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Choreme-Based Spatial Analysis and Tourism Assessment in the Oltenia de sub Munte Geopark, Romania
Built-Up Surface Ensemble Model for Romania Based on OpenStreetMap, Microsoft Building Footprints, and Global Human Settlement Layer Data Sources Using Triple Collocation Analysis
Accelerating Computation for Estimating Land Surface Temperature: An Efficient Global–Local Regression (EGLR) Framework

Journal Description

ISPRS International Journal of Geo-Information

ISPRS International Journal of Geo-Information (IJGI) is an international, peer-reviewed, open access journal on geo-information, and is published monthly online. It is the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). Society members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), GeoRef, PubAg, dblp, Astrophysics Data System, Inspec, and other databases.
Journal Rank: JCR - Q2 (Geography, Physical) / CiteScore - Q1 (Earth and Planetary Sciences (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 34.2 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the first half of 2025).
Rejection Rate: a rejection rate of 76% in 2024.
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.8 (2024); 5-Year Impact Factor: 3.3 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2220-9964

Latest Articles

36 pages, 4242 KB

Open AccessArticle

Comparative Framework for Multi-Modal Accessibility Assessment Within the 15-Minute City Concept: Application to Parks and Playgrounds in an Indian Urban Neighborhood

by Swati Bahale, Amarpreet Singh Arora and Thorsten Schuetze

ISPRS Int. J. Geo-Inf. 2025, 14(12), 479; https://doi.org/10.3390/ijgi14120479 (registering DOI) - 2 Dec 2025

Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable [...] Read more.

Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable transportation in Indian urban neighborhoods, with ‘Accessibility’ identified as a crucial subtheme. Recent advancements in Geographic Information Systems (GISs) and urban data analysis tools have enabled accessibility assessments of parks and playgrounds at a neighborhood scale, yet the OSMnx approach has been only marginally explored and compared in the literature. This study addresses this gap by comparing three tools—the Quantum Geographic Information System (QGIS), OSMnx, and Space Syntax—for accessibility assessments of parks and playgrounds in Ward 60 of Kalyan Dombivli city, based on the 15-Minute City concept. Accessibility was evaluated using 25 m and 100 m grid resolutions under peak and non-peak conditions across public and private transportation modes. The findings show that QGIS offers highly consistent results at micro-scale (25 m), while OSMnx provides better accuracy at coarser scales (100 m+). The results were validated with space syntax through integration and choice values. The comparison highlights spatial disparities in accessibility across different tools and transportation modes, including Intermediate Public Transport (IPT), which remains underexplored despite its crucial role in last-mile connectivity. The presented approach can support municipal authorities in optimizing neighborhood mobility and is transferable for applying the SusTAIN framework in other urban contexts. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

26 pages, 4176 KB

Open AccessFeature PaperArticle

An Effective Approach to Geometric and Semantic BIM/GIS Data Integration for Urban Digital Twin

by Peyman Azari, Songnian Li and Ahmed Shaker

ISPRS Int. J. Geo-Inf. 2025, 14(12), 478; https://doi.org/10.3390/ijgi14120478 (registering DOI) - 2 Dec 2025

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper [...] Read more.

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper proposes a novel, scalable methodology for comprehensive BIM/GIS integration, addressing both geometric and semantic challenges. The approach introduces a geometry conversion workflow that transforms solid BIMs into valid, simplified CityGML representations through a level-by-level detection of building elements and outer surface extraction. To preserve semantic richness, all entities, attributes, and relationships—including implicit connections—are automatically extracted and stored in a Labeled Property Graph (LPG) database. The method is further extended with a new CityGML Application Domain Extension (ADE) that supports Multi-LoD4 representations, enabling selective interior visualization and efficient rendering. A web-based urban digital twin platform demonstrates the integration, allowing dynamic semantic querying and scalable 3D visualization. Results show a significant reduction in geometric complexity, full semantic retention, and robust performance in visualization and querying, offering a practical pathway for advanced UDT development. Full article

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

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20 pages, 5765 KB

Open AccessArticle

Infrared–Visible Fusion via Cross-Modality Attention and Small-Object Enhancement for Pedestrian Detection

by Jie Yang, Yanxuan Jiang, Dengyin Jiang and Zhichao Chen

ISPRS Int. J. Geo-Inf. 2025, 14(12), 477; https://doi.org/10.3390/ijgi14120477 (registering DOI) - 2 Dec 2025

Pedestrian detection under low illumination and complex environments remains a significant challenge for vision-based systems, particularly in safety-critical applications such as urban rail transit. To address the limitations of single-modality detection in adverse conditions, this paper proposes IVIFusion, a lightweight yet robust pedestrian [...] Read more.

Pedestrian detection under low illumination and complex environments remains a significant challenge for vision-based systems, particularly in safety-critical applications such as urban rail transit. To address the limitations of single-modality detection in adverse conditions, this paper proposes IVIFusion, a lightweight yet robust pedestrian detection framework that fuses infrared and visible images at the feature level. The method integrates a dual-branch Transformer-based backbone for modality-specific feature extraction and introduces a Cross-Modality Attention Fusion Module (CMAFM) to adaptively enhance cross-modal representations while suppressing noise. Furthermore, a dedicated small-object detection layer is incorporated to improve the recall of distant and occluded pedestrians. Extensive experiments conducted on the public LLVIP dataset and the custom HGPD dataset demonstrate the superior performance of IVIFusion, achieving mAP_0.5 scores of 98.6% and 97.2%, respectively. The results validate the effectiveness of the proposed architecture in handling challenging lighting conditions while maintaining real-time efficiency and low computational cost. Full article

(This article belongs to the Topic State-of-the-Art Object Detection, Tracking, and Recognition Techniques)

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17 pages, 5641 KB

Open AccessArticle

A Novel Smartphone PDR Framework Based on Map-Aided Adaptive Particle Filter with a Reduced State Space

by Mengchi Ai, Ilyar Asl Sabbaghian Hokmabadi and Xuan Zhao

ISPRS Int. J. Geo-Inf. 2025, 14(12), 476; https://doi.org/10.3390/ijgi14120476 (registering DOI) - 2 Dec 2025

Accurate, reliable and infrastructure-free indoor positioning using a smartphone is considered an essential topic for applications such as indoor emergency response and indoor path planning. While the inertial measurement units (IMU) offer continuous and high-frequency motion data, pedestrian dead reckoning (PDR) based on [...] Read more.

Accurate, reliable and infrastructure-free indoor positioning using a smartphone is considered an essential topic for applications such as indoor emergency response and indoor path planning. While the inertial measurement units (IMU) offer continuous and high-frequency motion data, pedestrian dead reckoning (PDR) based on IMU data suffers from significant and accumulative errors. Map-aided particle filters (PFs) are important pose estimation frameworks that have exhibited capabilities to eliminate drifts by incorporating additional constraints from a pre-built floor map, without relying on other wireless or perception-based infrastructures. However, despite the recent approaches, a key challenging issue remains: existing map-aided PF-PDR solutions are computationally demanding, as they typically rely on a large number of particles and require map boundaries to eliminate non-matching particles. This process introduces substantial computational overhead, limiting efficiency and real-time performance on resource-constrained platforms such as smartphones. To address this key issue, this work proposes a novel map-aided PF-PDR framework that leverages a smartphone’s IMU data and a pre-built vectorized floor plan map. The proposed method introduces an adaptive PF-PDR solution that detects particle convergence using a cross-entropy distance of the particles and a Gaussian distribution. The number of particles is reduced significantly after a convergence is detected. Further, in order to reduce the computational cost, only the heading is included in particle attitude sampling. The heading is estimated accurately by levelling gyroscope measurements to a virtual plane, parallel to the ground. Experiments are performed using a dataset collected on a smartphone and the results demonstrate improved performance, especially in drift reduction, achieving an mean position error of 0.9 m and a processing rate of 37.0 Hz. Full article

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

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18 pages, 30684 KB

Open AccessArticle

Leveraging Explainable Artificial Intelligence for Place-Based and Quantitative Strategies in Urban Pluvial Flooding Management

by Chaorui Tan, Entong Ke and Haochen Shi

ISPRS Int. J. Geo-Inf. 2025, 14(12), 475; https://doi.org/10.3390/ijgi14120475 (registering DOI) - 1 Dec 2025

Reducing urban pluvial flooding susceptibility requires identifying dominant variables in different regions and offering quantitative management strategies, which remains a challenge for existing methodologies. To address this, this study delves into the characteristics of SHAP’s (Shapley Additive exPlanations) local interpretability and proposes a [...] Read more.

Reducing urban pluvial flooding susceptibility requires identifying dominant variables in different regions and offering quantitative management strategies, which remains a challenge for existing methodologies. To address this, this study delves into the characteristics of SHAP’s (Shapley Additive exPlanations) local interpretability and proposes a novel and concise framework based on explainable artificial intelligence (ensemble learning-SHAP) and applies it to the central urban area of Guangzhou as a case study. The research findings are as follows: (1) This framework captures the nonlinear and threshold effects of flood drivers, identifying specific inflection points where landscape features shift from mitigating to exacerbating flooding. (2) Anthropogenic variables, specifically impervious surface density (ISD) and vegetation (kNDVI), are identified as the dominant variables driving susceptibility in urban hotspots at the grid scale. (3) The interpretability results demonstrate high stability across model iterations. Finally, based on these findings, this study provides place-based and quantitative pluvial flooding management recommendations: for areas dominated by impervious surfaces and vegetation, maintaining the impervious surface density below 0.8 and the kNDVI above 0.25 can effectively reduce the susceptibility to urban flooding. This study provides a framework for achieving place-based and quantitative flooding management and offers valuable scientific insights for flooding management, urban planning, and sustainable urban development in the central district of Guangzhou, as well as in broader developing regions. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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30 pages, 1800 KB

Open AccessArticle

A GIS-Native Framework for Qualitative Place Models: Implementation and Evaluation

by Abdurauf Satoti and Alia I. Abdelmoty

ISPRS Int. J. Geo-Inf. 2025, 14(12), 474; https://doi.org/10.3390/ijgi14120474 (registering DOI) - 1 Dec 2025

Humans typically describe spatial location using names, hierarchies, and relative positions (e.g., east of, inside), yet mainstream GIS represents places primarily through geometric coordinates, rendering qualitative spatial queries computationally challenging. We introduce the Qualitative Place Model (QPM), a GIS-native framework that transforms standard [...] Read more.

Humans typically describe spatial location using names, hierarchies, and relative positions (e.g., east of, inside), yet mainstream GIS represents places primarily through geometric coordinates, rendering qualitative spatial queries computationally challenging. We introduce the Qualitative Place Model (QPM), a GIS-native framework that transforms standard boundary datasets and place layers into structured knowledge bases of Qualitative Place Description (QPD). QPM provides a homogeneous representation whereby administrative units and physical places are treated uniformly as Place entities. The model materializes a compact set of local relations, hierarchical containment, directional neighbourhood, and optional proximity, that support rich inferences through sound logical operations (inverse relationships and per-predicate transitive closure). We implement QPM as an ArcGIS Pro toolbox that computes and persists QPDs within a geodatabase, with optional RDF export for SPARQL querying. This implementation enables natural-language-style spatial queries such as “Where is x?” or “Which places are north of x?” within standard GIS workflows. Evaluation on Wales (UK) administrative, postal, and electoral hierarchies plus a comprehensive place layer demonstrates robust performance: QPM generated 95.8% of expected basic-place statements (52,821 places) and achieved 89.7–96.5% coverage across administrative hierarchies. All QPDs proved unique under our deterministic signature. Despite compact storage requirements, directional relations expand by more than an order of magnitude (10.6× overall expansion) under logical closure, demonstrating substantial inferential power from a minimal stored representation. QPM complements geometric GIS with an explainable qualitative layer that aligns with human spatial cognition while remaining fully operational within standard GIS environments. Full article

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15 pages, 12075 KB

Open AccessArticle

Impact of Scanning Quality on Deep Learning-Based Contour Vectorization from Topographic Maps

by Jakub Vynikal and Jan Pacina

ISPRS Int. J. Geo-Inf. 2025, 14(12), 473; https://doi.org/10.3390/ijgi14120473 (registering DOI) - 1 Dec 2025

The quality of scanned topographic maps—including parameters such as image compression, scanning resolution, and bit depth—may strongly influence the performance of deep learning models for contour vectorization. In this study, we investigate this dependence by training eight U-Net models on the same map [...] Read more.

The quality of scanned topographic maps—including parameters such as image compression, scanning resolution, and bit depth—may strongly influence the performance of deep learning models for contour vectorization. In this study, we investigate this dependence by training eight U-Net models on the same map data but under varying input quality conditions. Each model is trained to segment contour lines from the raster input, followed by a postprocessing pipeline that converts segmented output into vector contours. We systematically compare the models with respect to topological error metrics (such as contour intersections and dangling ends) in the resulting vector output and overlay metrics of matched contour segments within given tolerance. Our experiments demonstrate that while the input data quality indeed matters, moderate lowering of quality parameters doesn’t introduce significant practical tradeoff, while storage and computational requirements remain low. We discuss implications for the preparation of archival map scans and propose guidelines for choosing scanning settings when the downstream goal is automated vectorization. Our results highlight that deep learning methods, though resilient against reasonable compression, remain measurably sensitive to degradation in input fidelity. Full article

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26 pages, 8979 KB

Open AccessArticle

Assessing the Multidimensionality of the 15-Min City in Seville Through Open Geospatial Data

by Joaquín Osorio-Arjona and José David Albarrán-Periáñez

ISPRS Int. J. Geo-Inf. 2025, 14(12), 472; https://doi.org/10.3390/ijgi14120472 (registering DOI) - 1 Dec 2025

This paper aims to map the degree of implementation of the 15-min city model in a medium-sized city like Seville and analyze the demographic, economic, and structural characteristics that affect the varying degree of implementation of the model. To this end, facility density [...] Read more.

This paper aims to map the degree of implementation of the 15-min city model in a medium-sized city like Seville and analyze the demographic, economic, and structural characteristics that affect the varying degree of implementation of the model. To this end, facility density was estimated from 15-min walking isochrones for each census tract, and a synthetic index was calculated from the coefficients obtained for each type of facility using a Geographically Weighted Regression (GWR) model that takes into account the spatial variation in infrastructure availability. A second GWR model was used to study the spatial impact of several demographic, socio-economic and structural variables on the calculated synthetic index. The main results show residential neighborhoods with greater accessibility and infrastructure diversity have a higher degree of compliance with the 15-min city model, while the city’s most marginalized and vulnerable neighborhoods have a negative index. It also highlights the fact that the processes of touristification and gentrification of the city’s historic center contribute to a lack of compliance with the model. These findings provide an empirical basis for designing urban policies aimed at reducing the territorial gap and towards equity in access to basic services. Full article

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34 pages, 24575 KB

Open AccessArticle

Street View Image-Based Emotional Perception Modeling of Old Residential Communities: An Explainable Framework Integrating Random Forest and SHAP

by Yanqing Xu and Xiaoxuan Fan

ISPRS Int. J. Geo-Inf. 2025, 14(12), 471; https://doi.org/10.3390/ijgi14120471 (registering DOI) - 29 Nov 2025

Understanding how the built environment shapes residents’ emotional perceptions in old residential communities (ORCs) is essential for enhancing livability and supporting people-oriented urban regeneration. This study proposes an explainable analytical framework that integrates community attributes, streetscape indicators, and subjective evaluations. Using random forest [...] Read more.

Understanding how the built environment shapes residents’ emotional perceptions in old residential communities (ORCs) is essential for enhancing livability and supporting people-oriented urban regeneration. This study proposes an explainable analytical framework that integrates community attributes, streetscape indicators, and subjective evaluations. Using random forest (RF) regression combined with Shapley Additive Explanations (SHAP), we conducted an empirical study on ten ORCs in Yangzhou, China. A total of 1240 street view images (SVIs) were processed to extract social attributes, including building age, building scale, and point-of-interest (POI) diversity, as well as visual indicators such as walkability, green view index (GVI), and colorfulness. Six emotional perception scores were obtained from the MIT Place Pulse 2.0 model and further calibrated through questionnaires. The results show that the proposed framework effectively captures the spatial determinants of residents’ perceptions, with the model predictions being highly consistent with survey evaluations. Specifically, GVI and street enclosure are positively associated with perceptions of beauty, safety, and vitality, while building aging and functional monotony intensify negative feelings such as oppression and boredom. Visual diversity (VD) enhances aesthetic and vitality perceptions, whereas facility visual entropy demonstrates a dual role—reinforcing safety but potentially inducing oppressive feelings. By integrating interpretable machine learning with geospatial analysis, this study provides both theoretical and practical insights for micro-scale community renewal, and the framework can be extended to multimodal analyses including soundscapes and behavioral pathways. Full article

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

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23 pages, 16779 KB

Open AccessArticle

Exploring the Relationship Between the Built Environment and Spatiotemporal Heterogeneity of Urban Traffic Congestion During Tourism Peaks: A Case Study of Harbin, China

by Renyue Cui and Jun Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(12), 470; https://doi.org/10.3390/ijgi14120470 (registering DOI) - 29 Nov 2025

Understanding the spatial heterogeneity of traffic congestion drivers is crucial for data-informed urban planning in tourist cities. This study investigates the spatiotemporal relationship between built environment characteristics and traffic congestion in the central urban area of a major northern Chinese tourist city. We [...] Read more.

Understanding the spatial heterogeneity of traffic congestion drivers is crucial for data-informed urban planning in tourist cities. This study investigates the spatiotemporal relationship between built environment characteristics and traffic congestion in the central urban area of a major northern Chinese tourist city. We apply a Multiscale Geographically Weighted Regression (MGWR) model to geospatial data across four typical peak periods and benchmark the results against Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). The MGWR model demonstrates superior capability in capturing spatial non-stationarity and multiscale effects. The results reveal strong spatiotemporal heterogeneity in the effects of built environment factors on congestion. Intersection density demonstrates a stronger mitigating effect during weekday evening peaks. Catering facilities significantly exacerbate congestion in tourist hotspots. Tourism-related facilities such as hotels and attractions intensify congestion during weekend peaks. Parking availability shows dual impacts, with peripheral parking reducing pressure and central clustering worsening congestion. Our geospatially disaggregated results provide empirical evidence for location-sensitive and temporally adaptive traffic management and urban design strategies. This study highlights the value of MGWR-based spatial modeling in supporting geoinformation-driven urban mobility planning. Full article

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37 pages, 8888 KB

Open AccessArticle

The Art Nouveau Path: Trajectory Analysis and Spatial Storytelling Through a Location-Based Augmented Reality Game in Urban Heritage

by João Ferreira-Santos and Lúcia Pombo

ISPRS Int. J. Geo-Inf. 2025, 14(12), 469; https://doi.org/10.3390/ijgi14120469 (registering DOI) - 28 Nov 2025

Urban heritage, when enhanced by digital technologies, can become a living laboratory. This study explores the Art Nouveau Path, a mobile augmented reality game implemented in Aveiro, Portugal, as part of the EduCITY Digital Teaching and Learning Ecosystem. Designed as a circular [...] Read more.

Urban heritage, when enhanced by digital technologies, can become a living laboratory. This study explores the Art Nouveau Path, a mobile augmented reality game implemented in Aveiro, Portugal, as part of the EduCITY Digital Teaching and Learning Ecosystem. Designed as a circular path of eight georeferenced points of interest, it integrates narrative cartography, multimodal media, and sustainability competences framed by GreenComp, the European Sustainability Framework. A DBR approach guided the study, combining four interconnected datasets: the game’s structured curriculum review by 3 subject specialists (T1-R), gameplay logs from 118 student groups (4248 responses), post-game reflections from 439 students (S2-POST), and in-field observations from 24 teachers (T2-OBS). Descriptive statistics and thematic coding were triangulated to examine attention to architectural details, the mediational role of AR, spatial trajectories, and reflections about sustainability. The results present overall accuracy (85.33%), with particularly strong performance on video items (93.64%), stable outcomes on AR tasks (85.52%), and lower accuracy in denser urban contexts. Qualitative data highlight AR as a catalyst for perceiving hidden features, collaboration, and connecting heritage with sustainability. The study concludes that location-based AR games can generate semantically enriched geoinformation. They also act as cartographic interfaces that embed narrative and competence-oriented learning into urban heritage contexts. Full article

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24 pages, 1887 KB

Open AccessArticle

Geometry-Aware CRDTs for Efficient Collaborative Geospatial Editing

by Pengcheng Zhang and Chao Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(12), 468; https://doi.org/10.3390/ijgi14120468 - 28 Nov 2025

Maintaining consistency in real-time multi-user editing of planar geospatial features remains challenging for traditional collaborative editing techniques, which are primarily designed for text documents. When applied to spatial data, these methods often yield inaccurate results and cause information loss, while also overlooking the [...] Read more.

Maintaining consistency in real-time multi-user editing of planar geospatial features remains challenging for traditional collaborative editing techniques, which are primarily designed for text documents. When applied to spatial data, these methods often yield inaccurate results and cause information loss, while also overlooking the geospatial and topological properties of such features. Moreover, they fail to differentiate processing priorities due to limited spatial awareness, hindering targeted performance optimization. To address these limitations, we propose a geometry-aware collaborative editing algorithm based on Conflict-Free Replicated Data Types (CRDTs), integrating a spatial–semantic data model with spatio-temporal operation merging strategies. As an extension of CRDTs tailored for spatial data, it leverages geometric vector clocks (GVCs) and minimum bounding rectangles (MBRs) to capture temporal and spatial dependencies among editing operations, detects topological anomalies through geometric constraints, resolves conflicts via spatio-temporal metadata encoded in GVCs, and optimizes performance through MBR-based operation classification. Experimental results show that this approach improves editing accuracy, contributes to preserving topological integrity, and maintains strong performance under collaborative editing workloads, with notable efficiency gains for large-scale datasets and visible features. This work provides a novel geometry-aware framework for scalable, accurate multi-user editing of planar geospatial features that helps preserve topological integrity. Full article

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20 pages, 6042 KB

Open AccessFeature PaperArticle

GeoSpatial Analysis of Health-Oriented Justice in Tartu, Estonia

by Najmeh Mozaffaree Pour

ISPRS Int. J. Geo-Inf. 2025, 14(12), 467; https://doi.org/10.3390/ijgi14120467 - 28 Nov 2025

This study investigates the role of modern small-scale cities in addressing public health challenges through the lens of spatial justice, using the city of Tartu, Estonia, as a case study. Tartu has been recognized for its progressive public health initiatives, including the Tartu [...] Read more.

This study investigates the role of modern small-scale cities in addressing public health challenges through the lens of spatial justice, using the city of Tartu, Estonia, as a case study. Tartu has been recognized for its progressive public health initiatives, including the Tartu Health Care College, Mental Health Centre, Smoke-Free Tartu campaign, Health Trail network, Healthy School Program, and an expanding smart bike-sharing system. By employing Geographic Information Systems (GIS), we map and analyze the spatial distribution and accessibility of health-promoting infrastructure, such as healthcare facilities, green and blue spaces, health trails, and mobility services, across the urban landscape. A population-weighted accessibility assessment indicates that, although Tartu’s central districts (e.g., Kesklinn (HRI: 0.972)) are well-served, peripheral and densely populated districts such as Annelinn (HRI: 0.351) and Ropka (HRI: 0.377) exhibit notable deficits in health-related infrastructure. However, access to green infrastructure and mobility services is more evenly distributed citywide, reflecting a relatively equitable provision of non-clinical health assets. These findings highlight both the strengths and spatial gaps in Tartu’s health-oriented urban design, emphasizing the need for targeted investment in underserved areas. The study contributes to emerging studies on health-justice planning in small-scale urban contexts and demonstrates how spatial analytics can be guided to advance distributional justice in the provision of public health infrastructure. Ultimately, this research indicates the essential role of spatial analysis in guiding inclusive and data-informed health planning in urban environments. Full article

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

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17 pages, 4983 KB

Open AccessArticle

TAGNet: A Tidal Flat-Attentive Graph Network Designed for Airborne Bathymetric LiDAR Point Cloud Classification

by Ahram Song

ISPRS Int. J. Geo-Inf. 2025, 14(12), 466; https://doi.org/10.3390/ijgi14120466 - 28 Nov 2025

Airborne LiDAR bathymetry (ALB) provides dense three-dimensional point clouds that enable the detailed mapping of tidal flat environments. However, surface classification using these point clouds remains challenging due to residual noise, water surface reflectivity, and subtle class boundaries that persist even after standard [...] Read more.

Airborne LiDAR bathymetry (ALB) provides dense three-dimensional point clouds that enable the detailed mapping of tidal flat environments. However, surface classification using these point clouds remains challenging due to residual noise, water surface reflectivity, and subtle class boundaries that persist even after standard preprocessing. To address these challenges, this study introduces Tidal flat-Attentive Graph Network (TAGNet), a graph-based deep learning framework designed to leverage both local geometric relationships and global contextual cues for the point-wise classification of tidal flat surface classes. The model incorporates multi-scale EdgeConv layers for capturing fine-grained neighborhood structures and employs squeeze-and-excitation channel attention to enhance global feature representation. To validate TAGNet’s effectiveness, classification was conducted on ALB point clouds collected from adjacent tidal flat regions, focusing on four major surface classes: exposed flat, sea surface, sea floor, and vegetation. In benchmarking tests against baseline models, including Dynamic Graph Convolutional Neural Network, PointNeXt with Single-Scale Grouping, and PointNet Transformer, TAGNet consistently achieved higher macro F1-scores. Moreover, ablation studies isolating positional encoding, attention mechanisms, and detrended Z-features confirmed their complementary contributions to TAGNet’s performance. Notably, the full TAGNet outperformed all baselines by a substantial margin, particularly when distinguishing closely related classes, such as sea floor and exposed flat. These findings highlight the potential of graph-based architectures specifically designed for ALB data in enhancing the precision of coastal monitoring and habitat mapping. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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27 pages, 7255 KB

Open AccessArticle

A Methodology to Convert Highly Detailed BIM Models into 3D Geospatial Building Models at Different LoDs

by Jasper van der Vaart, Ken Arroyo Ohori and Jantien Stoter

ISPRS Int. J. Geo-Inf. 2025, 14(12), 465; https://doi.org/10.3390/ijgi14120465 - 28 Nov 2025

This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction [...] Read more.

This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction and conversion methods are required to generate usable outputs. Our study addresses this by developing a methodology that generates nine different LoDs from a single IFC input. These LoDs include both volumetric and surface-based abstractions for exterior and interior representations. The methodology involves voxelisation, filtering and simplification of surfaces, footprint derivation, storey abstraction, and interior geometry extraction. Together, these approaches allow flexible conversion tailored to specific applications, balancing accuracy, complexity, and computational efficiency. The methodology is implemented in a prototype tool named IfcEnvelopeExtractor. It automates IFC-to-CityGML/CityJSON conversion with minimal user input. The methodology was tested on a variety of models ranging from small houses to multistorey buildings. The evaluation covered geometric accuracy, semantic accuracy, and model complexity. Results show that non-volumetric abstractions and interior abstractions performed very well, producing robust and accurate results. However, the accuracy decreased for volumetric and complex abstractions, particularly at higher LoDs. Problems included missing or incorrectly trimmed surfaces, and modelling gaps and tolerance issues in the input IFC models. These limitations reveal that the quality of the input BIM models significantly affects the reliability of conversions. Overall, the methodology demonstrates that automated, flexible, and open-source solutions can effectively bridge the gap between BIM and geospatial domains, contributing to scalable GeoBIM integration in practice. Full article

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1 pages, 147 KB

Open AccessRetraction

RETRACTED: Zhao et al. Indoor Floor Localization Based on Multi-Intelligent Sensors. ISPRS Int. J. Geo-Inf. 2021, 10, 6

by Min Zhao, Danyang Qin, Ruolin Guo and Xinxin Wang

ISPRS Int. J. Geo-Inf. 2025, 14(12), 464; https://doi.org/10.3390/ijgi14120464 - 28 Nov 2025

The Journal retracts the article “Indoor floor localization based on multi-intelligent sensors” [...] Full article

19 pages, 4300 KB

Open AccessArticle

Investigating the Imbalanced Patterns and Determinants of Kindergarten Distribution Across China

by Guiling Tang and Feng Xu

ISPRS Int. J. Geo-Inf. 2025, 14(12), 463; https://doi.org/10.3390/ijgi14120463 - 25 Nov 2025

The unbalanced allocation of educational resources in kindergartens across China has attracted increasing attention from scholars and the public. However, few studies have examined their spatially imbalanced distribution and its influencing factors. Based on point-of-interest data, this study systematically analyzes the spatially imbalanced [...] Read more.

The unbalanced allocation of educational resources in kindergartens across China has attracted increasing attention from scholars and the public. However, few studies have examined their spatially imbalanced distribution and its influencing factors. Based on point-of-interest data, this study systematically analyzes the spatially imbalanced distribution characteristics of kindergartens in China from a multiscale perspective using the spatial analysis and spatial regression model to identify the factors influencing its formation pattern. The results reveal that the distribution pattern is “more in the southeast and fewer in the northwest,” with the Hu Huanyong Line serving as the boundary. Kernel density analysis revealed that areas with a density greater than 0.34 individual/km² were primarily concentrated in provincial capitals and major metropolitan areas, exhibiting a gradual decrease outward from these core zones. It also reveals a “large dispersion and small aggregation”, with a concentration around mega-cities, urban agglomerations, and provincial capitals. Significant spatial auto-correlations were found at all administrative levels, with hotspots distributed in northeast, north, and southeast China. The spatial determinants of kindergartens distribution in China exhibited significant spatial heterogeneity. The findings provide a reference in improving the spatial pattern and the state of unbalanced development of kindergarten education in China, as well as scientific suggestions to optimize resource allocation. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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18 pages, 1972 KB

Open AccessFeature PaperArticle

Automatic Reconstruction of 3D Building Models from ALS Point Clouds Based on Façade Geometry

by Tingting Zhao, Tao Xiong, Muzi Li and Zhilin Li

ISPRS Int. J. Geo-Inf. 2025, 14(12), 462; https://doi.org/10.3390/ijgi14120462 - 25 Nov 2025

Three-dimensional (3D) building models are essential for urban planning, spatial analysis, and virtual simulations. However, most reconstruction methods based on Airborne LiDAR Scanning (ALS) rely primarily on rooftop information, often resulting in distorted footprints and the omission of façade semantics such as windows [...] Read more.

Three-dimensional (3D) building models are essential for urban planning, spatial analysis, and virtual simulations. However, most reconstruction methods based on Airborne LiDAR Scanning (ALS) rely primarily on rooftop information, often resulting in distorted footprints and the omission of façade semantics such as windows and doors. To address these limitations, this study proposes an automatic 3D building reconstruction method driven by façade geometry. The proposed method introduces three key contributions: (1) a façade-guided footprint generation strategy that eliminates geometric distortions associated with roof projection methods; (2) robust detection and reconstruction of façade openings, enabling reliable identification of windows and doors even under sparse ALS conditions; and (3) an integrated volumetric modeling pipeline that produces watertight models with embedded façade details, ensuring both structural accuracy and semantic completeness. Experimental results show that the proposed method achieves geometric deviations at the decimeter level and feature recognition accuracy exceeding 97%. On average, the reconstruction time of a single building is 91 s, demonstrating reliable reconstruction accuracy and satisfactory computational performance. These findings highlight the potential of the method as a robust and scalable solution for large-scale ALS-based urban modeling, offering substantial improvements in both structural precision and semantic richness compared with conventional roof-based approaches. Full article

(This article belongs to the Special Issue Knowledge-Guided Map Representation and Understanding)

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23 pages, 15389 KB

Open AccessArticle

A Multi-Agent Deep Reinforcement Learning Method with Diversified Policies for Continuous Location of Express Delivery Stations Under Heterogeneous Scenarios

by Yijie Lyu, Zhongan Tang, Yalun Li, Baoju Liu, Min Deng and Guohua Wu

ISPRS Int. J. Geo-Inf. 2025, 14(12), 461; https://doi.org/10.3390/ijgi14120461 - 24 Nov 2025

Rational location planning of express delivery stations (EDS) is crucial for enhancing the quality and efficiency of urban logistics. The spatial heterogeneity of logistics demand across urban areas highlights the importance of adopting a scientific approach to EDS location planning. To tackle the [...] Read more.

Rational location planning of express delivery stations (EDS) is crucial for enhancing the quality and efficiency of urban logistics. The spatial heterogeneity of logistics demand across urban areas highlights the importance of adopting a scientific approach to EDS location planning. To tackle the issue of strategy misalignment caused by heterogeneous demand scenarios, this study proposes a continuous location method for EDS based on multi-agent deep reinforcement learning. The method formulates the location problem as a continuous maximum coverage model and trains multiple agents with diverse policies to enable adaptive decision-making in complex urban environments. A direction-controlled continuous movement mechanism is introduced to facilitate an efficient search and high-precision location planning. Additionally, a perception system based on local observation is designed to rapidly capture heterogeneous environmental features, while a local–global reward feedback mechanism is established to balance localized optimization with overall system benefits. Case studies conducted in Fuzhou, Fujian Province and Shenzhen, Guangdong Province, China, demonstrate that the proposed method significantly outperforms traditional heuristic methods and the single-agent deep reinforcement learning method in terms of both coverage rate and computational efficiency, achieving an increase in population coverage of 9.63 and 15.99 percentage points, respectively. Furthermore, by analyzing the relationship between the number of stations and coverage effectiveness, this study identifies optimal station configuration thresholds for different urban areas. The findings provide a scientific basis for investment decision-making and location planning in EDS construction. Full article

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28 pages, 6273 KB

Open AccessArticle

Environmental Sensitivity Index Assessment Based on Factors in Oil Spill Impact in Coastal Zone Using Spatial Data and Analytical Hierarchy Process Approach: A Case Study in Myanmar

by Tin Myo Thu, Veeranum Songsom, Thongchai Suteerasak and Kyaw Thinn Latt

ISPRS Int. J. Geo-Inf. 2025, 14(12), 460; https://doi.org/10.3390/ijgi14120460 - 24 Nov 2025

Oil spills threaten marine ecosystems and hinder progress toward Sustainable Development Goal (SDG) 14 on ocean conservation and sustainable marine resource use. Coastal ecosystems in Myanmar face growing risks from expanding maritime infrastructure, including ports, special economic zones, and offshore projects. This study [...] Read more.

Oil spills threaten marine ecosystems and hinder progress toward Sustainable Development Goal (SDG) 14 on ocean conservation and sustainable marine resource use. Coastal ecosystems in Myanmar face growing risks from expanding maritime infrastructure, including ports, special economic zones, and offshore projects. This study aims to develop a spatial Environmental Sensitivity Index (ESI) map for the Tanintharyi region by integrating biological, socio-economic, and physical factors. Using the Analytical Hierarchy Process (AHP), weighting values were derived from local conservation and livelihood experts to ensure regional relevance. The inclusion of chlorophyll-a as a biological indicator improves the assessment of marine productivity and ecosystem health, linking ESI mapping to ocean acidification. The results showed that 8% of the area was very highly sensitive, 25% was highly sensitive, and 23% was moderately sensitive. The most sensitive zones were concentrated along the southern coastline, particularly in Thayetchaung Township, due to dense mangroves, critical habitats, and resource-dependent fisheries. This study presents the first spatial ESI assessment for Tanintharyi, providing a practical framework for oil spill preparedness and ecosystem protection, with potential for future enhancement through integration with oil spill simulation modeling. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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