ISPRS International Journal of Geo-Information

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

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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

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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

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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

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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

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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

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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

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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

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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

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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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24 pages, 19595 KB

Open AccessArticle

Context-Aware Modeling of Morphology–Performance Associations and Cross-Temporal Generalization Using Variational Autoencoder Latent Representations

by Chengyu Sun, Xinru Wang and Yu Meng

ISPRS Int. J. Geo-Inf. 2026, 15(7), 328; https://doi.org/10.3390/ijgi15070328 - 17 Jul 2026

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Abstract

A better understanding of the associations between urban morphology and performance can support more evidence-based urban governance and design evaluation. However, conventional hand-crafted metrics may omit important configurational information, making it difficult to model morphology–performance relationships consistently across multiple performance domains and under [...] Read more.

A better understanding of the associations between urban morphology and performance can support more evidence-based urban governance and design evaluation. However, conventional hand-crafted metrics may omit important configurational information, making it difficult to model morphology–performance relationships consistently across multiple performance domains and under changing urban conditions. To address this issue, this study tests a modeling pathway based on variational autoencoder (VAE) latent representations of urban morphology, combined with neighborhood de-averaging to reduce the influence of locational context, and evaluates it through a four-phase design covering explanatory gain, cross-dimensional response, contextual-scale and spatial robustness, and temporal generalizability. Using Shanghai as the empirical case, the results show that the latent-representation modeling pathway consistently outperforms multiple hand-crafted metric-based baselines. This relative advantage remained evident across multiple contextual scales, under spatially grouped validation, and in temporal validation. The pathway enables complex urban morphology to enter multidimensional performance models through a unified representation and has practical application potential for data-driven urban evaluation and performance-oriented planning support. Full article

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24 pages, 9228 KB

Open AccessArticle

Short-Term Metro Passenger OD Demand Forecasting Based on Low-Rank Tensor Network Extended Kalman Filter

by Aijing Su, Bing Wu and Xiaoxing Fang

ISPRS Int. J. Geo-Inf. 2026, 15(7), 327; https://doi.org/10.3390/ijgi15070327 - 17 Jul 2026

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Abstract

Accurate short-term metro origin–destination (OD) demand forecasting is essential for intelligent passenger flow management and urban rail transit operation. However, forecasting large-scale metro OD demand remains challenging due to its high dimensionality, nonlinear spatiotemporal dependencies, and demand uncertainty. To address these challenges, this [...] Read more.

Accurate short-term metro origin–destination (OD) demand forecasting is essential for intelligent passenger flow management and urban rail transit operation. However, forecasting large-scale metro OD demand remains challenging due to its high dimensionality, nonlinear spatiotemporal dependencies, and demand uncertainty. To address these challenges, this paper proposes a Tensor Network Extended Kalman Filter (TNEKF) framework for short-term metro OD demand forecasting. First, metro OD demand is formulated as a nonlinear dynamic state-space prediction problem, where a multi-input multi-output Volterra model is adopted to characterize the nonlinear relationship between historical passenger demand and future OD flows. An Extended Kalman Filter (EKF) is then developed to recursively estimate the latent model parameters and continuously refine demand prediction using newly available observations. To improve computational efficiency for high-dimensional OD systems, both the latent state vector and covariance matrix are represented using low-rank tensor network structures, and all recursive filtering operations are implemented through tensor-network contractions without explicitly constructing large-scale matrices. Experiments on real-world smart-card data from the Hangzhou metro system demonstrate that the proposed method consistently outperforms ARIMA, conventional EKF, and several state-of-the-art spatiotemporal prediction models in terms of MAE, RMSE, and MAPE. Compared with the best-performing baseline of the whole-day scenario, the proposed method reduces MAE, RMSE, and MAPE by 30.2%, 9.8%, and 6.3%, respectively. Furthermore, the proposed framework exhibits strong robustness under disruption scenarios, demonstrating its effectiveness and scalability for large-scale metro OD demand forecasting. Full article

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32 pages, 1953 KB

Open AccessArticle

User Experience-Based Evaluation of Tactile Map Production Methods for Wayfinding Among People with Visual Impairments

by Ayça Eraslan, Ahmet Özgür Doğru and Nesibe Necla Uluğtekin

ISPRS Int. J. Geo-Inf. 2026, 15(7), 326; https://doi.org/10.3390/ijgi15070326 - 16 Jul 2026

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Abstract

Tactile maps play a critical role in supporting spatial learning and independent mobility for people with visual impairments, particularly in complex environments such as university campuses. This study evaluates the effects of two tactile map production methods, 3D printing and heat-sensitive embossed paper, [...] Read more.

Tactile maps play a critical role in supporting spatial learning and independent mobility for people with visual impairments, particularly in complex environments such as university campuses. This study evaluates the effects of two tactile map production methods, 3D printing and heat-sensitive embossed paper, through a user-centered mixed-methods experimental design. The research was conducted on the North Campus of Boğaziçi University and involved 15 university students with visual impairments. The experimental process consisted of two stages: (1) controlled tactile map perception and evaluation, and (2) real-world wayfinding and on-site navigation experience. Quantitative data were collected through structured questionnaires and analyzed using descriptive statistics, while qualitative data were obtained through observations, open-ended feedback, and thematic analysis. The findings suggest that tactile map production methods may influence tactile perception, mental map formation, and user confidence. Participants generally reported that 3D-printed maps provided clearer tactile spatial organization and supported mental mapping in complex environments, whereas embossed paper maps offered advantages in portability and rapid accessibility. Full article

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32 pages, 45084 KB

Open AccessArticle

A Multidimensional Spatial–Temporal and Econometric Framework for Pedestrian Safety and Injury Severity Analysis in Amman, Jordan

by Haitham A. Al Hasanat, Omar Alharasees, Lafee Alshamaileh and Rana Al-Matarneh

ISPRS Int. J. Geo-Inf. 2026, 15(7), 325; https://doi.org/10.3390/ijgi15070325 - 16 Jul 2026

Viewed by 739

Abstract

This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the [...] Read more.

This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the study utilizes radar graphs, Kernel Density Estimation (KDE), and DBSCAN cluster analysis to delineate high-risk zones and temporal peaks. Temporal findings indicate that Thursdays recorded the highest accident frequency (2382 cases), with peak occurrences between 17:00 and 23:00. Spatial clustering identified five significant high-risk zones, with Central Amman emerging as the primary critical area. The study’s novelty lies in being the first in the Jordanian context to bridge accident frequency with severity mechanisms by integrating advanced spatial clustering and KDE with a robust Ordered Logit Model. Severity analysis reveals that while 59.34% of incidents resulted in minimal injuries, fatalities accounted for 5.02%. The model demonstrates that injury outcomes are systematically associated with traffic dynamics and behavior rather than environmental factors. Speed-related driver error was identified as the strongest predictor of severe outcomes (OR = 81.3). Significant dependencies were confirmed between vehicle category and road type (χ² = 2182.20, p < 0.001), lighting and road surface (χ² = 76.21, p < 0.001), and vehicle type and lighting (χ² = 148.52, p < 0.001). The study proposes a multi-layered framework combining site-specific nodal improvements with corridor-level strategies to enhance urban safety in Amman City. Full article

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

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18 pages, 2704 KB

Open AccessArticle

A GIS Based Spatio-Temporal Analysis of Socioeconomic and Environmental Determinants of Child Malnutrition in Pakistan

by Muhammad Usman, Katarzyna Kopczewska and Mudassar Rashid

ISPRS Int. J. Geo-Inf. 2026, 15(7), 324; https://doi.org/10.3390/ijgi15070324 - 16 Jul 2026

Viewed by 335

Abstract

Child malnutrition remains a critical global health challenge, yet most existing studies rely on static risk estimates and overlook the spatial–temporal nature of environmental exposures and localized socioeconomic disparities. To address this gap, we integrated Earth observation-derived environmental indicators, geolocated conflict events, socioeconomic [...] Read more.

Child malnutrition remains a critical global health challenge, yet most existing studies rely on static risk estimates and overlook the spatial–temporal nature of environmental exposures and localized socioeconomic disparities. To address this gap, we integrated Earth observation-derived environmental indicators, geolocated conflict events, socioeconomic variables, and child health outcomes, and applied a Fixed Effects Two-Stage Least Squares Spatial Durbin Error Model (FE-2SLS-SDEM). We found distinct hotspots of joint vulnerability, where areas experiencing both high conflict intensity and recurrent droughts show significantly higher rates of childhood stunting. High conflict intensity, drought severity, diarrheal prevalence, and inadequate sanitation significantly increase stunting, while maternal and paternal education, improved sanitation, economic development (proxied by nighttime light intensity), and agricultural productivity reduce it. Among these determinants, female education demonstrated the most pronounced inverse relationship with childhood stunting. Additionally, exposure to both drought severity and high conflict intensity independently and in combination worsens childhood stunting not only within affected regions but also in nearby localities. Our results underscore the urgency of geographically targeted, multisectoral, and action-oriented policies aimed at strengthening community and health system capacities to mitigate the converging risks of climate change and conflict on child malnutrition. Full article

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

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16 pages, 281 KB

Open AccessArticle

Immersive Ocean: A Virtual Twin for Participatory Decision Support in Maritime Spatial Planning

by Xavier Fonseca, Carlos Pereira Santos, Kevin Hutchinson, Jens Hagen, Phil De Groot, Joey Relouw and Igor Mayer

ISPRS Int. J. Geo-Inf. 2026, 15(7), 323; https://doi.org/10.3390/ijgi15070323 - 16 Jul 2026

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Abstract

Current Digital Twins of the Ocean rely predominantly on two-dimensional geoportal interfaces that constrain the spatial comprehension of complex marine environments. This paper presents Immersive Ocean, a Virtual Twin platform developed under the EU-ILIAD Digital Twins of the Ocean initiative that procedurally transforms [...] Read more.

Current Digital Twins of the Ocean rely predominantly on two-dimensional geoportal interfaces that constrain the spatial comprehension of complex marine environments. This paper presents Immersive Ocean, a Virtual Twin platform developed under the EU-ILIAD Digital Twins of the Ocean initiative that procedurally transforms standardised European geospatial data (EMODnet, Copernicus Marine Service) into interactive three-dimensional maritime environments via Unreal Engine 5 (Epic Games, Cary, NC, USA). The platform supports both desktop and immersive virtual reality modes, enabling users to visualise and manipulate spatial scenarios involving offshore wind farms, shipping corridors, aquaculture installations, and species distributions. System performance testing confirmed stable rendering across PC and VR configurations (61 FPS and 42 FPS, respectively). A user evaluation with 31 participants across three hardware configurations revealed that core geovisualisation capabilities—ease of use, immersion, and procedural generation utility—remained robust, regardless of hardware, whilst feedback responsiveness (H = 5.99, p = 0.04995) and perceived realism (H = 6.31, p = 0.04258) were significantly affected. These findings inform deployment strategies for immersive geospatial tools: minimum specification systems preserve functional access, whilst recommended hardware enhances the perceptual fidelity critical for spatial decision support. This evaluation establishes usability among digitally proficient users; efficacy with domain stakeholders in authentic planning contexts remains for future work. Full article

21 pages, 5389 KB

Open AccessArticle

The Link Between Urban Resilience and Sustainable Development: Research Trends in Global Nature-Based Solutions Based on Bibliometric Analysis Using CiteSpace and VOSviewer

by Li Zhu, Meihua Song, Lien-Chieh Lee, Wei Zhou, Junjun Liu, Ting Wu and Xudong Yuan

ISPRS Int. J. Geo-Inf. 2026, 15(7), 322; https://doi.org/10.3390/ijgi15070322 - 16 Jul 2026

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Abstract

Rapid urbanization and climate change have intensified environmental pressures and social inequalities, making the integration of urban resilience and sustainable development a critical global challenge, with Nature-based Solutions (NbS) emerging as a promising pathway; however, the knowledge structure, collaboration patterns, and evolutionary trends [...] Read more.

Rapid urbanization and climate change have intensified environmental pressures and social inequalities, making the integration of urban resilience and sustainable development a critical global challenge, with Nature-based Solutions (NbS) emerging as a promising pathway; however, the knowledge structure, collaboration patterns, and evolutionary trends of NbS research remain fragmented and insufficiently understood. This study conducts a comprehensive bibliometric analysis of 1261 publications from the Web of Science Core Collection (2005–2025), employing tools including VOSviewer 1.6.20, CiteSpace 6.4.R1, and Bibliometrix 4.1.3 to map publication trends, collaboration networks, knowledge bases, and thematic evolution. The results reveal a rapid expansion of NbS research since 2013, characterized by strong interdisciplinarity and a multicentric yet uneven geographical distribution dominated by China, the United States, and Europe. Four major research clusters are identified, encompassing policy governance, environmental benefits, ecosystem services, and social equity, reflecting a shift from ecological performance to integrated socio-ecological frameworks. Additionally, thematic evolution indicates growing emphasis on governance mechanisms, public health, and environmental justice. Overall, NbS research is transitioning toward a multi-scale, multi-objective, and governance-oriented paradigm. These findings highlight the need for strengthened international collaboration, standardized evaluation frameworks, and inclusive policy design to enhance the effectiveness and global applicability of NbS in advancing urban sustainable 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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21 pages, 1222 KB

Open AccessArticle

Anchor-Guided Balanced Learning for Trajectory Representation

by Kaiyue Liu, Hang Zhou, Zhouzheng Xu, Bingyi Li, Yuxing Wu, Chaofan Fan, Junfang Gong and Shengwen Li

ISPRS Int. J. Geo-Inf. 2026, 15(7), 321; https://doi.org/10.3390/ijgi15070321 - 15 Jul 2026

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Abstract

Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads [...] Read more.

Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads to representations that overfit to frequent patterns, resulting in weak robustness and limited generalization to sparse or atypical trajectories. To address these issues, this paper presents a novel perspective, anchor-guided balanced learning, and instantiates it with a framework, AnchorTRL. AnchorTRL introduces anchors to proactively construct a balanced semantic space instead of passively fitting the empirical data distribution. Specifically, AnchorTRL designs a spatiotemporal anchor identification algorithm to recognize trajectory anchors that comprehensively cover the data manifold. And, it proposes a calculation method to measure all trajectories’ semantic similarity with anchors. Additionally, it develops an anchor-based balanced sampling strategy to mitigate the dominance of frequent patterns and steer the model towards learning a more balanced representation. Finally, it constructs a multi-task contrastive learning objective with adaptive constraints to enhance the aggregation of semantically similar trajectories. Experimental results show that AnchorTRL outperforms existing baseline methods in tasks such as travel time estimation and similar trajectory queries, demonstrating its effectiveness and robustness. This research provides methodological support for constructing more reliable trajectory representation learning models, and offers new insights for optimizing intelligent transportation applications under spatiotemporal biases. Full article

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22 pages, 44475 KB

Open AccessArticle

From the Sky to the Garden: A Top-Down and Bottom-Up Methodology for Estimating Population Trends in Port Moresby’s Informal Settlements

by Bradley Dare and Shimona Kealy

ISPRS Int. J. Geo-Inf. 2026, 15(7), 320; https://doi.org/10.3390/ijgi15070320 - 13 Jul 2026

Viewed by 329

Abstract

Papua New Guinea’s capital, Port Moresby, is growing rapidly, and housing development has not kept pace. Thousands arrive in the city each year from rural villages, and many come to live in the city’s sprawling urban squatter settlements. The National Capital District Commission [...] Read more.

Papua New Guinea’s capital, Port Moresby, is growing rapidly, and housing development has not kept pace. Thousands arrive in the city each year from rural villages, and many come to live in the city’s sprawling urban squatter settlements. The National Capital District Commission lacks reliable population data for these so-called “self-help” settlements, posing a challenge for urban planning and the allocation of scarce development resources in a fast-growing city. Utilizing publicly accessible geospatial data gathered from 18 years of aerial imagery (top-down) alongside insights from on-the-ground interviews (bottom-up), this project establishes a low-cost, mixed-methods approach for estimating population and change within a large informal settlement. The findings show how manual rooftop identification, combined with local qualitative validation, can produce robust settlement-level population estimates in data-scarce environments and illustrate context-specific limitations of automated Earth Observation-based population models in Melanesian cities. This approach is applied to 8-Mile: a well-established 2.5 km² settlement northeast of Port Moresby’s city center. Results demonstrate the practicality of this method and reveal that, despite evictions and commercial rezoning, the settlement is growing at close to double the national average and is currently home to at least 27,000 people. Overall, the study highlights the value of combining spatial analysis with community-level knowledge to support evidence-based urban governance in rapidly growing cities. Full article

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

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28 pages, 30478 KB

Open AccessArticle

GIS-Based Suitability Analysis of LPG Refill Stations Using Boolean and Hybrid Multi-Criteria Approaches: A Case Study of Nairobi, Kenya

by Dorothy Onchagwa and Felix Mutua

ISPRS Int. J. Geo-Inf. 2026, 15(7), 319; https://doi.org/10.3390/ijgi15070319 - 13 Jul 2026

Viewed by 437

Abstract

Rapid urbanization has increased demand for safe and reliable energy infrastructure, with Liquefied Petroleum Gas (LPG) emerging as an important clean cooking fuel. In Nairobi, Kenya, the siting of LPG refill stations is critical to minimizing safety risks and supporting sustainable urban development. [...] Read more.

Rapid urbanization has increased demand for safe and reliable energy infrastructure, with Liquefied Petroleum Gas (LPG) emerging as an important clean cooking fuel. In Nairobi, Kenya, the siting of LPG refill stations is critical to minimizing safety risks and supporting sustainable urban development. This study applied a GIS-based Multi-Criteria Decision Analysis (MCDA) framework to evaluate LPG station suitability by integrating land use, elevation, slope, geology, soil texture, and regulatory constraints. Two approaches were compared: a Boolean-only overlay model and a hybrid weighted overlay–Boolean model incorporating Analytic Hierarchy Process (AHP)-derived weights. The Boolean model produced an overly restrictive outcome, identifying no feasible locations under the combined exclusion criteria. In contrast, the hybrid model excluded 91.7% of the study area and identified 2779 potential candidate locations distributed across suitability classes. AHP results indicated that land-use compatibility and LPG proximity were the most influential criteria in determining suitability. Comparison with existing LPG stations revealed a spatial mismatch, with most facilities located in less suitable or unsuitable areas. The findings demonstrate that while Boolean approaches strictly enforce all constraints, hybrid GIS–MCDA models provide a more flexible and spatially differentiated basis for LPG infrastructure planning in rapidly growing urban environments. Full article

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34 pages, 28786 KB

Open AccessArticle

Block-Scale Mapping and Coupling Coordination Diagnosis of Multidimensional Urban Vitality Using Multi-Source Geospatial Big Data: A Case Study of Central Nanjing, China

by Youhui Xia, Xinyu Gao, Xiuxian Jiang, Jingyi Ren and Feng Wei

ISPRS Int. J. Geo-Inf. 2026, 15(7), 318; https://doi.org/10.3390/ijgi15070318 - 13 Jul 2026

Viewed by 405

Abstract

Urban vitality is a key indicator for characterizing the quality of urban space and the operational status of urban functions. However, existing studies still have limitations in multidimensional vitality measurement at the block scale, the representation of hierarchical differences in cultural facilities, and [...] Read more.

Urban vitality is a key indicator for characterizing the quality of urban space and the operational status of urban functions. However, existing studies still have limitations in multidimensional vitality measurement at the block scale, the representation of hierarchical differences in cultural facilities, and the coupling coordination diagnosis of multidimensional vitality. This study takes 2504 blocks in the central urban area of Nanjing as the basic analytical units and integrates multi-source geospatial data, including VIIRS nighttime light data, Baidu Huiyan population heat data, POIs, road networks, and water systems, to construct a three-dimensional urban vitality evaluation system encompassing economic, social, and cultural vitality. A Composite Nighttime Light Index (CNLI) is constructed by geometrically fusing VIIRS nighttime light data with the kernel density of industry- and consumption-related POIs to reduce the impact of the spatial generalization of nighttime lights on block-scale economic vitality measurement. Meanwhile, population heat data and cultural POIs are used to characterize social vitality and cultural resource supply, respectively, and PCA, a coupling coordination model, and spatial autocorrelation analysis are combined to identify the spatial structure of multidimensional vitality and the dominant factors of disorder. External reference variables are also introduced to conduct convergent validity verification. The results indicate that the comprehensive vitality of Nanjing’s central urban area exhibits a distinct “core agglomeration–multi-node diffusion” structure. High-vitality zones are primarily concentrated in Xinjiekou, Confucius Temple, Hunan Road–Zhongyang Road, Longjiang, and the Nanjing Olympic Sports Center, with localized vitality patches forming at peripheral commercial and transportation nodes. Both comprehensive vitality and coupling coordination degree exhibit significant positive spatial autocorrelation, with Moran’s I values of 0.8089 and 0.8372, respectively. The disorder types show distinct quantitative differences and spatial differentiation. Among these, blocks with lagging cultural vitality are the most numerous; peripheral new towns and newly developed residential areas are more prone to cultural vitality lag; areas surrounding scenic spots, universities, and large ecological spaces tend to exhibit economic vitality lag; and less developed peripheral blocks primarily exhibit comprehensive disorder. Based on accessible multi-source geospatial data, this study constructs a block-scale framework for measuring multidimensional urban vitality and diagnosing coordination status. This framework can provide a reference for vitality identification, functional shortcoming diagnosis, and refined spatial governance in Nanjing’s central urban area, and offer a case reference for historic and cultural cities with similar spatial structures. Full article

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

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23 pages, 57424 KB

Open AccessArticle

A GIS-Based Spatiotemporal Digital Twin-Oriented Framework for a Dammed River Shoreline: Methods, Validation, and Multi-Epoch Analysis

by Tomasz Templin, Julia Leszczyńska, Dariusz Popielarczyk and Katarzyna Zglejc

ISPRS Int. J. Geo-Inf. 2026, 15(7), 317; https://doi.org/10.3390/ijgi15070317 - 13 Jul 2026

Viewed by 454

Abstract

Digital twins are increasingly adopted in geographic research as dynamic representations of environmental systems; however, their application to regulated river shorelines remains limited, particularly where bathymetric change, hydrological variability, and shoreline-state dynamics must be integrated within a single GIS-based framework. This study develops [...] Read more.

Digital twins are increasingly adopted in geographic research as dynamic representations of environmental systems; however, their application to regulated river shorelines remains limited, particularly where bathymetric change, hydrological variability, and shoreline-state dynamics must be integrated within a single GIS-based framework. This study develops and validates a GIS-based spatiotemporal digital twin-oriented framework for the dam-affected shoreline downstream of the Włocławek Dam, Poland. The framework integrates four bathymetric surveys acquired in 2008–2011, water-level records, airborne laser scanning data, and three-dimensional hydrotechnical infrastructure within a unified geodatabase designed for dynamic shoreline-state reconstruction, multi-epoch analysis, and environmental monitoring. A key methodological element is the treatment of water level as a dynamic reference surface, enabling the automated delineation of inundation and exposure zones for observed and scenario-based hydrological conditions. The reconstructed bathymetric surfaces were organized as a multidimensional raster dataset with time as an explicit analytical dimension, supporting repeatable change detection, cross-sectional interpretation, and temporal trend analysis. To extend the framework beyond purely retrospective analysis, a near-real-time hydrological updating component was implemented through ingestion of operational water-level observations from the IMGW API into the geodatabase. Validation of the trend-based prediction for 2011 yielded R² = 0.967, RMSE = 0.44 m, MAE = 0.28 m, and bias = −0.06 m. The proposed framework provides a transferable geospatial basis for spatiotemporal modelling and monitoring of regulated river shoreline dynamics under changing hydrological conditions. Full article

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

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24 pages, 24004 KB

Open AccessArticle

Video Geospatial Mapping of Large-Scale Tower-Based Cameras Based on 3D GIS and Gradient Descent

by Xianguo Ling, Xingguo Zhang, Xin Li and Xiangfei Meng

ISPRS Int. J. Geo-Inf. 2026, 15(7), 316; https://doi.org/10.3390/ijgi15070316 - 12 Jul 2026

Viewed by 521

Abstract

To address the challenges of the large-scale georeferencing of tower-based cameras and the limited capability of video-based spatial analysis, we proposed a geospatial mapping method integrating 3D GIS and gradient descent optimization. Using a Digital Elevation Model (DEM), high-resolution remote sensing imagery, and [...] Read more.

To address the challenges of the large-scale georeferencing of tower-based cameras and the limited capability of video-based spatial analysis, we proposed a geospatial mapping method integrating 3D GIS and gradient descent optimization. Using a Digital Elevation Model (DEM), high-resolution remote sensing imagery, and tower-based video data as the primary data sources, the proposed method first estimates the intrinsic parameters of the tower-based camera by aligning a 3D GIS virtual camera with the video imagery. Subsequently, the initial camera extrinsic parameters are estimated using the PnP algorithm based on the previously estimated intrinsic matrix

K

and the corresponding control point pairs. Building upon these initial estimates, the camera intrinsic and extrinsic parameters are jointly optimized using a constrained L-BFGS-B framework that incorporates prior knowledge of the tower planar location, explicit box constraints, and a semi-constrained parameterization scheme with bounded parameter ranges. Furthermore, an outlier-removal and re-optimization strategy is employed to further improve the accuracy of parameter estimation. Finally, the optimized parameters are employed to transform image coordinates into three-dimensional world coordinates, and video geospatial mapping is achieved through the integration of colored point clouds with the 3D GIS scene. The results showed the following: (1) The 3D GIS scene constructed from publicly available DEM and high-resolution remote sensing imagery met the requirements for the initial estimation of intrinsic and extrinsic camera parameters. (2) Compared with PnP, RANSAC-PnP, SQPnP, and DLT, the proposed method achieves lower reprojection and 3D spatial errors. For the independent check points, the RMSE of the reprojection error is reduced by 66.4%, 73.6%, 68.0%, and 48.3%, respectively, while the RMSE of the 3D spatial error is reduced by 84.6%, 86.2%, 83.1%, and 69.4%, respectively. These results demonstrate that the proposed method provides reliable camera parameter estimates for video geospatial mapping. (3) Using the estimated camera parameters, image coordinates are transformed into 3D world coordinates to generate a georeferenced colored point cloud, which facilitates integrated analysis with existing geospatial datasets. The proposed method provides a feasible solution for tower-based camera georeferencing and three-dimensional visualization under conditions without field calibration. It offers a theoretical and technical basis for geospatial monitoring and related applications. Full article

(This article belongs to the Topic Sensing Urban Vitality: Remote Sensing and GeoAI Applications in Urban Sustainability)

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22 pages, 12467 KB

Open AccessArticle

Robust Visual SLAM with Multi-Level Adaptive Image Enhancement

by Qiaobin Dai, Zhe Yue, Wangyang Yu, Xuerong Zhang and Zengzeng Lian

ISPRS Int. J. Geo-Inf. 2026, 15(7), 315; https://doi.org/10.3390/ijgi15070315 - 11 Jul 2026

Viewed by 322

Abstract

To address the limitation that existing Visual Simultaneous Localization and Mapping (VSLAM) methods fail under complex and variable illumination conditions due to the inability to extract sufficient feature points, this paper proposes a robust V SLAM localization method based on multi-level adaptive image [...] Read more.

To address the limitation that existing Visual Simultaneous Localization and Mapping (VSLAM) methods fail under complex and variable illumination conditions due to the inability to extract sufficient feature points, this paper proposes a robust V SLAM localization method based on multi-level adaptive image enhancement. First, the method employs dynamic brightness compensation to preprocess the original image, initially improving the global brightness distribution. Second, through RGB-to-HSV color space conversion, the brightness V-channel is separated to eliminate the interference of color information in the enhancement process. On this basis, to overcome the limitation of the existing CLAHE algorithm that relies on a fixed clipping threshold and cannot adapt to the local brightness distribution and texture complexity of different image regions, we propose an improved adaptive-threshold CLAHE algorithm based on local statistical characteristics, providing a stable image foundation for feature extraction. Meanwhile, to handle the interference of moving objects in dynamic environments, we incorporate a YOLOv5 object detection thread into the ORB-SLAM3 framework to remove feature points on dynamic objects. This detection module works in synergy with the multi-level image enhancement module, further improving localization robustness in dynamic scenarios. Extensive experiments on the public EuRoC and TUM datasets demonstrate that our method reduces the root mean square error of absolute trajectory error by 29.60% compared to ORB-SLAM3, with a reduction of up to 97.85% on high-dynamic sequences. Our method achieves better localization accuracy and robustness under complex illumination conditions, offering a new solution for visual localization in challenging illumination scenarios. Full article

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

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28 pages, 32783 KB

Open AccessArticle

Future Urban and Rural Built-Up Land Change and Implications for Biodiversity in China

by Roujing Li, Ya Zhou, Hao Geng and Liqiang Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(7), 314; https://doi.org/10.3390/ijgi15070314 - 9 Jul 2026

Viewed by 323

Abstract

Urban expansion is known to drive biodiversity loss in China, but the future impacts of rural built-up land change remain a critical blind spot. Unlike concentrated urban growth, rural development is dispersed and closely tied to livelihood transitions, yet no studies have systematically [...] Read more.

Urban expansion is known to drive biodiversity loss in China, but the future impacts of rural built-up land change remain a critical blind spot. Unlike concentrated urban growth, rural development is dispersed and closely tied to livelihood transitions, yet no studies have systematically projected its biodiversity consequences under alternative socioeconomic pathways. To understand the magnitude and distribution of such impacts, we explore spatially explicit projections of China’s urban-rural settlement dynamics from 2020 to 2070, and assess the impacts on biodiversity. By 2070, urban areas are projected to expand to 1.29–1.74 times their 2020 levels, with the most significant growth occurring in eastern China. Rural built-up land increases under scenarios SSP1, SSP2, SSP3, and SSP4, except for SSP5, with rural shrinkage mainly occurring in eastern and southwestern China. Habitat loss caused by the expansion of rural built-up areas is expected to surpass that caused by urban expansion. Furthermore, habitat loss resulting from cropland displacement will exceed the loss from built-up area expansion. Birds and reptiles are identified as the most vulnerable groups to the expansion of rural-urban built-up areas. The findings contribute to more coordinated biodiversity conservation efforts and provide scientific support for achieving sustainable development goals. Full article

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24 pages, 6345 KB

Open AccessArticle

User-Comfort Pathfinding: Integrating Thermal Imagery and Street-Level Vegetation Analysis into Multi-Criteria Pedestrian Routing

by Saffa Mansour, Mohammed Itair, Rani El Meouche, Aurelie Talon and Pierre Breul

ISPRS Int. J. Geo-Inf. 2026, 15(7), 313; https://doi.org/10.3390/ijgi15070313 - 9 Jul 2026

Viewed by 719

Abstract

Urban heat island effects increasingly challenge pedestrian mobility by intensifying thermal stress and reducing the attractiveness of walking during hot periods. However, most pedestrian routing systems still prioritize distance or travel time, while environmental conditions such as heat exposure and shade are rarely [...] Read more.

Urban heat island effects increasingly challenge pedestrian mobility by intensifying thermal stress and reducing the attractiveness of walking during hot periods. However, most pedestrian routing systems still prioritize distance or travel time, while environmental conditions such as heat exposure and shade are rarely incorporated into operational route generation. Existing comfort-aware approaches often rely on static maps, simulated microclimatic indicators, or descriptive greenery measures, limiting their direct integration into user-configurable pedestrian navigation. This study develops a thermal comfort-aware pedestrian routing framework that integrates heterogenic data sources including observed land surface temperature, pedestrian-perspective tree-canopy coverage, and network distance into a unified multi-criteria pathfinding model. The workflow proceeds in four steps: first, airborne thermal imagery is processed to derive a high-resolution land surface temperature layer; second, Google Street View images are sampled at street-segment locations and segmented using SegFormer to extract visible tree-canopy coverage; third, both environmental indicators are aggregated to a cleaned pedestrian network; and fourth, normalized distance, temperature, and canopy attributes are combined through a user-adjustable edge-cost formulation and solved using Dijkstra’s algorithm. The framework is implemented as an operational web-based routing tool for the historic center of Clermont-Ferrand, France. The routable graph includes 551 nodes and 796 edges, with 600 segments carrying GSV-derived canopy information and 623 segments carrying airborne-derived LST values. Across the network, we observed LST ranges from 19.5 °C to 39.1 °C, while canopy coverage ranged from 0 to 70.6%. For a representative origin–destination pair, the coolest route reduces average LST by nearly 5 °C and almost triples canopy coverage compared with the shortest path, although at the cost of a 72% longer distance. These results demonstrate that the framework can generate interpretable comfort–efficiency trade-offs and support user-comfort pathfinding as an operational approach for heat-resilient pedestrian navigation. Full article

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29 pages, 7948 KB

Open AccessArticle

Generative Artificial Intelligence in Geography: Structure and Evolution of an Emerging Field

by Sai-Leung Ng and Chien-Min Chu

ISPRS Int. J. Geo-Inf. 2026, 15(7), 312; https://doi.org/10.3390/ijgi15070312 - 8 Jul 2026

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Abstract

Generative artificial intelligence (GenAI) is increasingly applied across geographic research, yet existing studies remain fragmented and lack a field-level synthesis. This study addresses this gap through a bibliometric analysis of 891 peer-reviewed journal articles published between 2022 and 2025 and indexed in Scopus. [...] Read more.

Generative artificial intelligence (GenAI) is increasingly applied across geographic research, yet existing studies remain fragmented and lack a field-level synthesis. This study addresses this gap through a bibliometric analysis of 891 peer-reviewed journal articles published between 2022 and 2025 and indexed in Scopus. The results indicate a rapid expansion of GenAI-related geographical research following the diffusion of large language models, alongside strong spatial concentration in a small number of countries and institutions. Five major research themes are identified, including LLM-based GeoAI and GIS workflows, Earth observation and remote sensing, knowledge-driven urban analytics, educational and scholarly communication contexts, and tool-oriented geospatial platforms. Temporal patterns suggest a shift from early exploratory studies toward workflow-level integration. Highly cited contributions concentrate in education, remote sensing, and GIScience, while intellectual foundations draw on both foundational AI architectures and long-standing geographic measurement and modeling. By providing a systematic and large-scale mapping of the structure and evolution of GenAI in geography, this study extends beyond existing narrative and application-focused reviews and offers an integrated account of the field’s development. It also identifies key research gaps for future research. Full article

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25 pages, 24555 KB

Open AccessArticle

Extraction of Non-Motorized Lane Information and Rideability Assessment Framework Based on Cycling Data

by Ruibo Cong, Xiaoya An, Yuqing Niu, Lu Luo, Bozhao Li and Zhongliang Cai

ISPRS Int. J. Geo-Inf. 2026, 15(7), 311; https://doi.org/10.3390/ijgi15070311 - 8 Jul 2026

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Abstract

As demand for non-motorized travel continues to rise, the underdevelopment of non-motorized lane infrastructure in high-density cities has become increasingly evident, affecting cyclists’ travel experience and safety. Existing cycling environment assessment methods have developed relatively comprehensive frameworks, but they still have difficulty capturing [...] Read more.

As demand for non-motorized travel continues to rise, the underdevelopment of non-motorized lane infrastructure in high-density cities has become increasingly evident, affecting cyclists’ travel experience and safety. Existing cycling environment assessment methods have developed relatively comprehensive frameworks, but they still have difficulty capturing the various disturbances encountered during actual cycling and identifying segment-level problems for targeted interventions. To address these limitations, this study proposes a cycling-data-based framework for non-motorized lane information extraction and rideability assessment. The framework integrates cycling trajectories, first-person cycling videos, urban road networks, and points of interest (POIs) to extract information on road space, facility attributes, pavement conditions, visual environment, and static and dynamic disturbances, and further transforms this information into segment-level rideability assessment indicators. On this basis, an assessment system covering safety, comfort, attractiveness, and accessibility is constructed, and Wuhan is used as an empirical case study. Fuzzy C-means (FCM) clustering is then applied to identify six typical lane types and support differentiated governance strategies. The findings provide practical references for non-motorized lane planning, slow-traffic space improvement, and the management of motorized–non-motorized traffic conflicts. Full article

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36 pages, 1711 KB

Open AccessArticle

GeoIR-Compiler: A Geospatial Intermediate Representation and Compilation Framework for Chinese Urban Spatial Question Answering

by Chaolin Zhang, Jiqiu Deng, Hui Zhang, Longbo Li, Liji Sun and Xiao Ma

ISPRS Int. J. Geo-Inf. 2026, 15(7), 310; https://doi.org/10.3390/ijgi15070310 - 8 Jul 2026

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Abstract

Natural-language access to spatial databases requires relation interpretation, entity grounding, metric normalization, and database-specific execution semantics. Direct generation of Structured Query Language (SQL) by large language models (LLMs) can therefore return executable but spatially wrong SQL, especially for Chinese urban questions with aliases, [...] Read more.

Natural-language access to spatial databases requires relation interpretation, entity grounding, metric normalization, and database-specific execution semantics. Direct generation of Structured Query Language (SQL) by large language models (LLMs) can therefore return executable but spatially wrong SQL, especially for Chinese urban questions with aliases, abbreviated place names, and geometry-dependent predicates. This paper presents GeoIR-Compiler, a spatially specialized framework that maps a Chinese question to a typed geospatial intermediate representation (GeoIR), grounds mentions and attributes to database objects, and deterministically compiles the grounded representation into SQL for PostGIS, a spatial database extension for PostgreSQL. The contribution is the specialization of intermediate representations for Chinese urban spatial question answering through explicit spatial relations, metric constraints, grounding records, and PostGIS execution templates. We construct two controlled executable benchmarks, NJ-GeoIR-700 and WH-GeoIR-700, covering retrieval, topology, distance, nearest-neighbor, aggregation, compositional, and alias/noisy-mention queries. Across seven locally served backbones, GeoIR-Full reaches mean execution accuracies of 0.7271 on Nanjing and 0.7363 on Wuhan, outperforming Direct-SQL, Data-Augmented In-Context Learning (DAIL)-SQL-style, and Linking-SQL under the fixed evaluation protocol. Ablations are consistent with grounding contributing strongly to the observed gains, while verification mainly trades coverage for answer reliability. Full article

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25 pages, 7866 KB

Open AccessArticle

Retrospective Assessment of Urban Flooding Susceptibility on the Qinghai–Tibet Plateau Under Data Scarcity

by Yuheng Liu, Libin Su, Yongtao Yang, Yonggang Guo and Tongliang Gong

ISPRS Int. J. Geo-Inf. 2026, 15(7), 309; https://doi.org/10.3390/ijgi15070309 - 7 Jul 2026

Viewed by 440

Abstract

Quantitative assessment of historical urban waterlogging on the Qinghai–Tibet Plateau (QTP) is severely hindered by the lack of early instrumental records. To bridge this data gap during the initial rapid urbanization period (1985–2003), this study proposes an integrated retrospective framework combining Large Language [...] Read more.

Quantitative assessment of historical urban waterlogging on the Qinghai–Tibet Plateau (QTP) is severely hindered by the lack of early instrumental records. To bridge this data gap during the initial rapid urbanization period (1985–2003), this study proposes an integrated retrospective framework combining Large Language Models (LLMs)-based semantic mining, spatial reconstruction, and Extreme Gradient Boosting (XGBoost)- SHapley Additive exPlanations (SHAP) modeling under a Spatial Block Cross-Validation (SBCV) strategy. Historical disaster archives were transformed into spatially explicit training samples, enabling the reconstruction of a high-resolution urban waterlogging susceptibility atlas across the QTP. The results indicate that high-susceptibility areas are predominantly concentrated within urbanized river valleys and account for approximately 45% of the total urban built-up area across the QTP. The proposed framework achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9793 under the SBCV strategy, indicating good spatial transferability within the study area. SHAP analysis revealed that geomorphic variables contributed more strongly than most climatic variables, highlighting the important role of a geomorphic confinement effect in shaping susceptibility patterns. Comparative analyses further suggest a spatial transition from basin-dominated accumulation patterns to increasingly valley-confined susceptibility distributions under stronger topographic constraints. In addition, surface albedo and land surface temperature were identified as influential predictors, likely reflecting integrated thermal-hydrological conditions associated with antecedent soil moisture and local urban thermal dynamics. This study establishes a historical risk baseline for the QTP and provides a reproducible and cost-effective framework for historical hazard assessment in other data-scarce mountainous and high-altitude regions. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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23 pages, 2948 KB

Open AccessArticle

A VGI-Based Intelligent Agent for Quality Inspection and Data Fusion of Building Data

by Yingjie Ji, Song Liu, Shiqiang Nie, Jinyu Wang and Weiguo Wu

ISPRS Int. J. Geo-Inf. 2026, 15(7), 308; https://doi.org/10.3390/ijgi15070308 - 7 Jul 2026

Viewed by 412

Abstract

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides [...] Read more.

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides a crowdsourced solution for geospatial data collection, it is commonly hindered by significant heterogeneity—manifested in inconsistent data completeness, positional inaccuracies and poor topological consistency across different datasets. To address these critical limitations, this study proposes an intelligent geospatial agent framework designed to autonomously fuse building data from multiple heterogeneous sources, including VGI, Very High-Resolution (VHR) satellite imagery, and Light Detection and Ranging (LiDAR) data. This study’s core innovative points are embodied in three key modules: a supervised VGI quality verification module that leverages the Random Forest model to evaluate the reliability of individual building feature elements; a hybrid building extraction engine which integrates LiDAR data with the Segment Anything Model (SAM) to realize zero-shot building extraction; and a cognitive rule engine that adopts Multi-Criteria Decision Analysis (MCDA) for the intelligent resolution of spatial conflicts. Comprehensive validation experiments were conducted in two African cities experiencing rapid urbanization—Kigali and Dar es Salaam. The results show that the proposed framework boosts data completeness by more than 29% and attains a fused dataset F1-Score of 0.919, effectively converting incomplete VGI data into a geospatial resource with near-official authoritative quality. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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ISPRS Int. J. Geo-Inf., Volume 15, Issue 7 (July 2026) – 61 articles

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