.

Virtual reality and geovisualization offer new ways to present, study, and experience geographic space, including urban cultural heritage. This article presents the process of creating a gamified VR application focused on selected preserved monuments in the centre of Gorzów Wielkopolski, Poland, a city whose historic fabric was strongly affected by World War II. The aim of the project was not only to introduce users to the city’s cultural heritage, but also to encourage greater interest in, respect for, and appreciation of that heritage. The work was divided into four stages: conceptual design, data preparation, implementation, and publication/evaluation. The application was developed using geospatial data, orthophotography, LoD1 building models, field photographs, archival postcards, manual 3D modelling, interface design, and implementation in Unity. The final VR environment allows users to explore part of the city, view information about monuments, match historical postcards with buildings, receive feedback, collect points, and move between stations. The application was additionally evaluated through an online questionnaire completed by 20 students. The results indicated a generally positive perception of its educational and heritage-communication potential, while the realism of the vegetation appears to be an area that could benefit from further improvement. The case study shows that immersive geovisualization can support spatial understanding, engagement, and heritage communication, and presents a workflow that may be adapted for similar cultural heritage projects. Full article

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27 pages, 46923 KB

Open AccessArticle

Spatio-Temporal Dynamics of Bicycle Accidents in the Lisbon Metropolitan Area: An Integrated Emerging Hotspot Analysis

by Jonathan Sandoval and Bertha Santos

ISPRS Int. J. Geo-Inf. 2026, 15(8), 343; https://doi.org/10.3390/ijgi15080343 - 28 Jul 2026

Abstract

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine [...] Read more.

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine the evolution of reported bicycle–vehicle injury accidents in the Lisbon Metropolitan Area (LMA). The framework combines Geographic Information Systems (GIS)-based spatial statistics with Emerging Hotspot Analysis (EHA) to identify and track changes in accident clustering over time, across pre-, during-, and post-COVID-19 containment periods. This study contributes by applying Emerging Hotspot Analysis to bicycle accident data, an approach still largely unexplored, and by proposing a sequential and integrated framework that links traditional spatial analysis methods with dynamic hotspot detection and machine learning techniques, enabling a shift from static pattern identification to enhanced interpretation of evolving accident occurrence patterns and hotspot dynamics. Results reveal evidence of spatial consolidation and changing hotspot distributions over time, with emerging hotspots increasingly located in suburban transition zones and at the edges of existing cycling infrastructure. These patterns may reflect changes in mobility demand and infrastructure provision, although the absence of exposure data prevents a direct assessment of this relationship. Complementary analysis using forest-based machine learning models identifies key factors associated with hotspot formation and accident severity, including crash type, temporal patterns (e.g., day of the week), and environmental conditions such as slope and lighting. These findings highlight the value of combining spatio-temporal analysis with predictive modelling to support data-driven urban planning and targeted safety interventions. Lisbon provides a relevant case study for cities undergoing similar transitions toward sustainable transport systems. Full article

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26 pages, 20725 KB

Open AccessArticle

Channel Attention-Based Multi-Domain Feature Alignment for Moving Vehicle Detection in Satellite Videos Toward Smart Urban Planning

by Ning Zhao, Xiao Wang, Xiaopeng Zhang, Jun Shi, Zhiguo Jiang and Haopeng Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(8), 342; https://doi.org/10.3390/ijgi15080342 - 26 Jul 2026

Abstract

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is [...] Read more.

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article

(This article belongs to the Special Issue Novel Theories and Applications on Geo-Spatial Databases, Models and AI in Urban Science, Planning, Development and Governance)

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23 pages, 1352 KB

Open AccessArticle

Taking Negative Spatial Autocorrelation Seriously: Deconstructing Fifty Years of Pro-Positive Bias in Spatial Statistics

by Daniel A. Griffith

ISPRS Int. J. Geo-Inf. 2026, 15(8), 341; https://doi.org/10.3390/ijgi15080341 - 25 Jul 2026

Abstract

This essay addresses the persistent understudying of negative spatial autocorrelation (SA) in spatial analysis, extending existing arguments that principal interpretations and geographic models have unduly prioritized positive dependence/correlation. It further reframes SA to explicitly recognize its negative nature as a legitimate and frequently [...] Read more.

This essay addresses the persistent understudying of negative spatial autocorrelation (SA) in spatial analysis, extending existing arguments that principal interpretations and geographic models have unduly prioritized positive dependence/correlation. It further reframes SA to explicitly recognize its negative nature as a legitimate and frequently occurring georeferenced data property. Through empirical examples, scale-sensitive analysis, and a synthesized typology of negative SA facets, this paper advances a more balanced spatial dependence/correlation understanding. It challenges the superfluous model-induced artifact view about negative SA, demonstrating its real-world empirical and substantive emergence through heretofore unexamined empirical conditional territorial reshuffling and spatial competition, using historical Texas county subdivisions as a concrete exemplar. It translates the eight established canonical positive-SA-oriented interpretations to more negative-SA-friendly multivariate geospatial contexts, highlighting competing or compensatory dynamics among attributes. This paper emphasizes that negative SA can be a localized, scale-sensitive property, one often masked by dominant positive SA in any of its geographic extents, and encourages permutation/randomization-based inference to diagnose it in negative-SA-governed geographic landscapes. In addition, this paper explores the symmetry between positive and negative SA clusters across global, regional, and local scales, and examines negative SA roles with regard to spatial outliers, territorial management, and random contrasts, such as those earmarking early diffusion processes. This paper concludes that negative SA captures inverse locational relationships, is inherently delicate and transient, and responds sensitively to changes in geographic scale, resolution, and data aggregation, meriting more candid consideration in spatial statistics/econometric modeling. Full article

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22 pages, 8981 KB

Open AccessArticle

A Spatial Semantic-Guided Online Crime Spatiotemporal Prediction Model

by Huan Jiang, Miaoxuan Shan, Licheng Hao, Jinguang Sui and Peng Chen

ISPRS Int. J. Geo-Inf. 2026, 15(8), 340; https://doi.org/10.3390/ijgi15080340 - 24 Jul 2026

Abstract

Accurate crime spatiotemporal prediction is crucial for crime prevention. However, crime occurrences are influenced by diverse and interacting social factors, resulting in dynamically evolving distributions with non-stationarity and spatial heterogeneity. Most existing methods focus on data preprocessing or architectural enhancements and remain offline [...] Read more.

Accurate crime spatiotemporal prediction is crucial for crime prevention. However, crime occurrences are influenced by diverse and interacting social factors, resulting in dynamically evolving distributions with non-stationarity and spatial heterogeneity. Most existing methods focus on data preprocessing or architectural enhancements and remain offline models, which limits their generalization capability. To address these challenges, we propose a novel spatial semantic-guided online learning framework. Specifically, we first compute the spatial semantic similarity between urban regions using points of interest. Based on this, we then introduce a contrastive learning objective guided by this similarity during training. This design aims to enhance the model’s ability to capture both the similarities and discrepancies among regions. During the prediction process, an iterative online learning strategy is employed to adapt to dynamically changing crime patterns. By continuously fine-tuning the model with streaming data, the proposed framework improves robustness and generalization under non-stationary crime spatiotemporal distributions. Finally, extensive experiments on real-world crime datasets indicate the effectiveness and stability of our proposed approach. Full article

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18 pages, 2304 KB

Open AccessArticle

Exploring Effects of Boundaries on Path Integration—An Approach to Link Spatial Navigation Performance to Brain Activity Concepts

by Denise O’Meara, Julian Keil, Cara Oster, Dennis Edler, Annika Korte and Frank Dickmann

ISPRS Int. J. Geo-Inf. 2026, 15(8), 339; https://doi.org/10.3390/ijgi15080339 - 24 Jul 2026

Abstract

Digital maps increasingly replace paper maps because they are accessible, regularly updated, and customizable. Yet they often weaken spatial orientation skills and create technological dependency. One possible solution is supporting navigation without extra cognitive effort by designing maps that address spatially responsive brain [...] Read more.

Digital maps increasingly replace paper maps because they are accessible, regularly updated, and customizable. Yet they often weaken spatial orientation skills and create technological dependency. One possible solution is supporting navigation without extra cognitive effort by designing maps that address spatially responsive brain cells. These cells are thought to be involved in the construction of an internal spatial representation. As animal studies have shown that the perception of environmental boundaries contributes to the stabilization of firing behavior, we examined boundary effects on path integration (PI) in screen-based and virtual reality (VR) settings. This allowed us to test whether the effect is robust across formats with different immersion and self-motion feedback. Participants completed PI tasks in a virtual arena while viewing a briefly displayed elevated line, wall, or no artificial boundary. It was expected that perceived boundaries would stabilize the activity of spatially responsive cells, such as grid cells. This is likely to contribute to a reduction in PI errors. Results showed a supportive tendency for the line condition, whereas the wall condition produced the highest errors. This pattern was comparable across both media. The findings suggest that the effect of spatial boundary cues depends on their design and perceptual properties. Full article

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31 pages, 2922 KB

Open AccessArticle

Geospatial Analysis of the Evolution of European Tourism in Spain Using Mobile Phone Data, the Space–Time Cube, and Emerging Hot Spot Analysis

by José Manuel Sánchez-Martín, Felipe Leco-Berrocal and Ana Beatriz Mateos-Rodriguez

ISPRS Int. J. Geo-Inf. 2026, 15(8), 338; https://doi.org/10.3390/ijgi15080338 - 24 Jul 2026

Abstract

In Spain, inbound European tourism exhibits marked territorial imbalances whose evolution is difficult to characterize using aggregate indicators. This study analyzes its spatiotemporal patterns at the municipal level between July 2019 and December 2025 based on experimental statistics from the National Institute of [...] Read more.

In Spain, inbound European tourism exhibits marked territorial imbalances whose evolution is difficult to characterize using aggregate indicators. This study analyzes its spatiotemporal patterns at the municipal level between July 2019 and December 2025 based on experimental statistics from the National Institute of Statistics compiled using mobile phone data. The objective is to identify processes of growth, persistence, and spatial intensification using a geospatial methodology based on the Space–Time Cube (STC) and Emerging Hot Spot Analysis (EHSA). The analysis covers the 1000 municipalities with the highest cumulative volume of European tourists, which account for most of the flows recorded during the period. The results show positive and statistically significant temporal trends in 911 municipalities, although the formation of persistent spatial clusters is considerably less widespread. EHSA identified 48 municipalities classified as hot spots when applying a one-month temporal neighborhood and 77 when using a three-month configuration. The two classifications showed an observed agreement of 96.0% and, for the four shared categories, a Cohen’s kappa coefficient of 0.660. The post-pandemic recovery in tourism did not, therefore, result in a homogeneous territorial consolidation of stable spatial patterns. We identify persistent hubs, areas undergoing intensification, and destinations with episodic behavior, located primarily in metropolitan, coastal, and island areas. The main contribution of the study lies in the development of a reproducible workflow based on the STC–EHSA integration, capable of distinguishing between temporal growth, persistence, intensification, and spatial intermittency, and of evaluating the stability of the results under different temporal neighborhood configurations. Full article

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35 pages, 30465 KB

Open AccessArticle

A Policy-Derived Multi-Tiered Analytical Framework for Assessing the Beautiful China Goals (BCGs) Implementation at the Urban Agglomeration Scale

by Yuxuan Wang, Ze Tian, Xiaodong Jing and Mengyao Li

ISPRS Int. J. Geo-Inf. 2026, 15(7), 337; https://doi.org/10.3390/ijgi15070337 - 22 Jul 2026

Abstract

To advance environmental sustainability, China proposed the Beautiful China Goals (BCGs) as its localized strategy, with urban agglomerations serving as the key implementation scale. To address the limitations of difficulty in identifying key tasks and insufficient regional applicability, this study develops a multi-goal [...] Read more.

To advance environmental sustainability, China proposed the Beautiful China Goals (BCGs) as its localized strategy, with urban agglomerations serving as the key implementation scale. To address the limitations of difficulty in identifying key tasks and insufficient regional applicability, this study develops a multi-goal evaluation system comprising 21 goals and 52 indicators rooted in the policy framework. Methodologically, a three-tiered assessment framework—goal, city, and region—is constructed for urban agglomerations, integrating spatial-temporal analysis, city-level two-dimensional diagnostics, and regional synergy quantification. The framework is applied to the Yangtze River Delta Urban Agglomeration (YRDUA), a national-level pilot area for the BCGs, over the period 2015–2023. Results indicate that: (1) progress toward the BCGs in the YRDUA increased by 5.7%, but full achievement by 2035 remains unlikely. Significant structural imbalances exist among the 21 goals, with infrastructure-related goals scoring higher than those related to institutional development, innovation, and carbon neutrality. Spatially, BCGs’ performance follows a “high southeast, low northwest” pattern, although distribution varied by goal, and regional equity has improved. (2) Fewer than half of the 41 cities had achieved “double high” states in both development magnitude and evenness by 2023, with cities following four distinct development pathways that reflect differing priorities and strategies for goal attainment. (3) Intercity cooperation in advancing the BCGs remains limited. Synergistic effects are relatively stronger for green production goals but weaker for ecological, technological, and institutional goals, with Ningbo, Suzhou, and Shaoxing emerging as key contributors to regional synergy. This framework offers a replicable tool for regional environmental planning and provides evidence for BCGs implementation strategies in China and beyond. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—3rd Edition)

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16 pages, 1727 KB

Open AccessArticle

The Moderating Role of Street-View Greenery in the Relationship Between Mental Health and Life Satisfaction: An Exploratory Case Study Across Contrasting Community Contexts in Korea

by Yoohyung Joo, Jaeyoung Jung, Jiwan Hong, Sangyoon Park, Jaelim Cho, Juyeon Ko, Changsoo Kim and Joon Heo

ISPRS Int. J. Geo-Inf. 2026, 15(7), 336; https://doi.org/10.3390/ijgi15070336 - 22 Jul 2026

Abstract

Growing evidence suggests that urban greenery is associated with improved mental health, yet how eye-level exposure functions within specific socio-environmental contexts remains underexplored. This study presents an exploratory case study utilizing semantic segmentation of street-view imagery to quantify eye-level greenery (“open greenery”) across [...] Read more.

Growing evidence suggests that urban greenery is associated with improved mental health, yet how eye-level exposure functions within specific socio-environmental contexts remains underexplored. This study presents an exploratory case study utilizing semantic segmentation of street-view imagery to quantify eye-level greenery (“open greenery”) across two contrasting community contexts: a densely developed area (Region 1) and a less developed area (Region 2) in Korea. Using interaction models reinforced by 5000-iteration bootstrap analyses, we identified the moderating role of greenery in the relationship between mental health (depression and cognitive function) and life satisfaction. Our findings indicate that the psychological benefits of greenery are highly contingent upon the interplay between individual vulnerability and regional context. Specifically, in Region 1, greenery moderated well-being for the low-cognitive function subgroup, while in Region 2, the moderating effect was most pronounced among individuals with depressive symptoms. Despite the inherent limitations of small subgroup samples, the stability of these patterns across repeated bootstrap iterations highlights meaningful “spatial intersections” where greenery plays a role in shaping psychological well-being. By adopting a case-centric approach, this study highlights that the benefits of street-view greenery are not uniform but context-dependent. These results underscore the necessity of context-aware green infrastructure strategies tailored to the specific environmental needs of vulnerable populations in diverse community settings. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research, 2nd Edition)

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23 pages, 7971 KB

Open AccessArticle

Geo-InkGAN: An Adaptive Generative Framework for Topographically Faithful Ink-Wash Style Transfer in Terrain Mapping

by Songyuan Gao and Daping Xi

ISPRS Int. J. Geo-Inf. 2026, 15(7), 335; https://doi.org/10.3390/ijgi15070335 - 21 Jul 2026

Abstract

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic [...] Read more.

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic constraints. To overcome this limitation, we propose Geo-InkGAN, a geo-heuristic framework that integrates geographic principles with generative processes to achieve high-fidelity ink-wash style synthesis. A key component of our approach is an adaptive optimization strategy grounded in the Slope Standard Deviation (SSD). By establishing a quantitative relationship between geomorphological entropy and the cycle-consistency loss weight (

λ_{c y c}

), we effectively address the Pareto trade-off between geomorphic accuracy and esthetic representation. Our results indicate that alluvial plains benefit from low-intensity constraints to facilitate fluid ink diffusion, whereas rugged terrains require high-intensity constraints to maintain the integrity of the topological framework. Additionally, the HCEG-SE mechanism (Hillshade-Contour Edge-Guided Stroke Enhancement) narrows the semantic divide between terrain skeletons and artistic textures by combining multi-directional non-photorealistic rendering with precise edge extraction techniques. Evaluated across five geomorphologically diverse regions—from karst towers to loess plateaus—Geo-InkGAN demonstrably surpasses existing benchmarks in Geomorphological Structure Correlation (GSC). This geomorphology-aware approach advances the scientific rigor of AI-driven cartography and offers a refined methodology for the cultural representation of digital twin landscapes. Full article

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22 pages, 16612 KB

Open AccessArticle

Paleoenvironmental Changes and Human Adaptation: A Multidisciplinary Investigation of Site Abandonment at Qusayrat Aad Archaeological Site, Central Saudi Arabia

by Mohamed Metwaly and Abdullah Alshami

ISPRS Int. J. Geo-Inf. 2026, 15(7), 334; https://doi.org/10.3390/ijgi15070334 - 21 Jul 2026

Abstract

This study examines the critical relationship between geoenvironmental changes and human occupation patterns in the central Arabian Peninsula, focusing on the Al-Aflaj region. By integrating geospatial modeling with preliminary archeological excavation results that indicated the site is dated to 5th century BCE to [...] Read more.

This study examines the critical relationship between geoenvironmental changes and human occupation patterns in the central Arabian Peninsula, focusing on the Al-Aflaj region. By integrating geospatial modeling with preliminary archeological excavation results that indicated the site is dated to 5th century BCE to 6th century CE, we evaluate how climatic stressors dictated human adaptation and eventual site abandonment. Late Quaternary climatic fluctuations, particularly the Early Holocene pluvial phases, initially created favorable conditions for settlement through sustained freshwater resources. Subsequent aridification triggered significant migration and settlement contraction, demonstrating a high degree of human resilience. The site of Qusayrat Aad serves as a compelling case study of sophisticated adaptation, evidenced by mudbrick architecture and advanced irrigation systems. Integration of geological, topographic, and paleoclimatic factors indicates that the porous sedimentary layers of the Heet and Al Biyadh Formations were essential for groundwater recharge and spring formation. The geospatial analysis reveals that the settlement was strategically localized on a stable surface with a mean slope of 1.51°. Furthermore, the Topographic Wetness Index (TWI) identifies the site as a significant hydrological function, with a mean value of 8.79 (reaching a 90th percentile of 12.37), which is markedly higher than the regional average of 7.96. These quantitative findings establish a causal necessity for the site’s advanced subsurface canal systems as an engineered response to minimize evaporative losses in high-potential moisture zones. Ultimately, the correlation between the archeological record and climatic proxies suggests that the intensification of late Holocene aridification depleted these specific water resources beyond adaptive capacity, serving as the primary driver for the site’s abandonment and the migration of populations toward the eastern and southern parts of the Peninsula. Full article

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

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20 pages, 1231 KB

Open AccessArticle

Meta-FedGeo: Adaptive Federated Learning with Spatiotemporal Transformers for Urban GeoAI in Smart Cities

by Rosny Jean and Stabak Roy

ISPRS Int. J. Geo-Inf. 2026, 15(7), 333; https://doi.org/10.3390/ijgi15070333 - 20 Jul 2026

Abstract

This study introduces Meta-FedGeo, a federated learning framework that integrates meta-learning and spatiotemporal transformers to address key challenges in urban GeoAI for smart cities. Data streams in smart city environments are inherently non-stationary and heterogeneous, limiting the adaptability of traditional federated learning approaches. [...] Read more.

This study introduces Meta-FedGeo, a federated learning framework that integrates meta-learning and spatiotemporal transformers to address key challenges in urban GeoAI for smart cities. Data streams in smart city environments are inherently non-stationary and heterogeneous, limiting the adaptability of traditional federated learning approaches. Meta-FedGeo overcomes these limitations through a hybrid centralised–decentralised architecture that pre-trains a global model using meta-learning to capture cross-city spatiotemporal patterns and dynamically refines it through federated updates. The framework incorporates performance-aware client selection and temporally weighted aggregation to enhance model robustness and convergence. To model complex urban dynamics, the proposed system employs a Spatio-Temporal Transformer (ST-Transformer). In addition, an Uncertainty-Calibrated Decision Engine (UCDE) is introduced to align model predictions with accessibility and urban planning constraints. Unlike static federated methods, Meta-FedGeo can dynamically identify and filter malicious or low-quality clients using local validation loss, while Shapley value-based mechanisms support efficient and fair knowledge transfer across distributed nodes. To clarify the scope of the present study, Meta-FedGeo is reported as a partially implemented research prototype: the ST-Transformer backbone, the meta-learning initialisation, the validation-loss-based client filtering and the temporal-weighted aggregation were implemented and evaluated on partitioned real-world datasets, whereas the Shapley-value contribution assessment, the Lightweight Data Harmonisers (LDHs) and the UCDE are presented as architectural components with proof-of-concept implementations whose full empirical validation is identified as future work. The framework is designed for seamless integration with existing urban infrastructure without requiring major modifications. Experimental results using real-world urban datasets partitioned into non-IID federated clients indicate improved predictive performance and faster convergence relative to the federated baselines considered here. Overall, Meta-FedGeo advances GeoAI toward scalable, adaptive, and practical applications in smart city environments. Full article

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20 pages, 3589 KB

Open AccessArticle

Research on Incremental Geometrical Reconstruction Method of Building Structures Based on Point Clouds

by Xian Cao, Changyu Qian, Hanqiang Deng, Xiangrong Ni, Hao Chen, Lun Zhang and Jian Huang

ISPRS Int. J. Geo-Inf. 2026, 15(7), 332; https://doi.org/10.3390/ijgi15070332 - 20 Jul 2026

Abstract

With the development of intelligent unmanned systems, it is important for indoor mobile mapping and structural perception to reconstruct building structures in a timely manner from sequential LiDAR point clouds. However, many existing reconstruction methods rely on complete or accumulated point clouds, making [...] Read more.

With the development of intelligent unmanned systems, it is important for indoor mobile mapping and structural perception to reconstruct building structures in a timely manner from sequential LiDAR point clouds. However, many existing reconstruction methods rely on complete or accumulated point clouds, making them less suitable for partial observations, occlusions, and continuous updates. This paper proposes an incremental geometric reconstruction framework for building structures based on LiDAR point clouds. The method combines temporal state inheritance and orthogonal projection to transform 3D point-cloud processing into 2D plane-based contour updating. A transmissive relationship-based hole detection strategy is introduced to preserve real openings such as doors and windows while completing partially unobserved regions. Simulation and real-world experiments show that the proposed method can recover major planar building structures. In the simulation scene, the proposed method achieves a CD-L1 of 0.088 m, a CD-L2 of 0.007 m², and an F1-score of 0.901, with an average single-frame processing time of 1.02 s. The experimental results indicate that the proposed method provides a compact and interpretable plane-based structural representation for near-real-time incremental reconstruction of building structures from sequential LiDAR point clouds. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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18 pages, 7251 KB

Open AccessArticle

A GIS-Based Analysis of the Spatiotemporal Evolution and Driving Mechanisms of Rural Settlements in an Ethnic Minority Region: Evidence from Fuxin Mongolian Autonomous County, China

by Xinshuang Zhang, Sihan Li and Jun Yang

ISPRS Int. J. Geo-Inf. 2026, 15(7), 331; https://doi.org/10.3390/ijgi15070331 - 18 Jul 2026

Abstract

Understanding the spatiotemporal evolution of rural settlements in ethnic minority regions is essential for coordinated rural development, cultural landscape conservation, and rural revitalization. Taking Fuxin Mongolian Autonomous County in Northeast China as a case study, this study examined rural settlement patterns and their [...] Read more.

Understanding the spatiotemporal evolution of rural settlements in ethnic minority regions is essential for coordinated rural development, cultural landscape conservation, and rural revitalization. Taking Fuxin Mongolian Autonomous County in Northeast China as a case study, this study examined rural settlement patterns and their driving mechanisms from 2000 to 2024 using GIS-based spatial analysis, landscape pattern metrics, and the optimal parameter-based geographical detector (OPGD) model. A multidimensional indicator system was constructed from four dimensions: natural environment, production-resource environment, ethnic–cultural environment, and socioeconomic environment. The results show that rural settlements remained significantly clustered, although clustering gradually weakened, with average nearest-neighbor ratios increasing from 0.7729 in 2000 to 0.8370 in 2024. High agglomeration was mainly concentrated in the southern and southeastern areas, whereas low agglomeration occurred in the western and northwestern areas. Annual average temperature had the strongest explanatory power (q = 0.2492), followed by road network density (q = 0.1786) and elevation (q = 0.1716), indicating that thermal conditions, transportation accessibility, and topographic constraints were dominant drivers. All two-factor interactions showed enhancement effects, suggesting a coupled rather than single-factor mechanism. Ethnic–cultural variables had relatively lower q-values but remain important for interpreting cultural continuity, heritage conservation value, and differentiated rural development. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—3rd Edition)

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34 pages, 6573 KB

Open AccessArticle

Exploratory Vulnerability Assessment of the Urban Ecological Security Pattern in Bogotá: Static and Dynamic Attack Simulations and Cascading-Failure Modelling in a Global South City

by Jose David Martinez Otalora, Jie Shen and Anyela Piedad Rojas Celis

ISPRS Int. J. Geo-Inf. 2026, 15(7), 330; https://doi.org/10.3390/ijgi15070330 - 18 Jul 2026

Abstract

The Ecological Security Pattern (ESP), composed of ecological sources, resistance surfaces, and corridors, provides a spatial basis for mitigating urban landscape fragmentation and sustaining ecological security. However, most urban ESP studies have focused on its spatial delimitation, while the assessment of network vulnerability [...] Read more.

The Ecological Security Pattern (ESP), composed of ecological sources, resistance surfaces, and corridors, provides a spatial basis for mitigating urban landscape fragmentation and sustaining ecological security. However, most urban ESP studies have focused on its spatial delimitation, while the assessment of network vulnerability under disturbance remains limited. This study applies an integrated, exploratory, and model-based methodological framework that combines ESP mapping, ecological network analysis, attack simulation, and load–capacity cascading failure modelling to generate simulated indications of the potential vulnerability of the urban ecological network of Bogota. The results identified 58 ecological sources with a combined area of 123.02 km² (19.58% of the study area) and 107 active corridors. In the simulations, sources N540, N433, and N847 showed the highest topological relevance, whereas sources N933, N337, and N847 concentrated the greatest functional importance. In the disturbance simulations, the network showed greater relative robustness to random removals; in contrast, degree- and betweenness-targeted removals produced a more accelerated loss of the connected component, whereas degree- and PageRank-based perturbations accelerated simulated functional degradation. In the dynamic scenario analyzed, the model organized the network into four risk levels and suggested indirect and multi-stage trajectories of simulated potential failure propagation. These findings contribute to the exploratory diagnosis of ESP functional vulnerability and provide preliminary, simulation-based spatial criteria to guide exploratory ecological prioritization analyses and scenario assessment in Bogotá and dense Global South metropolises. Full article

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

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24 pages, 20538 KB

Open AccessArticle

Application of an Interpretable Machine Learning Model to Archaeological Site Prediction: A Case Study of the Three Gorges Region in Chongqing

by Xiaoli Wang, Houxi Zou, Hao Chen and Yani Cao

ISPRS Int. J. Geo-Inf. 2026, 15(7), 329; https://doi.org/10.3390/ijgi15070329 - 18 Jul 2026

Abstract

The construction of the Archaeological Site Prediction Model (ASPM) and quantitative research on the driving mechanisms of influencing factors are key to better understanding the multidimensional interactions between ancient humans and the environment. They also constitute an essential technical approach for guiding field [...] Read more.

The construction of the Archaeological Site Prediction Model (ASPM) and quantitative research on the driving mechanisms of influencing factors are key to better understanding the multidimensional interactions between ancient humans and the environment. They also constitute an essential technical approach for guiding field archaeological survey and excavation. This study aims to develop a highly stable, accurate, and interpretable predictive model for archaeological sites in the Chongqing Three Gorges region (CQTGR). BP neural network prediction (BPNN) has been applied in various fields, but its random initial weights and thresholds often lead to suboptimal accuracy and weak interpretability. To address these issues, this study constructs BPNN optimized with a Bayesian algorithm to enhance its accuracy. Additionally, it integrates the SHAP model to quantitatively identify nonlinear interactions and threshold effects of influencing factors, thereby improving its interpretability. The results indicate that: (1) The BPNN-based prediction model outperforms other conventional models. After hyperparameter optimization using the Bayesian algorithm, the AUC (area under the ROC curve) on the test set increases by 0.0812, reaching a final value of 0.8815. This indicates that the optimization model is effective and that the model exhibits strong predictive capability. (2) Archaeological sites exhibit a tiered and linear corridor distribution pattern along the Yangtze River and its major tributaries. This pattern can be divided into five concentric tiers radiating outward from the core of the main river systems. High-probability zones are particularly clustered in the low-lying and flat river valleys. (3) The distribution of archaeological sites is comprehensively influenced by both the natural environment and human activities. Elevation and river systems are key driving factors, with NDVI and land use also exerting significant influence. The major factors demonstrate notable threshold and interaction effects. Areas where the interaction between factors exhibits positive enhancement are often the core areas of archaeological site distribution. This research not only provides a precise scientific basis for the preventive protection and monitoring of potential distribution areas of archaeological sites but also offers decision support for the spatial conservation planning of these sites. Full article

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