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A Convolutional Autoencoder-Based Method for Vector Curve Data Compression
Do We Care Enough About Child Maltreatment?—Analyzing Social Media Discourse on Child Maltreatment in the United States
From Stars to LETTERS: A Multi-Dimensional, FAIR-Aligned Framework for Geospatial Metadata Quality Evaluation
Making Participation Tangible: A Methodological Reflection on the Potentials and Limitations of Immersive Virtual Reality, Electrodermal Activity Measurement, and Qualitative Inquiry in the Analysis of Urban Fear Spaces

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, 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 34.9 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2026).
Rejection Rate: a rejection rate of 74% in 2025.
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: 3.2 (2025); 5-Year Impact Factor: 3.5 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2220-9964

Latest Articles

21 pages, 2478 KB

Open AccessArticle

Population-Level Route Diversity Varies with Origin–Destination Structure: Evidence from Mobile Phone Signaling Data in Shenzhen, China

by Decun Wu and He Yang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 395; https://doi.org/10.3390/ijgi15090395 (registering DOI) - 30 Aug 2026

Route-choice research has treated route diversity mainly as an individual behavioral property, but population-scale route signatures make it possible to examine diversity as an attribute of origin–destination (OD) flows. Using September 2025 mobile phone signaling data from the DaaS BI platform (Zhihuizuji, China [...] Read more.

Route-choice research has treated route diversity mainly as an individual behavioral property, but population-scale route signatures make it possible to examine diversity as an attribute of origin–destination (OD) flows. Using September 2025 mobile phone signaling data from the DaaS BI platform (Zhihuizuji, China Unicom), we analyze map-matched route signatures for 1.61 million stable commuters in Shenzhen, China. The primary outcome is the route diversity index (RDI), computed as the ratio of distinct route signatures to total route observations within an analytical unit. OD flows whose endpoints occupy the same geohash-7 cell are more diverse than flows whose endpoints occupy different cells (RDI = 0.83 vs. 0.49; Cohen’s d = 1.61), and route diversity follows a nonlinear distance profile, with the lowest values in the 5–10 km range. The full OD-level model achieves high in-sample fit, while a model without the endpoint-grid indicator has nearly the same fit. The near-equivalent fits are consistent with shared information among flow scale, distance, and OD structure rather than being unique contributions from the endpoint-grid indicator. Social gradients are present but smaller than the OD contrast: the same-cell vs. different-cell gap is 1.6 times the full age-gradient range and 5.4 times the ARPU range. The local road-node density measure adds little independent information after OD-level controls. The largest observed differences occur at the OD-pair level. All the results refer to DaaS-derived, map-matched route signatures, not to direct observations of every trip. Full article

22 pages, 3909 KB

Open AccessArticle

Spatial Inequality in School Accessibility: Evidence from Hubei Province, China

by Hua Shu, Yansen Zhao, Houxie Zhao, Jintao Yang, Jing Wang and Fanghe Zhao

ISPRS Int. J. Geo-Inf. 2026, 15(9), 394; https://doi.org/10.3390/ijgi15090394 (registering DOI) - 29 Aug 2026

Educational accessibility plays a central role in achieving spatial equity, yet most existing studies remain confined to single-municipality analyses and are heavily concentrated on highly urbanized metropolitan areas, offering little understanding of how different urban, peri-urban, and rural settings shape access to basic [...] Read more.

Educational accessibility plays a central role in achieving spatial equity, yet most existing studies remain confined to single-municipality analyses and are heavily concentrated on highly urbanized metropolitan areas, offering little understanding of how different urban, peri-urban, and rural settings shape access to basic education. This study provides a province-wide grid-based assessment of school accessibility across Hubei, China. The accessibility analysis was conducted at a 1 km grid resolution, while the distribution of school-age population was incorporated later to evaluate inequality. Municipalities are compared using two complementary indicators: population-weighted travel distance and the Gini coefficient, capturing both accessibility levels and spatial equity. Results reveal strong spatial heterogeneity: the eastern and central plains have much shorter school travel distances than the western mountainous region. Notably, some highly urbanized municipalities achieve very short average travel distances but also display substantial internal inequalities. A two-dimensional efficiency–equity typology further highlights four distinct municipal patterns, from high-quality accessibility to dual disadvantage. Geodetector analysis identifies the key socio-economic factors shaping these spatial imbalances. These findings underscore the need for differentiated policy strategies responding to the unique spatial and socio-economic conditions of each municipality. Full article

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26 pages, 1809 KB

Open AccessArticle

From Basemap to Color Palette: A Machine-Learning Approach to Context-Adaptive Map Design

by Jule Drews, Julian Keil, Frank Dickmann and Dennis Edler

ISPRS Int. J. Geo-Inf. 2026, 15(9), 393; https://doi.org/10.3390/ijgi15090393 (registering DOI) - 28 Aug 2026

The choice of suitable color palettes is a central task in thematic cartography. Established palette libraries such as ColorBrewer 2.0 provide cartographically proven color schemes, but they account only to a limited extent for the specific visual context of a background map. Particularly [...] Read more.

The choice of suitable color palettes is a central task in thematic cartography. Established palette libraries such as ColorBrewer 2.0 provide cartographically proven color schemes, but they account only to a limited extent for the specific visual context of a background map. Particularly in web-based mapping environments, basemaps vary widely in lightness, colorfulness, texture, and semantic density. This paper introduces CartoPalette v4.2, a basemap-dependent tool for generating thematic color palettes. The system combines a trained Conditional Variational Autoencoder model (CVAE) with explicit scoring, minimum domain checks, constrained reranking, and optional CIELAB-color-space-based repair. The goal is not the automation of cartographic design, but a transparent, traceable decision aid. The benchmark comprised 40 basemaps from held-out test locations, drawn from the ten map styles included in the training data, together with two palette types and four class counts. In this setting, 90% of CartoPalette’s suggestions scored higher than the corresponding best available ColorBrewer palette. In about 83% of cases, the visual contrast to the background map was greater than with ColorBrewer. The results show that explicitly incorporating the basemap context offers a clear added value over classical palette libraries. These findings therefore refer to previously unseen locations within basemap styles represented during training. Transfer to entirely new styles has not yet been evaluated. Full article

29 pages, 6958 KB

Open AccessArticle

Building Community Discovery by Integrating Spatial-Cognitive Knowledge with Graph Representation Learning

by Zhiruo Zhao, Tinghua Ai, Xiang Ding and Hao Wu

ISPRS Int. J. Geo-Inf. 2026, 15(9), 392; https://doi.org/10.3390/ijgi15090392 (registering DOI) - 28 Aug 2026

Building community discovery aims to identify spatially continuous and cognitively coherent groups of buildings that represent meaningful urban spatial structures. Existing methods mainly integrate geometric similarity, spatial proximity, and topological adjacency with handcrafted grouping rules or supervised learning but rarely use spatial-cognitive knowledge [...] Read more.

Building community discovery aims to identify spatially continuous and cognitively coherent groups of buildings that represent meaningful urban spatial structures. Existing methods mainly integrate geometric similarity, spatial proximity, and topological adjacency with handcrafted grouping rules or supervised learning but rarely use spatial-cognitive knowledge to guide the learning process, limiting the ability to distinguish ordinary geometric adjacency from cognitively meaningful building associations. To address this limitation, this study proposes a knowledge-guided graph representation learning framework for building community discovery. First, a spatial-cognitive building knowledge graph is constructed by integrating building attributes, spatial relationships, and cognitive organization principles. Then, spatial-cognitive knowledge is quantified as cognitive affinities between neighboring buildings and incorporated into a graph neural network model to guide self-supervised graph representation learning. Finally, the learned node embeddings are used to identify building communities through graph-based community discovery. Experimental results across 50 cities showed that the proposed method produces building communities with good spatial continuity and cognitive consistency. Comparative analysis and ablation studies further confirm the effectiveness of integrating spatial-cognitive knowledge with graph representation learning. Full article

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

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25 pages, 21293 KB

Open AccessArticle

A Study on the Development and Future Directions of the National Digital Twin: A Case Study of Korea

by Byeongsun Kim

ISPRS Int. J. Geo-Inf. 2026, 15(9), 391; https://doi.org/10.3390/ijgi15090391 - 28 Aug 2026

The rapid advancement of digital twin technologies has increased the importance of establishing interoperable geospatial information standards for national-scale Digital Twin implementation. The Republic of Korea has developed the National Digital Twin (NDT) standard framework based primarily on OGC CityGML 3.0. This study [...] Read more.

The rapid advancement of digital twin technologies has increased the importance of establishing interoperable geospatial information standards for national-scale Digital Twin implementation. The Republic of Korea has developed the National Digital Twin (NDT) standard framework based primarily on OGC CityGML 3.0. This study presents a case-study-based analysis of the development and application of the Korean NDT standards, focusing on how a geospatial standard framework can be structured and extended to support a national-scale Digital Twin. A comparative review of international initiatives indicates that existing approaches generally develop CityGML-based 3D city models as geospatial foundations and subsequently extend them toward Digital Twin applications, whereas the Korean NDT establishes a common reference framework and geospatial data models as a standardized foundation for national-scale Digital Twin implementation. The analysis examines the phased development process, the hierarchical relationship among the reference framework, core data model, and application data models, and the organization of multiple spatial domains, including Building, Transportation, Terrain, Indoor Space, and Underground. The applicability of the NDT standards was further examined through pilot dataset implementation and validation from schema, semantic, geometric, and topological perspectives. The analysis also identifies current limitations, including restricted data openness, limited software support for CityGML 3.0, and the need for further mechanisms to integrate dynamic information. Based on these findings, this study discusses future directions for NDT standardization, including model management, standardized data production and validation, dynamic information integration, and technical implementation guidance. The Korean experience provides a case for understanding both the potential and practical challenges of applying CityGML 3.0-based geospatial standards to national-scale Digital Twin initiatives. Full article

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

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1 pages, 134 KB

Open AccessCorrection

Correction: Wang et al. Identifying Spatial Determinants of Rice Yields in Main Producing Areas of China Using Geospatial Machine Learning. ISPRS Int. J. Geo-Inf. 2024, 13, 76

by Qingyan Wang, Longzhi Sun and Xuan Yang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 390; https://doi.org/10.3390/ijgi15090390 - 28 Aug 2026

In the original publication [...] Full article

8 pages, 193 KB

Open AccessEditorial

Continued Advances in AI-Driven Geospatial Analysis and Data Generation

by Levente Juhász, Hartwig H. Hochmair and Hao Li

ISPRS Int. J. Geo-Inf. 2026, 15(9), 389; https://doi.org/10.3390/ijgi15090389 - 28 Aug 2026

GeoAI has maintained strong momentum since the conclusion of the 2024 Special Issue on “Advances in AI-Driven Geospatial Analytics and Data Generation” [...] Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))

24 pages, 13011 KB

Open AccessArticle

Spatial Mapping of Tourism Elements and Perceptual Characteristics Based on User-Generated Content: Evidence from Northeast China

by Xu Lu, Jinghua Zhang, Shan Huang, Yuhang Sun and Haoming Yi

ISPRS Int. J. Geo-Inf. 2026, 15(9), 388; https://doi.org/10.3390/ijgi15090388 - 27 Aug 2026

The integration of geo-spatial databases, advanced modeling, and artificial intelligence provides novel opportunities to investigate regional differentiation with direct implications for urban science and regional development. To avoid homogeneous competition and support regional tourism planning, it is important to examine how tourists perceive [...] Read more.

The integration of geo-spatial databases, advanced modeling, and artificial intelligence provides novel opportunities to investigate regional differentiation with direct implications for urban science and regional development. To avoid homogeneous competition and support regional tourism planning, it is important to examine how tourists perceive different tourism elements across large regional spaces. However, existing studies still lack a systematic workflow for extracting destination-specific perceptual characteristics and linking them to specific tourism locations. This study develops a UGC-based framework to identify tourism elements and perceptual characteristics, construct POI-level perceptual weights, and map the spatial patterns of tourism perception across Northeast China using 196,028 Ctrip reviews from 1491 POIs. The framework integrates jieba segmentation with BERT ranking for keyword extraction, ChatGPT-assisted filtering and manual review for keyword standardization, K-means clustering for perceptual dimension identification, and GIS-based kernel density analysis for spatial visualization. The main findings are as follows. First, tourism perception in Northeast China presented a diversified composite structure. Second, different perceptual dimensions and representative keywords showed differentiated spatial distributions. Third, the spatial structure of tourism perception in Northeast China was summarized as a composite spatial pattern centered on the Five-Dimensional Composite Perception Core Belt, supported by the Natural-Landscape Perception Belt and the Water Recreation Perception Area, and supplemented by multi-level urban nodes and inter-node perceptual linkages. This study provides methodological support and planning implications for understanding regional tourism differentiation and guiding evidence-based destination planning. The construction of geo-spatial databases plays a fundamental role in linking tourists’ perceptions to specific tourism elements and locations, enabling fine-grained and spatially explicit analysis that is difficult to achieve using conventional survey-based data. 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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26 pages, 46379 KB

Open AccessArticle

Adaptive Multi-Level Scale Segmentation Method for Detecting Land and Marine Coastal Aquaculture Areas

by Xuzhe Yan, Li Zhang, Jingjuan Liao, Bowei Chen, Yingwen Hu, Jian Zuo, Qing Zhang and Liujie He

ISPRS Int. J. Geo-Inf. 2026, 15(9), 387; https://doi.org/10.3390/ijgi15090387 - 27 Aug 2026

Coastal zones provide favorable geographical conditions for aquaculture. Accurate information on land and marine aquaculture (LMA) activities is essential for managing their spatial distribution and estimating blue-food production. However, complex coastal landscapes and the heterogeneity of aquaculture facilities make it difficult to extract [...] Read more.

Coastal zones provide favorable geographical conditions for aquaculture. Accurate information on land and marine aquaculture (LMA) activities is essential for managing their spatial distribution and estimating blue-food production. However, complex coastal landscapes and the heterogeneity of aquaculture facilities make it difficult to extract LMA areas accurately and completely at large scales. To address this challenge, we developed an adaptive multi-level scale segmentation (AMLSS) method for the precise extraction of LMA areas and modified a radar dual-polarization water index to reduce interference from the seawater background. We then constructed a class-specific hierarchical decision-tree model using multi-source features to map LMA areas at the national scale. AMLSS increased extraction accuracy by 19.7% for pond aquaculture and 22.9% for marine aquaculture compared with single-scale segmentation and was particularly effective in northern China. In 2022, landward pond and marine cage/raft aquaculture within China’s coastal zone covered approximately 7237 km² and 1152 km², respectively; the resulting map achieved an overall accuracy of 90.8%. This study provides a methodological basis for the long-term monitoring and spatially explicit mapping of LMA areas. Full article

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

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26 pages, 29387 KB

Open AccessArticle

Bridging the Gap: Competition-Aware MH3SFCA Mapping of Dental Care Accessibility in Germany

by Sebastian Völker, Boris Kauhl, Tim Johansson and Antje van der Zee-Neuen

ISPRS Int. J. Geo-Inf. 2026, 15(9), 386; https://doi.org/10.3390/ijgi15090386 - 26 Aug 2026

Equitable access to dental care across Germany remains constrained by geographic barriers, yet traditional provider-to-population ratios fail to capture spatial complexity. This study employed a Modified Huff Three-Step Floating Catchment Area (MH3SFCA) method to quantify dental care accessibility at the ZIP-code level across [...] Read more.

Equitable access to dental care across Germany remains constrained by geographic barriers, yet traditional provider-to-population ratios fail to capture spatial complexity. This study employed a Modified Huff Three-Step Floating Catchment Area (MH3SFCA) method to quantify dental care accessibility at the ZIP-code level across all 8171 German postal code zones, integrating 2022 Census demand data, 38,023 dental facilities, and network-based travel times. Results revealed stark geographic disparities: accessibility exhibited strong positive spatial autocorrelation (Moran’s I = 0.97, p < 0.001), with 22.6% of the population in high-access urban clusters concentrated in Berlin, Hamburg, and the Rhine-Ruhr region, while statistically significant cold spots affected only a small share of the national population but formed geographically persistent clusters in rural and peripheral regions. The Gini coefficient of 0.55 indicated moderate overall inequality, substantially higher than for physician distribution (Gini = 0.19). Regional heterogeneity was prominent: Mecklenburg-Western Pomerania had 74.9% of its population in the lowest accessibility quartile (mean index = 0.7), while Berlin achieved near-universal high access (mean = 15.3). The MH3SFCA framework provides German planning bodies with a dynamic tool for routine accessibility monitoring and scenario analysis, supporting targeted, place-based workforce interventions that move beyond static mapping toward more equitable distribution of dental care. Full article

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

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29 pages, 3701 KB

Open AccessArticle

Geometry Guided Adaptive Ray-Surface Intersection for LiDAR Camera Fusion Spatial Localization

by Zishuo Lian, Jun Wu and Honglin Chen

ISPRS Int. J. Geo-Inf. 2026, 15(9), 385; https://doi.org/10.3390/ijgi15090385 - 26 Aug 2026

In LiDAR-camera fusion measurement, conventional nearest-neighbor back-projection methods are limited by discrete point-cloud sampling and struggle to accurately recover the spatial position corresponding to image measurement points under sparse point-cloud conditions. Moreover, targets in complex industrial environments often exhibit diverse local geometric structures, [...] Read more.

In LiDAR-camera fusion measurement, conventional nearest-neighbor back-projection methods are limited by discrete point-cloud sampling and struggle to accurately recover the spatial position corresponding to image measurement points under sparse point-cloud conditions. Moreover, targets in complex industrial environments often exhibit diverse local geometric structures, making it difficult for a single surface model to effectively describe different surface characteristics. This paper proposes a Geometry-Guided Adaptive Ray-Surface Intersection measurement method, which replaces the traditional image-to-discrete-point mapping with ray-to-continuous-surface intersection. A spatial viewing ray is constructed from the image measurement point and camera optical center, and local neighborhood points are extracted through nearest projected-point back-projection. Based on curvature and normal-vector statistical features, local surfaces are classified into four geometric categories, and corresponding surface models are adaptively constructed using plane fitting, curved surface fitting, voxel-assisted plane searching, or bilinear patch fitting. The three-dimensional coordinates are then obtained through ray-model intersection. Experiments were conducted on multiple target types in an industrial equipment warehouse over a range of 5–20 m. The results show that the proposed method reduces the average measurement error from 5.63 cm of the nearest-neighbor method to 1.81 cm. When the point-cloud density decreases to 25%, the average absolute error increases by only 26.5%, compared with 92.6% and 58.7% for the nearest-neighbor and interpolation methods, respectively. These results demonstrate the effectiveness and robustness of the proposed method for LiDAR-camera fusion measurement under sparse point-cloud conditions. Full article

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

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21 pages, 3124 KB

Open AccessArticle

Geographic Access and School Participation in Tanzania

by Farhiya Jarso, William C. Smith and Gary R. Watmough

ISPRS Int. J. Geo-Inf. 2026, 15(9), 384; https://doi.org/10.3390/ijgi15090384 - 25 Aug 2026

Over 200 million of the world’s 273 million out-of-school children live in Central and Southern Asia or Sub-Saharan Africa, where distance to school is a persistent barrier, and walking remains the dominant travel mode. Tanzania presents a critical case: over 300,000 children dropped [...] Read more.

Over 200 million of the world’s 273 million out-of-school children live in Central and Southern Asia or Sub-Saharan Africa, where distance to school is a persistent barrier, and walking remains the dominant travel mode. Tanzania presents a critical case: over 300,000 children dropped out before completing primary school in 2023, and only 8.5% complete upper-secondary education. We estimated walking travel time to the nearest primary and secondary school using a 100-metre resolution cost-surface that incorporated land cover, topography, and road networks. Travel time surfaces were linked to Demographic and Health Survey (DHS) data and analysed using multilevel Poisson regression for primary school-age children (aged 7–13) and single-level models for secondary school-age adolescents (aged 14–19). Among primary school-age children (n = 9213), poor geographic access (walking time > 45 min) was associated with a 27.9% higher prevalence of non-participation after socioeconomic adjustment, consistent across wealth quintiles and maternal education groups. Among secondary school-age adolescents (n = 2585), poor access was not significant. Household wealth and maternal education showed strong protective relationships at both levels. Each additional 10 min of walking travel time was associated with a 2.9% higher prevalence of non-participation, equivalent to approximately 18% per additional hour. Geographic access is an independent barrier to primary school-age participation irrespective of socioeconomic position, as there was approximately a 2.9% higher prevalence of non-participation in school for every additional 10 min of travel time, supporting geographically targeted infrastructure investment at primary level and demand-side interventions at secondary level. Full article

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47 pages, 25367 KB

Open AccessArticle

Exploring the Nonlinear Response Patterns and Interaction Effects of Urban Resilience Using the XGBoost-SHAP Model: Evidence from the Yangtze River Economic Belt

by Shasha Li, Yi Du, Zhengjia Chen, Wenjing Li, Dewen Wu and Guofang Zhai

ISPRS Int. J. Geo-Inf. 2026, 15(9), 383; https://doi.org/10.3390/ijgi15090383 - 25 Aug 2026

Urban resilience (UR) is shaped by multiple interacting factors, yet existing studies have paid limited attention to the differentiated nonlinear and interactive mechanisms across different resilience dimensions. Based on panel data from 110 prefecture-level and above cities in the Yangtze River Economic Belt [...] Read more.

Urban resilience (UR) is shaped by multiple interacting factors, yet existing studies have paid limited attention to the differentiated nonlinear and interactive mechanisms across different resilience dimensions. Based on panel data from 110 prefecture-level and above cities in the Yangtze River Economic Belt (YREB) from 2015 to 2024, this study adopts a four-dimensional framework covering social, economic, ecological, and infrastructure resilience. Spatial analysis and the XGBoost-SHAP model are combined to examine the spatiotemporal evolution, key drivers, nonlinear responses, turning point characteristics, and factor interactions of overall UR and its four dimensions. The results show that overall UR increased steadily, while the four dimensions followed distinct evolutionary trajectories: economic and infrastructure resilience improved most rapidly, ecological resilience increased steadily, and social resilience improved relatively slowly. The importance and effects of key drivers also varied across overall resilience and its four dimensions. Major factors, including technological investment, non-registered population, unemployment insurance coverage, land use, and natural population growth, exhibited distinct nonlinear responses and turning point characteristics, with their effects changing across different variable levels. Furthermore, significant interactions were identified among population and industrial structure, population and transportation, industry and technology, and industry and social security across different resilience dimensions. Overall, UR in the YREB is characterized by differentiated development, nonlinear responses, nonlinear transition patterns, and joint influences of multiple factors. Full article

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

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30 pages, 13337 KB

Open AccessArticle

3D Urban Structural Morphology at the Block Scale: A Geometric Vector Framework Based on Mean Building Volume, Height Variation Coefficient, and Mean Sky View Factor

by Rossana Estanqueiro, Carla Rebelo and José António Tenedório

ISPRS Int. J. Geo-Inf. 2026, 15(9), 382; https://doi.org/10.3390/ijgi15090382 - 25 Aug 2026

Three-dimensional urban morphology has gained renewed analytical relevance as advances in remote sensing, photogrammetry, and geospatial data processing have made it possible to derive 3D data and high-resolution digital surface and elevation models. Block-scale morphological analysis remains predominantly two-dimensional, leaving volumetric scale, vertical [...] Read more.

Three-dimensional urban morphology has gained renewed analytical relevance as advances in remote sensing, photogrammetry, and geospatial data processing have made it possible to derive 3D data and high-resolution digital surface and elevation models. Block-scale morphological analysis remains predominantly two-dimensional, leaving volumetric scale, vertical heterogeneity, and spatial aperture undetectable, while existing 3D approaches treat building metrics as isolated variables or rely on costly semantic city models. This study proposes a parsimonious geometric vector framework in which each urban block is represented as a vector of three conceptually distinct dimensions: Mean Building Volume (MBV, volumetric scale), Height Variation Coefficient (HVC, vertical heterogeneity), and Mean Sky View Factor (SVF, spatial aperture). The framework was operationalized through reproducible GIS and Python 3.12 workflows using a large-scale topographic dataset and a LiDAR-derived Digital Surface Model, and it was tested through K-means clustering of 816 statistical blocks in the municipality of Amadora, Portugal. The results confirm the near-orthogonality of the three dimensions (correlations |r| < 0.25; Variance Inflation Factors ≤ 1.08) and identify five structurally distinct typologies ordered along a volumetric gradient (MBV), in which HVC is the principal differentiating dimension among Types 1–4, and SVF distinguishes Type 5; the partition remains stable under 500-iteration consensus clustering (Adjusted Rand Index = 0.9445). The contribution of this study lies in the transferability of the analytical pipeline rather than of the typology itself, which is specific to Amadora and would require independent derivation elsewhere. The framework provides urban planners with a parsimonious, geometrically transparent, and operationally feasible tool for classifying urban blocks in 3D, complementing 2D analytical approaches and establishing a morphological basis for future climate-responsive analyses. Full article

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

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28 pages, 16200 KB

Open AccessArticle

A Global Spatio-Temporal Relationship- and Dynamic Friendship-Aware POI Recommendation Method

by Xiaoyu Ji, Yibing Cao, Jiangshui Zhang, Minjie Chen, Pengyu Cui and Yuan Yang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 381; https://doi.org/10.3390/ijgi15090381 - 25 Aug 2026

Point-of-interest (POI) recommendation in location-based social networks (LBSNs) predicts a user’s next visit using check-in records. As critical factors influencing user decision making, geographic and social contexts are incorporated to deliver higher-quality recommendation services. However, current methods suffer from two limitations: geographical modeling [...] Read more.

Point-of-interest (POI) recommendation in location-based social networks (LBSNs) predicts a user’s next visit using check-in records. As critical factors influencing user decision making, geographic and social contexts are incorporated to deliver higher-quality recommendation services. However, current methods suffer from two limitations: geographical modeling is confined to distance thresholds, ignoring long-range spatio-temporal transitions, and social graphs remain static, failing to capture dynamic behavioral similarities among unconnected users. To address these gaps, we propose GSTRDFA (Global Spatio-Temporal Relationship- and Dynamic Friendship-Aware), a model comprising three layers. First, we construct spatio-temporal KGs (STKGs) that encode four relationship types: global spatio-temporal and local geospatial POI–POI links, dynamic user–user friendships, and static social ties. Second, four dedicated encoders—STSEncoder (spatio-temporal state embedding), GeoEncoder (geographical convolution), DFEncoder (graph attention network), and SocEncoder (GraphSAGE)—propagate and aggregate user and POI embeddings along these STKG relations. Third, a GRU-based sequence predictor uses the fused embeddings to match candidate POIs to the user. Evaluations on Foursquare datasets (NYC, JK, CA) show that GSTRDFA outperforms existing methods, improving Acc@1/5/10 and MRR by 0.24–3.31%. Key contributions include (1) unifying spatial, temporal, and dynamic social signals via STKGs; (2) jointly modeling global spatio-temporal transitions and dynamic friendships; and (3) enabling balanced short-/long-range and short-/long-term transition prediction. Full article

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

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20 pages, 2610 KB

Open AccessArticle

UVP-LIO: Uncertainty-Aware Voxel-Plane Mapping for Robust LiDAR-Inertial Odometry

by Yifan Li, Shitong Du, Lizhao Fu, Shuang Li, Zihan Yang and Baoguo Yu

ISPRS Int. J. Geo-Inf. 2026, 15(9), 380; https://doi.org/10.3390/ijgi15090380 - 25 Aug 2026

LiDAR SLAM relies on reliable geometric constraints to estimate sensor motion and maintain consistent maps in complex three-dimensional environments. Planar features are commonly used for LiDAR registration, but repeatedly fitting local planes from neighboring points brings extra computation and may be sensitive to [...] Read more.

LiDAR SLAM relies on reliable geometric constraints to estimate sensor motion and maintain consistent maps in complex three-dimensional environments. Planar features are commonly used for LiDAR registration, but repeatedly fitting local planes from neighboring points brings extra computation and may be sensitive to noisy observations. Voxel-plane maps address this issue by storing planar structures in voxel cells, yet most existing methods still construct planes and assign points to voxels according to the nominal point coordinates. When LiDAR measurement noise and pose prediction errors are ignored, plane parameters may be biased and points may be associated with unsuitable voxels. This paper presents UVP-LIO, an uncertainty-aware voxel-plane mapping method for LiDAR-inertial odometry. The measurement uncertainty of each LiDAR point and the uncertainty from state estimation are jointly propagated to the world frame to obtain a point-wise covariance. This covariance is then used in uncertainty-aware voxel association and covariance-weighted incremental plane updating. Plane thickness is further introduced to weight point-to-plane residuals during registration. Experiments in a LiDAR-only configuration on KITTI and in a LiDAR-inertial configuration on M3DGR show that UVP-LIO improves trajectory consistency and mapping robustness, especially in scenes with weak or ambiguous geometric constraints, while maintaining real-time performance. Full article

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

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26 pages, 28365 KB

Open AccessArticle

Explaining Intra-Urban Spatial Interaction with Theory-Informed Interpretable Machine Learning: Nonlinear Contributions of Complementarity, Intervening Opportunities, and Transferability

by Shouzhi Chang, Minhua Dong, Boyu Hou, Fusheng Liu and Yangming Huang

ISPRS Int. J. Geo-Inf. 2026, 15(9), 379; https://doi.org/10.3390/ijgi15090379 - 25 Aug 2026

Understanding intra-urban spatial interaction is essential for context-sensitive urban planning. While classical spatial interaction theories provide strong conceptual foundations, systematically translating these theoretical concepts into quantifiable indicators remains a significant methodological challenge. Furthermore, capturing the complex, non-linear dynamics driving urban mobility requires analytical [...] Read more.

Understanding intra-urban spatial interaction is essential for context-sensitive urban planning. While classical spatial interaction theories provide strong conceptual foundations, systematically translating these theoretical concepts into quantifiable indicators remains a significant methodological challenge. Furthermore, capturing the complex, non-linear dynamics driving urban mobility requires analytical approaches that balance predictive power with interpretability. To address this gap, this study develops a feasible, theory-informed analytical framework that bridges classical spatial interaction theory with interpretable machine learning to quantify the predictive patterns underlying intra-urban mobility. In a case study of Changchun, China, Ullman’s three core concepts, together with fundamental measures of urban scale, were systematically operationalized as a set of quantitative proxy variables based on multi-source geospatial big data. XGBoost was then used to model grid-level origin-destination flows at multiple spatial resolutions, and the SHapley Additive exPlanations (SHAP) was used to assess the contributions and dependence patterns of the theory-driven indicators. The results demonstrate the framework’s predictive robustness, with the XGBoost model consistently outperforms the traditional parametric benchmark across all evaluated spatial resolutions. The 1000 m resolution provided the best balance between predictive performance and spatial detail, yielding an R² of 0.696, compared with 0.611 for the benchmark. The explanatory analysis indicates that functional complementarity is the most critical predictive dimension overall. It also identifies distinct nonlinear patterns, including negative associations between transfer impedance and predicted mobility flows beyond critical thresholds, positive associations between built-environment scale indicators and predicted flows only above minimum intensity thresholds, and diminishing marginal associations between intervening opportunities and predicted flows. This study provides a scalable and transferable approach for diagnosing spatial interactions in data-rich urban contexts, providing an empirical basis for calibrating future micro-level urban simulations. Full article

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20 pages, 15907 KB

Open AccessArticle

Dynamic Evaluation of Fire Service Accessibility for Fireworks Manufacturers

by Dingli Liu, Wentao Zhao, Feiyue Wang, Yan Tang and Long Yan

ISPRS Int. J. Geo-Inf. 2026, 15(9), 378; https://doi.org/10.3390/ijgi15090378 - 24 Aug 2026

Although fire service accessibility has been extensively studied for urban public facilities such as hospitals and parks, it remains largely unexplored for high-risk industrial facilities such as fireworks manufacturers. In this study, fire risk and transportation network conditions are jointly considered, and a [...] Read more.

Although fire service accessibility has been extensively studied for urban public facilities such as hospitals and parks, it remains largely unexplored for high-risk industrial facilities such as fireworks manufacturers. In this study, fire risk and transportation network conditions are jointly considered, and a dynamic risk-weighted fire service accessibility evaluation framework is developed. An empirical study was conducted using 35 fire stations and 410 fireworks manufacturers in Liuyang City, China. Fire truck travel times were simulated via online map application programming interfaces (APIs) at 10–30 min intervals over a continuous three-day period, generating 193 evaluation scenarios and 158,260 data samples. The results indicate that the total average travel time of different types of fire trucks to demand points with varying risk levels was found to range from 570.33 to 1362.18 s, and risk-weighted fire service accessibility ranges from 16.84 to 57.26, reflecting a generally poor level of fire service accessibility within the current transport network. To resolve these constraints, it is recommended to optimize the spatial configuration of micro fire stations, promote the collaborative planning of shared enterprise-based fire units, and cross-integrate accessibility metrics into regional industrial land-use planning. These spatial measures are expected to significantly enhance the spatial resilience of transportation systems serving peripheral high-risk industrial clusters. Full article

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26 pages, 2508 KB

Open AccessArticle

Cluster-Based Strategic Planning and Capacity Optimization of Assembly Areas in Disaster Management

by Mesut Ulu

ISPRS Int. J. Geo-Inf. 2026, 15(9), 377; https://doi.org/10.3390/ijgi15090377 - 24 Aug 2026

Planning emergency assembly areas, which are vital in the first hours after a disaster, is a critical decision problem, especially in metropolitan areas with high population density and urbanization pressure. This study comprehensively evaluates the capacity adequacy, population pressure, and spatial imbalances of [...] Read more.

Planning emergency assembly areas, which are vital in the first hours after a disaster, is a critical decision problem, especially in metropolitan areas with high population density and urbanization pressure. This study comprehensively evaluates the capacity adequacy, population pressure, and spatial imbalances of existing assembly areas in the Eyüpsultan district of Istanbul. A two-stage hybrid model is proposed: in the first stage, assembly areas are clustered based on geographical proximity using the K-means algorithm; in the second stage, an improvement is implemented for clusters falling below the per capita area target (3 m²/person), based on the reallocation of parks from neighboring clusters with excess capacity through administrative assignment. As a result of trials with different cluster numbers, the five-cluster configuration was selected as the preferred solution when the Silhouette score (0.568), minimum per capita area (3.21 m²), and spatial continuity criteria were evaluated together. Thanks to the proposed hybrid model, the per capita space in all clusters has been kept above the 3 m² threshold. These findings demonstrate that capacity gaps can be addressed solely through the rational optimization of service assignments, without increasing the urban open space stock. Consequently, the proposed approach offers a more equitable, accessible, capacity-balanced decision-support structure for disaster management for local governments. 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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31 pages, 23750 KB

Open AccessArticle

Spatial Allocation of Elderly Care Resources in High-Density Urban Areas Under the Guidance of Efficiency and Equity

by Siyu Zhao, Shaohua Wang, Haojian Liang, Jingyi Zhou, Hao Wang, Ning Zhang, Chang Liu and Hong Gao

ISPRS Int. J. Geo-Inf. 2026, 15(8), 376; https://doi.org/10.3390/ijgi15080376 - 21 Aug 2026

Rapid urban population aging and increasing land constraints pose significant challenges for improving both service coverage and spatial equity in elderly care facility planning. This study develops an integrated optimization framework that simultaneously addresses accessibility, efficiency, and equity in the spatial allocation of [...] Read more.

Rapid urban population aging and increasing land constraints pose significant challenges for improving both service coverage and spatial equity in elderly care facility planning. This study develops an integrated optimization framework that simultaneously addresses accessibility, efficiency, and equity in the spatial allocation of elderly care facilities. First, an improved Gaussian Two-Step Floating Catchment Area (G2SFCA) method is employed to evaluate accessibility patterns across multiple facility types under both walking and driving scenarios. Second, resource allocation equity is quantified using Lorenz curves and spatial Gini coefficients to identify mismatches between elderly care supply and population demand. Building upon these analyses, a fairness-oriented maximum covering location model—termed the Equity Maximum Covering Location Problem (EMCLP)—is formulated and further transformed into a Markov Decision Process. A deep reinforcement learning-based algorithm is subsequently designed to solve the EMCLP under complex spatial constraints. Experiment results demonstrate that the proposed approach achieves improved computational efficiency while maintaining robust solution quality, and effectively enhances service provision in underserved areas through differentiated functional allocation strategies. 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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