ISPRS International Journal of Geo-Information

28 pages, 11439 KB

Open AccessArticle

Multi-Scale Quantitative Direction-Relation Matrix for Cardinal Directions

by Xuehua Tang, Mei-Po Kwan, Yong Zhang, Yang Yu, Linxuan Xie, Kun Qin and Binbin Lu

ISPRS Int. J. Geo-Inf. 2026, 15(1), 11; https://doi.org/10.3390/ijgi15010011 (registering DOI) - 25 Dec 2025

Existing qualitative direction-relation matrix models employ rigid classification schemes, limiting their ability to differentiate directional relationships between multiple targets within the same directional tile. This paper proposes two quantitative matrix models for qualitative direction-relation with differing levels of precision. Based on directional tile [...] Read more.

Existing qualitative direction-relation matrix models employ rigid classification schemes, limiting their ability to differentiate directional relationships between multiple targets within the same directional tile. This paper proposes two quantitative matrix models for qualitative direction-relation with differing levels of precision. Based on directional tile partitioning derived from qualitative direction-relation models, the new models achieve quantitative expression of qualitative directionality through two distinct descriptive parameters: order and coordinate. The order matrix utilizes angular and displacement measurements as sequential variables, capturing the directional sequence characteristics within the same directional tile. The coordinate matrix employs direction-relation coordinates as matrix elements, integrating directional and distance relationships to identify the distribution of targets at varying distances along the same line of sight. These two novel models operate at distinct scales and achieve soft classification of directional relationships, substantially enhancing descriptive precision. Furthermore, they serve as foundational quantitative frameworks for the qualitative direction-relation models, establishing a bridge between quantitative and qualitative models. Experimental assessment confirms that the new models substantially improve directional relationship precision through their quantitative elements while supporting various application domains. Full article

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

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25 pages, 1482 KB

Open AccessArticle

Three-Dimensional Reconstruction of Indoor Building Components Based on Multi-Dimensional Primitive Modeling Method

by Jaeyoung Lee, Soomin Kim and Sungchul Hong

ISPRS Int. J. Geo-Inf. 2026, 15(1), 10; https://doi.org/10.3390/ijgi15010010 - 23 Dec 2025

Abstract

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and [...] Read more.

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and accurate manner. However, limitations remain when handling massive point clouds to reconstruct complex indoor structures with varying ceiling and floor heights. This study proposes a semi-automatic 3D model reconstruction method. First, point clouds are aligned with 3D Cartesian axes and the spatial extent of the indoor space is measured. Subsequently, the point clouds are projected onto each coordinate plane to hierarchically extract structural elements of a building component, such as boundary lines, rectangles, and cuboids. Boolean operations are then applied to the cuboids to reconstruct a 3D wireframe model. Additionally, wall points are segmented to identify openings like doors and windows. For validation, the method was applied to three typical building components with Manhattan-world structures: an office, a hallway, and a stairway. The reconstructed models were evaluated using reference points, resulting in positional accuracies of 0.033 m, 0.034 m, and 0.030 m, respectively. Finally, the resulting wireframe model served as a reference to build an as-built BIM model. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

15 pages, 10432 KB

Open AccessArticle

A Monte-Carlo-Based Method for Probabilistic Permafrost Mapping Across Northeast China During 2003 to 2022

by Yao Xiao, Lei Zhao, Shuqi Wang, Xuyang Wu, Kai Gao and Yunhu Shang

ISPRS Int. J. Geo-Inf. 2026, 15(1), 9; https://doi.org/10.3390/ijgi15010009 (registering DOI) - 22 Dec 2025

Abstract

Permafrost degradation under climate warming has profound implications for ecological processes, hydrology, and human activities. Northeast China, characterized by sporadic and isolated patch permafrost near the southern limit of latitudinal permafrost (SLLP), represents one of the most sensitive and complex permafrost regions. This [...] Read more.

Permafrost degradation under climate warming has profound implications for ecological processes, hydrology, and human activities. Northeast China, characterized by sporadic and isolated patch permafrost near the southern limit of latitudinal permafrost (SLLP), represents one of the most sensitive and complex permafrost regions. This study aims to improve the reliability of permafrost mapping by incorporating parameter uncertainty into simulations. We developed a Monte Carlo–Temperature at the Top of Permafrost (TTOP) (MC–TTOP) framework that integrates an equilibrium model with Monte Carlo sampling to quantify parameter sensitivity and model uncertainty. Using all-sky daily air temperature data and land use and land cover information, we generated probabilistic estimates of mean annual ground temperature (MAGT), permafrost occurrence probability (PZI), and associated uncertainties. Model validation against borehole observations demonstrated improved accuracy compared with global-scale simulations, with a reduced bias and RMSE. Results reveal that permafrost in Northeast China was relatively stable during 2003–2010 but experienced pronounced degradation after 2011, with the total area decreasing to ~2.79 × 10⁵ km² by 2022. Spatial uncertainty was greatest in transitional zones near the southern boundary, where PZI-based delineations tended to overestimate permafrost extent. Regional comparisons further showed that permafrost in Northeast China is more fragmented and uncertain than that on the Tibetan Plateau, owing to complex snow–vegetation–topography interactions and intensive human disturbances. Overall, the MC-TTOP simulations indicate an accelerated permafrost degradation after 2011, with the highest uncertainty concentrated in southern transitional zones near the SLLP, demonstrating that the MC-TTOP framework provides a robust tool for probabilistic permafrost mapping, offering improved reliability for regional-scale assessments and important insights for future risk evaluation under climate change. Full article

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

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32 pages, 7858 KB

Open AccessArticle

Domain-Adapted MLLMs for Interpretable Road Traffic Accident Analysis Using Remote Sensing Imagery

by Bing He, Wei He, Qing Chang, Wen Luo and Lingli Xiao

ISPRS Int. J. Geo-Inf. 2026, 15(1), 8; https://doi.org/10.3390/ijgi15010008 (registering DOI) - 21 Dec 2025

Abstract

Traditional road traffic accident analysis has long relied on structured data, making it difficult to integrate high-dimensional heterogeneous information such as remote sensing imagery and leading to an incomplete understanding of accident scene environments. This study proposes a road traffic accident analysis framework [...] Read more.

Traditional road traffic accident analysis has long relied on structured data, making it difficult to integrate high-dimensional heterogeneous information such as remote sensing imagery and leading to an incomplete understanding of accident scene environments. This study proposes a road traffic accident analysis framework based on Multimodal Large Language Models. The approach integrates high-resolution remote sensing imagery with structured accident data through a three-stage progressive training pipeline. Specifically, we fine-tune three open-source vision–language models using Low-Rank Adaptation (LoRA) to sequentially optimize the model’s capabilities in visual environmental description, multi-task accident classification, and Chain-of-Thought (CoT) driven causal reasoning. A multimodal dataset was constructed containing remote sensing image descriptions, accident classification labels, and interpretable reasoning chains. Experimental results show that the fine-tuned model achieved a maximum improvement in the CIDEr score for image description tasks. In the joint classification task of accident severity and duration, the model achieved an accuracy of 71.61% and an F1-score of 0.8473. In the CoT reasoning task, both METEOR and CIDEr scores improved significantly. These results validate the effectiveness of structured reasoning mechanisms in multimodal fusion for transportation applications, providing a feasible path toward interpretable and intelligent analysis for real-world traffic management. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

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23 pages, 3964 KB

Open AccessArticle

TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation

by Yiming Li, Liuwei Lu, Guangming Guo, Luying Na, Xianpu Liang, Peng Su, Qi An and Pengjiang Wang

ISPRS Int. J. Geo-Inf. 2026, 15(1), 7; https://doi.org/10.3390/ijgi15010007 (registering DOI) - 21 Dec 2025

Abstract

To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM [...] Read more.

To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM method integrating instance-level motion classification, temporally adaptive super-pixel segmentation, and optical flow propagation. The system first employs an instance-level motion classifier combining residual flow estimation and a YOLOv8-seg instance segmentation model to distinguish moving objects. Then, temporally adaptive super-pixel segmentation algorithm SLIC (TA-SLIC) is applied to achieve fine-grained dynamic region partitioning. Subsequently, a proposed dynamic region missed-detection correction mechanism based on optical flow propagation (OFP) is used to refine the missed-detection mask, enabling accurate identification and capture of motion regions containing non-rigid local object movements, undefined moving objects, and low-dynamic objects. Finally, dynamic feature points are removed, and valid static features are utilized for pose estimation. The localization accuracy of the visual SLAM system is validated using two widely adopted datasets, TUM and BONN. Experimental results demonstrate that the proposed method effectively suppresses interference from dynamic objects (particularly non-rigid local motions) and significantly enhances both localization accuracy and system robustness in dynamic environments. Full article

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

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25 pages, 5120 KB

Open AccessArticle

Application of a Hybrid CNN-LSTM Model for Groundwater Level Forecasting in Arid Regions: A Case Study from the Tailan River Basin

by Shuting Hu, Mingliang Du, Jiayun Yang, Yankun Liu, Ziyun Tuo and Xiaofei Ma

ISPRS Int. J. Geo-Inf. 2026, 15(1), 6; https://doi.org/10.3390/ijgi15010006 - 21 Dec 2025

Abstract

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction [...] Read more.

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction methods. To address this, a hybrid CNN-LSTM deep learning model is constructed. This model is designed to extract multivariate coupled features and capture temporal dependencies from multi-variable time series data, while simultaneously simulating the nonlinear and delayed responses of aquifers to groundwater abstraction. Specifically, the convolutional neural network (CNN) component extracts the multivariate coupled features of hydro-meteorological driving factors, and the long short-term memory (LSTM) network component models the temporal dependencies in groundwater level fluctuations. This integrated architecture comprehensively represents the combined effects of natural recharge–discharge processes and anthropogenic pumping on the groundwater system. Utilizing monitoring data from 2021 to 2024, the model was trained and tested using a rolling time-series validation strategy. Its performance was benchmarked against traditional models, including the autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN), and standalone LSTM. The results show that the CNN-LSTM model delivers superior performance across diverse hydrogeological conditions: at the upstream well AJC-7, which is dominated by natural recharge and discharge, the Nash–Sutcliffe efficiency (NSE) coefficient reached 0.922; at the downstream well AJC-21, which is subject to intensive pumping, the model maintained a robust NSE of 0.787, significantly outperforming the benchmark models. Further sensitivity analysis reveals an asymmetric response of the model’s predictions to uncertainties in pumping data, highlighting the role of key hydrogeological processes such as delayed drainage from the vadose zone. This study not only confirms the strong applicability of the hybrid deep learning model for groundwater level prediction in data-scarce arid regions but also provides a novel analytical pathway and mechanistic insight into the nonlinear behavior of aquifer systems under significant human influence. 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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24 pages, 6395 KB

Open AccessArticle

Research on Spatiotemporal Dynamic and Driving Mechanism of Urban Real Estate Inventory: Evidence from China

by Ping Zhang, Sidong Zhao, Hua Chen and Jiaoguo Ma

ISPRS Int. J. Geo-Inf. 2026, 15(1), 5; https://doi.org/10.3390/ijgi15010005 - 20 Dec 2025

Abstract

Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical [...] Read more.

Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical value. By employing ESDA, the Boston Matrix, and geographically weighted regression models to analyze 2017–2022 data from 287 Chinese cities, this study reveals a cyclical shift in China’s real estate inventory management—from “destocking” to “restocking”. The underlying drivers have transitioned from policy-led interventions to fundamentals-driven factors, including population dynamics, income levels, and market expectations. China’s real estate inventory and its changes exhibit significant spatiotemporal differentiation and spatial agglomeration patterns, demonstrating a spatial structure characterized by “multiple clustered highlands with peripheral lowlands” led by urban agglomerations. The influencing mechanism of China’s real estate inventory constitutes a complex system shaped by three key dimensions: macro-level drivers, regional differentiation, and structural contradictions. Policymakers should reorient destocking policies from “short-term stimulus” to “long-term coordination”, from “industrial policy” to “spatial policy”, and from addressing market “symptoms” to tackling “root causes”. This study argues that effective destocking policies constitute a systematic engineering challenge, demanding policymakers demonstrate profound analytical depth. They must move beyond simplistic sales metrics and perform multi-dimensional evaluations encompassing economic geography, demographic trends, fiscal systems, and land supply mechanisms. This paradigm shift from “symptom management” to “root cause resolution” and “systemic regulation” is essential for achieving sustainable real estate market development. Full article

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

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38 pages, 1179 KB

Open AccessSystematic Review

Reproducible GIS-Based Evidence for Public Health and Urban Security: A Systematic Mapping and Review

by Washington Ramírez Montalvan, Ibeth Manzano Gallardo, Verónica Defaz Toapanta, Edison Espinosa Gallardo and Lucas Garcés Guayta

ISPRS Int. J. Geo-Inf. 2026, 15(1), 4; https://doi.org/10.3390/ijgi15010004 - 19 Dec 2025

Abstract

Geographic Information Systems (GIS) are increasingly applied to public health and urban security challenges, yet current evidence remains fragmented across methods, disciplines, and regions. This study integrates Systematic Mapping (SM) and Systematic Review (SR) within a unified PICOS–SPICE framework to consolidate existing GIS-based [...] Read more.

Geographic Information Systems (GIS) are increasingly applied to public health and urban security challenges, yet current evidence remains fragmented across methods, disciplines, and regions. This study integrates Systematic Mapping (SM) and Systematic Review (SR) within a unified PICOS–SPICE framework to consolidate existing GIS-based research. From an initial corpus of 7106 records, 65 studies met all methodological and reproducibility criteria. Scientific production shows consistent growth, peaking in 2023, with research concentrated in Asia and North America and limited representation from Africa and South America. Methodologically, the literature is dominated by accessibility assessments and spatial autocorrelation, while advanced analytical models—such as Bayesian inference and machine learning—remain scarce. GIS workflows rely mainly on ArcGIS and QGIS, complemented by open-source tools, including R, Python, and SaTScan. The fused SM + SR pipeline provides a transparent and replicable structure that highlights current strengths in spatial resolution and analytical versatility while revealing persistent gaps in data openness, reproducibility, and global equity. These findings offer a consolidated evidence base to guide future GIS research and support informed decision-making in public health and urban security. Full article

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

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19 pages, 2354 KB

Open AccessArticle

Spatial Patterns and Influencing Factors of Chinese Traditional Villages: A Sustainability Perspective

by Kan Wang, Jianjun Bai, Feng Bao, Feifei Hua, Xing Dang and Na Gu

ISPRS Int. J. Geo-Inf. 2026, 15(1), 3; https://doi.org/10.3390/ijgi15010003 - 19 Dec 2025

Abstract

Traditional villages serve as crucial carriers of natural and cultural heritage worldwide. Current research on traditional villages, however, exhibits several shortcomings. On one hand, existing studies tend to focus solely on spatial patterns while neglecting issues of distributional equity from a sustainability perspective. [...] Read more.

Traditional villages serve as crucial carriers of natural and cultural heritage worldwide. Current research on traditional villages, however, exhibits several shortcomings. On one hand, existing studies tend to focus solely on spatial patterns while neglecting issues of distributional equity from a sustainability perspective. On the other hand, few studies have explored the underlying spatial and non-spatial characteristics influencing the distribution of traditional villages through multidimensional factors. To address these gaps, this study selects 8171 Chinese traditional villages as research subjects. Utilizing spatial analysis of GIS, spatial econometrics, and statistical methods, we first analyze the spatial pattern of traditional villages, then assess distributional equity of traditional villages from a sustainability perspective. Finally, we investigate the influence of six multidimensional factors on their distribution and the potential characteristics of these influences. The findings are as follows: (1) Traditional villages in China form three high-density cores, with distribution density significantly higher in the eastern and central regions compared to the western and northeastern regions. The western and northeastern regions exhibit notable low–low clustering. (2) Equity analysis reveals a Gini coefficient of 0.525 for accessibility, indicating notable spatial deprivation. There is also evidence of social inequity, reflected in the deprivation of aging populations by non-aging groups. (3) Except for population density, factors such as elevation and annual precipitation significantly influence the distribution of traditional villages, with effects varying regionally. Quantile regression further confirms that the six factors exert heterogeneous impacts depending on village density levels. For example, as village density increases, road density exerts a stronger positive effect. This study provides a theoretical reference for future sustainability assessments of traditional villages. Full article

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

22 pages, 1099 KB

Open AccessArticle

Cross-Attention Diffusion Model for Semantic-Aware Short-Term Urban OD Flow Prediction

by Hongxiang Li, Zhiming Gui and Zhenji Gao

ISPRS Int. J. Geo-Inf. 2026, 15(1), 2; https://doi.org/10.3390/ijgi15010002 - 19 Dec 2025

Abstract

Origin–destination (OD) flow prediction is fundamental to intelligent transportation systems, yet existing diffusion-based models face two critical limitations. First, they inadequately exploit spatial semantics, focusing primarily on temporal dependencies or topological correlations while neglecting urban functional heterogeneity encoded in Points of Interest (POIs). [...] Read more.

Origin–destination (OD) flow prediction is fundamental to intelligent transportation systems, yet existing diffusion-based models face two critical limitations. First, they inadequately exploit spatial semantics, focusing primarily on temporal dependencies or topological correlations while neglecting urban functional heterogeneity encoded in Points of Interest (POIs). Second, static embedding fusion cannot dynamically capture semantic importance variations during denoising—particularly during traffic surges in POI-dense areas. To address these gaps, we propose the Cross-Attention Diffusion Model (CADM), a semantically conditioned framework for short-term OD flow forecasting. CADM integrates POI embeddings as spatial semantic priors and employs cross-attention to enable semantic-guided denoising, facilitating dynamic spatiotemporal feature fusion. This design adaptively reweights regional representations throughout reverse diffusion, enhancing the model’s capacity to capture complex mobility patterns. Experiments on real-world datasets demonstrate that CADM achieves balanced performance across multiple metrics. At the 30 min horizon, CADM attains the lowest RMSE of 5.77, outperforming iTransformer by 1.9%, while maintaining competitive performance at the 15 min horizon. Ablation studies confirm that removing POI features increases prediction errors by 15–20%, validating the critical role of semantic conditioning. These findings advance semantic-aware generative modeling for spatiotemporal prediction and provide practical insights for intelligent transportation systems, particularly for newly established transportation hubs or functional zone reconfigurations where semantic understanding is essential. Full article

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

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17 pages, 2657 KB

Open AccessArticle

GEPReS: A Geospatially Enabled Predictive Recommendation System for the Preventive Management of Historical Buildings

by Noëlla Dolińska, Gabriela Wojciechowska, Joanna Bac-Bronowicz and Łukasz Jan Bednarz

ISPRS Int. J. Geo-Inf. 2026, 15(1), 1; https://doi.org/10.3390/ijgi15010001 - 19 Dec 2025

Abstract

This study introduces GEPReS, a Geospatially Enabled Predictive Recommendation System designed to support the preventive management of historical buildings through short-horizon risk forecasting and context-aware decision support. The system integrates Geographic Information Systems (GISs), Internet of Things (IoT) sensor networks, and authoritative meteorological [...] Read more.

This study introduces GEPReS, a Geospatially Enabled Predictive Recommendation System designed to support the preventive management of historical buildings through short-horizon risk forecasting and context-aware decision support. The system integrates Geographic Information Systems (GISs), Internet of Things (IoT) sensor networks, and authoritative meteorological data to generate timely, actionable recommendations for conservation interventions. These may include pre-emptive shutter closure during heatwaves, activation of ventilation under elevated humidity, or intensified monitoring of structurally sensitive zones during heavy precipitation. By coupling historical datasets with real-time telemetry and calibrated predictive models, GEPReS addresses the distinctive vulnerabilities of heritage structures, which arise from material sensitivity, conservation constraints, and operational limitations under contemporary climatic conditions. The architecture combines spatial analysis, typology-aware risk assessment, and reproducible modelling practices to ensure interpretability and compliance with conservation principles. Designed for scalability and online implementation, the system provides a modular framework capable of adapting to diverse building typologies and resource environments. The paper details the system architecture, data sources, modelling approach, and implementation challenges, supported by empirical evidence from multi-site pilot deployments. Full article

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

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25 pages, 8743 KB

Open AccessArticle

Irregular Area Cartograms for Local-Level Presentation of Selected SDGs Indicators Based on Earth Observation Data

by Anna Markowska and Dariusz Dukaczewski

ISPRS Int. J. Geo-Inf. 2025, 14(12), 500; https://doi.org/10.3390/ijgi14120500 - 18 Dec 2025

Abstract

The objective of this study is to explore the applicability of irregular area cartograms for the visualization of sustainable development indicator components, utilizing earth observation (EO) data. The analysis focuses on selected Sustainable Development Goals (SDG 11 ‘Make cities and human settlements inclusive, [...] Read more.

The objective of this study is to explore the applicability of irregular area cartograms for the visualization of sustainable development indicator components, utilizing earth observation (EO) data. The analysis focuses on selected Sustainable Development Goals (SDG 11 ‘Make cities and human settlements inclusive, safe, resilient and sustainable’ and SDG 13 ‘Take urgent action to combat climate change and its impacts’) and specific targets and indicators related to green urban areas and air quality (targets: 13.2, 11.6, and 11.7; indicators: 11.6.2., 11.7.1., 13.2.2.). A comprehensive review of the relevant literature indicates that irregular area cartograms are employed only sporadically in the context of SDG monitoring, particularly at lower levels of territorial division (i.e., communes and counties). To address this gap, a series of thematic maps, including choropleth maps and irregular area cartograms, was developed. These visualizations are based on EO-derived datasets and supplemented with statistical information obtained from the Local Data Bank of the Statistics Poland. The analysis demonstrates that irregular area cartograms provide an effective means of visualizing spatial disparities in variables such as urban green space availability and air pollution at the commune and county levels. These visualizations enhance the interpretability of complex indicator structures and support more nuanced assessments of progress toward selected Sustainable Development Goals, especially in spatially detailed analytical frameworks. Preliminary usability testing among potential users revealed that irregular area cartograms are perceived as an interesting visualization technique that enhances data interpretation. Full article

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

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28 pages, 5922 KB

Open AccessArticle

Effects of a VR Mountaineering Education System on Learning, Motivation, and Cognitive Load in Compass and Map Skills

by Cheng-Pin Yu and Wernhuar Tarng

ISPRS Int. J. Geo-Inf. 2025, 14(12), 499; https://doi.org/10.3390/ijgi14120499 - 18 Dec 2025

Abstract

This study aimed to design a virtual reality (VR)–based mountaineering education system and examined its effects on junior high school students’ learning outcomes, motivation, and cognitive load in compass operation and map reading. The system integrated 3D terrain models and interactive mechanisms across [...] Read more.

This study aimed to design a virtual reality (VR)–based mountaineering education system and examined its effects on junior high school students’ learning outcomes, motivation, and cognitive load in compass operation and map reading. The system integrated 3D terrain models and interactive mechanisms across four instructional modules: Direction Recognition, Map Symbols, Magnetic Declination Adjustment, and Resection Positioning. By incorporating immersive 3D environments and hands-on virtual exercises, the system simulates authentic mountaineering scenarios, enabling students to develop essential field orientation and navigation skills. An experimental design was implemented, with participants assigned to either an experimental group learning with the VR system or a control group receiving slide-based instruction. Data were collected using pre-tests, post-tests, and questionnaires, and analyzed using SPSS for descriptive statistics, paired-sample t-tests, independent-sample t-tests, and one-way ANCOVA at a significance level of α = 0.05. The findings indicated that the experimental group achieved significantly higher post-test learning performance than the control group (F = 6.37, p = 0.014). Moreover, significant or highly significant improvements were observed across the four dimensions of learning motivation—attention, relevance, confidence, and satisfaction. The experimental group also exhibited a significantly lower extraneous cognitive load (p = 0.024). Therefore, the VR mountaineering education system provides an immersive, safe, and effective approach to teaching mountaineering and outdoor survival skills. Full article

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23 pages, 30210 KB

Open AccessArticle

Local Altimetric Correction of Global DEMs in Data-Scarce Floodplains: A Practical GNSS-Based Approach

by Jose Miguel Fragozo Arevalo, Jorge Escobar-Vargas and Jairo R. Escobar Villanueva

ISPRS Int. J. Geo-Inf. 2025, 14(12), 498; https://doi.org/10.3390/ijgi14120498 - 18 Dec 2025

Abstract

A reliable Digital Elevation Model (DEM) is a key input for land use planning and risk management, particularly in floodplains where low-resolution models often fail to represent subtle topographic variations. In many regions worldwide, high-precision elevation data are unavailable, necessitating the development of [...] Read more.

A reliable Digital Elevation Model (DEM) is a key input for land use planning and risk management, particularly in floodplains where low-resolution models often fail to represent subtle topographic variations. In many regions worldwide, high-precision elevation data are unavailable, necessitating the development of methods to enhance existing global digital elevation models (DEM). This study proposes a practical and replicable methodology to improve the vertical accuracy of global DEMs in flat terrains with limited data availability. The approach is based on correcting the altimetric differences between the DEM and GNSS-RTK-surveyed topographic points, incorporating land cover classification to refine adjustments. The methodology was tested in the Ranchería River delta in Riohacha, La Guajira, Colombia, using four global DEMs: FABDEM, SRTM, ASTER, and ALOS. Results showed a significant reduction in root mean square error (RMSE), with improvements of up to 76.691% for ASTER, 55.882% for FABDEM, 55.932% for SRTM, and 36.842% for ALOS. The proposed method requires minimal computational resources and no advanced programming. Due to minimal data requirements, it makes it a scalable and replicable solution for similar floodplain environments. These enhancements in local altimetric accuracy could help to improve the reliability of hydrodynamic modeling, with direct implications for flood risk management and decision-making in vulnerable flatland areas. Full article

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22 pages, 5552 KB

Open AccessArticle

MSA-UNet: Multiscale Feature Aggregation with Attentive Skip Connections for Precise Building Extraction

by Guobiao Yao, Yan Chen, Wenxiao Sun, Zeyu Zhang, Yifei Tang and Jingxue Bi

ISPRS Int. J. Geo-Inf. 2025, 14(12), 497; https://doi.org/10.3390/ijgi14120497 - 17 Dec 2025

Abstract

An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep learning models, limitations remain in handling significant variations [...] Read more.

An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep learning models, limitations remain in handling significant variations in building scales and complex architectural forms, which may lead to inaccurate boundaries or difficulties in extracting small or irregular structures. Therefore, the present study proposes MSA-UNet, a reliable semantic segmentation framework that leverages multiscale feature aggregation and attentive skip connections for an accurate extraction of building footprints. This framework is constructed based on the U-Net architecture, incorporating VGG16 as a replacement for the original encoder structure, which enhances its ability to capture low-discriminative features. To further improve the representation of image buildings with different scales and shapes, a serial coarse-to-fine feature aggregation mechanism was used. Additionally, a novel skip connection was built between the encoder and decoder layers to enable adaptive weights. Furthermore, a dual-attention mechanism, implemented through the convolutional block attention module, was integrated to enhance the focus of the network on building regions. Extensive experiments conducted on the WHU and Inria building datasets validated the effectiveness of MSA-UNet. On the WHU dataset, the model demonstrated a state-of-the-art performance with a mean Intersection over Union (mIoU) of 94.26%, accuracy of 98.32%, F1-score of 96.57%, and mean Pixel accuracy (mPA) of 96.85%, corresponding to gains of 1.41% in mIoU over the baseline U-Net. On the more challenging Inria dataset, MSA-UNet achieved an mIoU of 85.92%, indicating a consistent improvement of up to 1.9% over the baseline U-Net. These results confirmed that MSA-UNet markedly improved the accuracy and boundary integrity of building extraction from HR data, outperforming existing classic models in terms of segmentation quality and robustness. Full article

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

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26 pages, 1085 KB

Open AccessArticle

Can Urban Information Infrastructure Development Improve Resident Health? Evidence from China Health and Retirement Longitudinal Survey

by Huiling Zhao, Chenyang Yu and Zhanchuang Han

ISPRS Int. J. Geo-Inf. 2025, 14(12), 496; https://doi.org/10.3390/ijgi14120496 - 16 Dec 2025

Abstract

Taking the “Broadband China” policy (BCP) as a quasi-natural experiment, this paper utilizes nationwide tracking data from the China Health and Retirement Longitudinal Survey (CHARLS) for 2011, 2013, 2015, and 2018 and employs a Difference-in-Differences (DID) model to evaluate whether and how urban [...] Read more.

Taking the “Broadband China” policy (BCP) as a quasi-natural experiment, this paper utilizes nationwide tracking data from the China Health and Retirement Longitudinal Survey (CHARLS) for 2011, 2013, 2015, and 2018 and employs a Difference-in-Differences (DID) model to evaluate whether and how urban information infrastructure development affects resident health. We identify a clear and significant improvement in health outcomes attributable to BCP. After the implementation of BCP, physical health and mental health increase by 2.5% and 1.7%, respectively. Furthermore, mechanism analysis confirms that BCP enhances resident health primarily by improving information and communication technology (ICT) levels and by promoting local economic development. The positive health effect of BCP is more pronounced in regions with a better medical environment, suggesting the presence of complementary public-service capacity. At the individual level, heterogeneity tests reveal that BCP exerts a stronger positive influence on the physical health of male and rural respondents, while the benefits for older respondents are relatively smaller. At the city level, the health-promoting effect of BCP is stronger in economically less developed regions, and cities with higher administrative status exhibit more substantial health improvements. Full article

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

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28 pages, 4317 KB

Open AccessArticle

A Semantic Collaborative Filtering-Based Recommendation System to Enhance Geospatial Data Discovery in Geoportals

by Amirhossein Vahdat, Thierry Badard and Jacynthe Pouliot

ISPRS Int. J. Geo-Inf. 2025, 14(12), 495; https://doi.org/10.3390/ijgi14120495 - 13 Dec 2025

Abstract

Traditional geoportals depend primarily on keyword-based search, which often fails to retrieve relevant datasets when metadata are heterogeneous, incomplete, or inconsistent with user terminology. This limitation reduces the efficiency of data discovery and selection, particularly in domains where metadata quality varies widely. This [...] Read more.

Traditional geoportals depend primarily on keyword-based search, which often fails to retrieve relevant datasets when metadata are heterogeneous, incomplete, or inconsistent with user terminology. This limitation reduces the efficiency of data discovery and selection, particularly in domains where metadata quality varies widely. This study aims to address this challenge by developing a semantic collaborative filtering recommendation system designed to enhance dataset discovery in geoportals through the analysis of implicit user interactions. The system captures users’ search queries, viewed datasets, downloads, and applied filters to infer feedback and organize it into a user–item matrix. Because interaction data are typically sparse, semantic user clustering is applied to mitigate this limitation by grouping users with semantically related interests through hierarchical relationships represented in the Simple Knowledge Organization System (SKOS). However, as users often need complementary datasets to complete specific tasks, association rule mining is employed to identify co-occurrence patterns in search histories and enhance task-related result diversity. The final recommendation scores are then computed by factorizing the user–item matrix with Alternating Least Squares (ALS), using cosine similarity on the latent user vectors to identify nearest neighbors, and applying a standard user-based neighborhood prediction model to rank unseen datasets. The system is implemented within an existing ontology-based geoportal as a standalone, configurable component, requiring only access to user interaction logs and dataset identifiers. Evaluation using precision, recall, and Precision@5 demonstrates that increasing user interactions improves recommendation performance by strengthening behavioral evidence used for ranking. The findings indicate that integrating semantic relationships and behavioral patterns can strengthen dataset discovery in geoportals and complement conventional metadata-based search mechanisms. Full article

(This article belongs to the Special Issue Intelligent Interoperability in the Geospatial Web)

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22 pages, 35558 KB

Open AccessArticle

Mapping Pastoral Mobility: A Geospatial Inventory of Temporary Dwellings Within the Southern Carpathians

by Emil Marinescu, Sidonia Marinescu and Liliana Popescu

ISPRS Int. J. Geo-Inf. 2025, 14(12), 494; https://doi.org/10.3390/ijgi14120494 - 11 Dec 2025

Abstract

Temporary pastoral settlements are a keystone of high-mountain ecologies, yet they are not included in any official datasets. Therefore, to fill this gap, this research aims to create the first systematic spatial inventory of high-altitude rural temporary dwellings (sheepfolds and shelters) and land [...] Read more.

Temporary pastoral settlements are a keystone of high-mountain ecologies, yet they are not included in any official datasets. Therefore, to fill this gap, this research aims to create the first systematic spatial inventory of high-altitude rural temporary dwellings (sheepfolds and shelters) and land use in the central part of the Southern Carpathians, one of the major traditional areas for sheep breeding in Romania. The data sources include 1:5000 orthophotos, 1:25,000-scale topographic maps, the Corine Land Cover model, field investigation campaigns, and forestry maps. Each one provided complementary information, which was integrated through cross-comparison and ground truth validation for settlement status and the consistent classification of land-use categories. The methodological steps followed are as follows: digitizing shelters, sheepfolds, and agricultural surfaces; overlaying elements of interest for the study; using Data Management, Spatial Analyst, Conversion Tools, and Field Calculation; and interpreting graphical and cartographical materials. Through overlay analysis, we examined how temporary settlements correlate with land-use categories; the ArcGIS Saptial Analyst tools enabled the identification of altitudinal patterns and spatial clusters. We identified 753 sheepfolds and 5411 shelters in this part of the Carpathians, situated at high altitudes, closely connected to the transhumance and pendulation phenomenon. The analysis of land use for the altitude-temporary settlements within the Parâng-Cindrel Mountains highlighted the fact that the traditional agriculture is still carried on by the locals, but biodiversity is at stake where fields are abandoned. Implications regarding the ecological and environmental impact of grazing in the area, conflict mitigation, and livestock protection as well as the cultural dimension are discussed. The study provides the first spatially explicit inventory of these shelters and sheepfolds, providing a cornerstone for interdisciplinary policy-making, conservation, and local development priorities. Full article

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29 pages, 10236 KB

Open AccessArticle

A Graph Data Model for CityGML Utility Network ADE: A Case Study on Water Utilities

by Ensiyeh Javaherian Pour, Behnam Atazadeh, Abbas Rajabifard, Soheil Sabri and David Norris

ISPRS Int. J. Geo-Inf. 2025, 14(12), 493; https://doi.org/10.3390/ijgi14120493 - 11 Dec 2025

Abstract

Modelling connectivity in utility networks is essential for operational management, maintenance planning, and resilience analysis. The CityGML Utility Network Application Domain Extension (UNADE) provides a detailed conceptual framework for representing utility networks; however, most existing implementations rely on relational databases, where connectivity must [...] Read more.

Modelling connectivity in utility networks is essential for operational management, maintenance planning, and resilience analysis. The CityGML Utility Network Application Domain Extension (UNADE) provides a detailed conceptual framework for representing utility networks; however, most existing implementations rely on relational databases, where connectivity must be reconstructed through joins rather than represented as explicit relationships. This creates challenges when managing densely connected network structures. This study introduces the UNADE–Labelled Property Graph (UNADE-LPG) model, a graph-based representation that maps the classes, relationships, and constraints defined in the UNADE Unified Modelling Language (UML) schema into nodes, edges, and properties. A conversion pipeline is developed to generate UNADE-LPG instances directly from CityGML UNADE datasets encoded in GML, enabling the population of graph databases while maintaining semantic alignment with the original schema. The approach is demonstrated through two case studies: a schematic network and a real-world water system from Frankston, Melbourne. Validation procedures, covering structural checks, topological continuity, classification behaviour, and descriptive graph statistics, confirm that the resulting graph preserves the semantic structure of the UNADE schema and accurately represents the physical connectivity of the network. An analytical path-finding query is also implemented to illustrate how the UNADE-LPG structure supports practical network-analysis tasks, such as identifying connected pipeline sequences. Overall, the findings show that the UNADE-LPG model provides a clear, standards-aligned, and operationally practical foundation for representing utility networks within graph environments, supporting future integration into digital-twin and network-analytics applications. Full article

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ISPRS International Journal of Geo-Information

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