ISPRS International Journal of Geo-Information

26 pages, 26537 KiB

Open AccessArticle

Contrastive Learning with Image Deformation and Refined NT-Xent Loss for Urban Morphology Discovery

by Chunliang Hua, Daijun Chen, Mengyuan Niu, Lizhong Gao, Junyan Yang and Qiao Wang

ISPRS Int. J. Geo-Inf. 2025, 14(5), 196; https://doi.org/10.3390/ijgi14050196 - 8 May 2025

The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city [...] Read more.

The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city area. Raster analysis can lead to fragmented forms, and focusing only on the central city area ignores many representative urban forms in the suburbs and towns. In this study, a vast and complex dataset was applied to the urban morphology discovery based on the administrative community or village boundary, and a new image deformation pipeline was proposed to enhance the morphological characteristics of building groups. This allows visual neural networks to focus on extracting the morphological characteristics of building groups. Additionally, the research on urban morphology often uses unsupervised learning, which means that the learning process is difficult to control. Therefore, we refined the NT-Xent loss so that it can integrate morphological indicators. This improvement allows the visual neural network to “recognize” the similarity of samples during optimization. By defining the similarity, we can guide the network to bring samples closer or move them farther apart based on certain morphological indicators. Three Chinese cities were used for our testing. Representative urban types were identified, particularly some types located at the urban fringe. The data analysis demonstrated the effectiveness of our image deformation pipeline and loss function, and the sociological analysis illustrated the unique urban functions of these urban types. Full article

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

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22 pages, 8448 KiB

Open AccessArticle

Leveraging Principal Component Analysis for Data-Driven and Objective Weight Assignment in Spatial Decision-Making Framework for Qanat-Induced Subsidence Susceptibility Assessment in Railway Networks

by Farzaneh Naeimiasl, Hossein Vahidi and Niloufar Soheili

ISPRS Int. J. Geo-Inf. 2025, 14(5), 195; https://doi.org/10.3390/ijgi14050195 - 6 May 2025

Abstract

Railway networks are highly susceptible to land subsidence, which can undermine their functional stability and safety, resulting in recurring failures and vulnerabilities. This paper aims to evaluate the susceptibility of the railway network due to Qanat underground channels in the city of Bafq, [...] Read more.

Railway networks are highly susceptible to land subsidence, which can undermine their functional stability and safety, resulting in recurring failures and vulnerabilities. This paper aims to evaluate the susceptibility of the railway network due to Qanat underground channels in the city of Bafq, Iran. The criteria considered for assessing the susceptibility of Qanats subsidence on the railway network in this study are Qanat channel density, Qanat well density, discharge rate of the Qanat, depth of the Qanat, railway traffic, and the railway passing load. The subjective determination of criteria weights in Multi-Criteria Decision-Making (MCDM) for susceptibility analysis is typically a complex, time-consuming, and biased task. Furthermore, there is no comprehensive study on the impact and relative significance of Qanat-related factors on railway subsidence in Iran. To address this gap, this study developed a novel spatial objective weighting approach based on Principal Component Analysis (PCA)—as an unsupervised Machine Learning (ML) technique—within a spatial decision-making framework specifically designed for railway susceptibility assessment. In the proposed framework, the final Qanat-induced subsidence susceptibility zoning was conducted using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This study identified 7.7 km² of the total area as a high-susceptibility zone, which encompasses 15 km of railway network requiring urgent attention. The developed framework demonstrated promising performance without deploying subjective information, providing a robust data-driven approach for susceptibility assessment in the study area. Full article

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27 pages, 33588 KiB

Open AccessArticle

Geospatial Approach to Assess Flash Flood Vulnerability in a Coastal District of Bangladesh: Integrating the Multifaceted Dimension of Vulnerabilities

by Sajib Sarker, Israt Jahan, Xin Wang and Abul Azad

ISPRS Int. J. Geo-Inf. 2025, 14(5), 194; https://doi.org/10.3390/ijgi14050194 - 6 May 2025

Abstract

Flash floods pose a significant threat to Bangladesh; in particular, on 20 August 2024, the Feni district experienced a major flash flood, affecting more than 550,000 people and causing widespread damage. To effectively mitigate the impacts of flash floods, it is essential to [...] Read more.

Flash floods pose a significant threat to Bangladesh; in particular, on 20 August 2024, the Feni district experienced a major flash flood, affecting more than 550,000 people and causing widespread damage. To effectively mitigate the impacts of flash floods, it is essential to conduct a comprehensive flash flood vulnerability assessment, incorporating multiple triggering factors. This study aims to assess flash flood vulnerability in the Feni District through a unique approach, integrating various dimensions of vulnerability. The study utilizes a geospatial methodology, employing the formula of vulnerability developed by UNESCO-IHE. Four dimensions of vulnerability were analyzed: social, physical, economic, and environmental. For each dimension, specific variables were selected to assess exposure, susceptibility, and resilience. Principal Component Analysis (PCA) was used to assign weights to these variables. The geospatial layers of influencing vulnerability factors were integrated together to create flash flood vulnerability maps of four dimensions. These were then overlaid to generate a composite flash flood vulnerability map. The analysis revealed a distinct spatial distribution of vulnerability across Feni District. In terms of environmental vulnerability due to flash flood, about 14% of the total area falls into the very highly vulnerable zone, whereas 13%, 8% and 5% of the study area were found to be very highly vulnerable regarding social, economic and physical aspects, respectively. The composite flash flood vulnerability map identified key vulnerability hotspots, with the most vulnerable unions (the smallest administrative unit in Bangladesh) being Feni Pourashava (68% very high), Sonagazi Paurashava (40% very high), and Nawabpur (32% very high), while the least vulnerable areas were Jailashkara (58% very low), Anandapur (81% very low), and Darbarpur (82% very low). The results show that the Feni District’s flash flood susceptibility varies significantly throughout the region, which provide crucial insights for policymakers and local authorities in order to identify vulnerability hotspots, prioritize interventions in vulnerable areas, enhance flash flood resilience, and implement adaptive strategies. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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22 pages, 46427 KiB

Open AccessArticle

PixelQuery: Efficient Distance Range Join Query Technique for Visualization Analysis

by Bo Pang, Zebang Liu, Wei Xiong and Mengyu Ma

ISPRS Int. J. Geo-Inf. 2025, 14(5), 193; https://doi.org/10.3390/ijgi14050193 - 5 May 2025

Abstract

A distance range join query (DRJQ) is a fundamental and critical operation in spatial database queries. It identifies geographic elements within specified distance ranges. This technique has a wide range of applications in multiple domains, including Geographic Information Systems (GISs), urban planning, and [...] Read more.

A distance range join query (DRJQ) is a fundamental and critical operation in spatial database queries. It identifies geographic elements within specified distance ranges. This technique has a wide range of applications in multiple domains, including Geographic Information Systems (GISs), urban planning, and environmental monitoring. However, performing a DRJQ on large-scale spatial data remains a challenging problem, as the computational complexity of existing techniques escalates rapidly with increasing volumes of data. We propose PixelQuery, an efficient DRJQ method specifically optimized for visualization analysis. PixelQuery integrates spatial indexing with visualization-oriented strategies. It directly computes the display values of query results within the viewport, substantially lowering computational costs. Experiments conducted on datasets of varying scales demonstrate that this method can handle visualization queries involving tens of millions of elements on a standard laptop, with a maximum processing time of only 7.64 s. This technology provides a robust solution for rapid DRJQ processing and the visualization of large-scale vector data, offering promising potential for a diverse range of applications. Full article

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20 pages, 3506 KiB

Open AccessArticle

Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation

by Chenglin Yu, Lihong Shi and Yangyang Zhao

ISPRS Int. J. Geo-Inf. 2025, 14(5), 192; https://doi.org/10.3390/ijgi14050192 - 3 May 2025

Abstract

Next point-of-interest (POI) recommendation aims to predict users’ future visitation intentions based on historical check-in trajectories. However, this task faces significant challenges, including coarse-grained user interest representation, insufficient social modeling, sparse check-in data, and the insufficient learning of contextual patterns. To address this, [...] Read more.

Next point-of-interest (POI) recommendation aims to predict users’ future visitation intentions based on historical check-in trajectories. However, this task faces significant challenges, including coarse-grained user interest representation, insufficient social modeling, sparse check-in data, and the insufficient learning of contextual patterns. To address this, we propose a model that combines check-in trajectory information with user friendship relationships and uses a Transformer architecture for prediction (TraFriendFormer). Our approach begins with the construction of trajectory flow graphs using graph convolutional networks (GCNs) to globally capture POI correlations across both spatial and temporal dimensions. In parallel, we design an integrated social graph that combines explicit friendships with implicit interaction patterns, in which GraphSAGE aggregates neighborhood information to generate enriched user embeddings. Finally, we fuse the POI embeddings, user embeddings, timestamp embeddings, and category embeddings and input them into the Transformer architecture. Through the self-attention mechanism, the model captures the complex temporal relationships in the check-in sequence. We validate the effectiveness of TraFriendFormer on two real-world datasets (FourSquare and Gowalla). The experimental results show that TraFriendFormer achieves an average improvement of 10.3% to 37.2% in metrics such as Acc@k and MRR compared to the selected state-of-the-art baselines. Full article

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

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31 pages, 14936 KiB

Open AccessArticle

Pattern Recognition in Urban Maps Based on Graph Structures

by Xiaomin Lu, Zhiyi Zhang, Haoran Song and Haowen Yan

ISPRS Int. J. Geo-Inf. 2025, 14(5), 191; https://doi.org/10.3390/ijgi14050191 - 30 Apr 2025

Abstract

Map groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. While both [...] Read more.

Map groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. While both have achieved certain recognition outcomes, they suffer from four key limitations: (1) insufficient algorithmic interpretability; (2) limited model generalizability; (3) restricted pattern diversity in recognition; (4) inability of existing methods (including deep learning and traditional algorithms) to achieve multi-pattern recognition across heterogeneous map group types (e.g., building groups vs. road networks) using a single framework. To address these limitations, this study proposes a graph structure-based multi-pattern recognition algorithm for map groups. The algorithm integrates the quantitative advantages of directional entropy in characterizing spatial distribution patterns with the discriminative power of node degree in analyzing edge-node geometric models. Experimental validation utilized building and road network data from multiple cities, constructing a dataset of 600 samples divided into two subsets: Sample Set 1 (for parameter threshold calibration and rule generation) and Sample Set 2 (for algorithm performance validation and transferability testing). The results demonstrate a classification accuracy of 97% for the proposed algorithm, effectively distinguishing four building group patterns (linear, curved, grid, irregular) and two road network patterns (grid, irregular). This work establishes a novel methodological framework for multi-scale spatial pattern analysis in map generalization and urban planning. Full article

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26 pages, 12493 KiB

Open AccessArticle

Spatiotemporal Distribution and Evolution of Global World Cultural Heritage, 1972–2024

by Yangyang Lu, Qingwen Han, Zheng Zhang, Zhong Sun and Jian Dai

ISPRS Int. J. Geo-Inf. 2025, 14(5), 190; https://doi.org/10.3390/ijgi14050190 - 30 Apr 2025

Abstract

Taking 992 world cultural heritage (WCH) sites as the research object, the spatial distribution and evolution characteristics of WCH were analyzed by kernel density analysis, mathematical statistics, standard deviation ellipse, among other methods, and nine correlation factors were selected to explore the mechanism [...] Read more.

Taking 992 world cultural heritage (WCH) sites as the research object, the spatial distribution and evolution characteristics of WCH were analyzed by kernel density analysis, mathematical statistics, standard deviation ellipse, among other methods, and nine correlation factors were selected to explore the mechanism underlying the spatial and elevation-dependent distribution patterns of WCH and their sensitivity to climate change by using geographic detectors and multi-scale geographically weighted regression (MGWR) models. The results show the following: (1) The spatial distribution type of WCH is aggregation, and 80% of WCH are clustered below 500 m, with Europe and Asia-Pacific as the primary hotspots. (2) The distribution of WCH tends to be global and in the direction of “W-WN” to “E-ES”, and the average center movement direction is “E → EN → ES → E”. There is a trend of positive east–west distribution on the whole. (3) Road density, per capita GDP, and other factors are the dominant factors affecting the spatial pattern of world cultural heritage, and the interaction between the factors shows a nonlinear enhancement or two-factor enhancement trend. (4) There are spatial differences in the mechanisms of the factors, with river density contributing positively, aspect rate and forest cover contributing negatively, population density, per capita GDP, and road density mainly contributing positively to the spatial distribution of the WCH, annual precipitation mainly contributing negatively, and the positive and negative effects of altitude and GDP being comparable. Based on the above-mentioned differences in spatial distribution, evolutionary characteristics, and mechanism of action, the causes are discussed, and some suggestions for developing and protecting the world cultural heritage are presented. Full article

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19 pages, 9295 KiB

Open AccessArticle

Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts

by Liwei Zou, Zhi He, Xianwei Wang and Yutian Liang

ISPRS Int. J. Geo-Inf. 2025, 14(5), 189; https://doi.org/10.3390/ijgi14050189 - 30 Apr 2025

Abstract

Typhoons are among the most destructive natural phenomena, posing significant threats to human society. Therefore, accurate damage assessment is crucial for effective disaster management and sustainable development. While social media texts have been widely used for disaster analysis, most current studies tend to [...] Read more.

Typhoons are among the most destructive natural phenomena, posing significant threats to human society. Therefore, accurate damage assessment is crucial for effective disaster management and sustainable development. While social media texts have been widely used for disaster analysis, most current studies tend to neglect the geographic references and primarily focus on single-label classification, which limits the real-world utility. In this paper, we propose a multi-task learning method that synergizes the tasks of location extraction and damage identification. Using Bidirectional Encoder Representations from Transformers (BERT) with auxiliary classifiers as the backbone, the framework integrates a toponym entity recognition model and a multi-label classification model. Novel toponym-enhanced weights are designed as a bridge to generate augmented text representations for both tasks. Experimental results show high performance, with F1-scores of 0.891 for location extraction and 0.898 for damage identification, representing improvements of 4.3% and 2.5%, respectively, over single-task and deep learning baselines. A case study of three recent typhoons (In-fa, Chaba, and Doksuri) that hit China’s coastal regions reveals the spatial distribution and temporal pattern of typhoon damage, providing actionable insights for disaster management and resource allocation. This framework is also adaptable to other disaster scenarios, supporting urban resilience and sustainable development. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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22 pages, 6480 KiB

Open AccessArticle

Heterogeneity Analysis of Resident Demands and Public Service Facilities in Megacities of China from the Perspective of Urban Health Examination

by Ning Zhang, Shaohua Wang, Haojian Liang, Zhuonan Huang, Xiao Li and Zhenbo Wang

ISPRS Int. J. Geo-Inf. 2025, 14(5), 188; https://doi.org/10.3390/ijgi14050188 - 30 Apr 2025

Abstract

Public service facilities are the cornerstone of urban development and further expansion, and their spatial distribution fairness is closely related to the quality of life of urban residents. Existing research tends to focus on coverage analysis of a single city or a single [...] Read more.

Public service facilities are the cornerstone of urban development and further expansion, and their spatial distribution fairness is closely related to the quality of life of urban residents. Existing research tends to focus on coverage analysis of a single city or a single type of public service facility, lacking a macro perspective at a medium-to-large scale and consideration of residents’ public service needs. To improve the monitoring of urban public service facility coverage and supply–demand patterns, this paper adopts an urban diagnostic perspective, using 14 megacities from nine urban agglomerations in China as the study area. By integrating spatial and temporal social sensing big data, including road networks, population, and points of interest (POI) data, and employing spatial analysis methods including coverage rate calculation, supply–demand matching efficiency, spatial heterogeneity, and sp{atial stability analysis, this study reveals the spatial distribution patterns of various facilities within cities, as well as the heterogeneity, balance, and equity of supply–demand matching efficiency between different cities. The results show that the spatial distribution of public service facilities in different cities generally tends to concentrate in the central areas, although there are some variations due to local topographical influences. The coverage rate of transportation and education facilities is relatively high, while that of healthcare facilities is generally lower. This study provides information support for urban planning and the optimization of public service facility allocation, contributing to the achievement of sustainable urban development through the comprehensive analysis and comparison of 14 megacities. Full article

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23 pages, 2596 KiB

Open AccessArticle

RouteLAND: An Integrated Method and a Geoprocessing Tool for Characterizing the Dynamic Visual Landscape Along Highways

by Loukas-Moysis Misthos and Vassilios Krassanakis

ISPRS Int. J. Geo-Inf. 2025, 14(5), 187; https://doi.org/10.3390/ijgi14050187 - 30 Apr 2025

Abstract

Moving away from a static concept for the landscape that surrounds us, in this research article, we approach the visual landscape as a dynamic concept. Moreover, we attempt to provide an interconnection between the domains of landscape and cartography by designing maps that [...] Read more.

Moving away from a static concept for the landscape that surrounds us, in this research article, we approach the visual landscape as a dynamic concept. Moreover, we attempt to provide an interconnection between the domains of landscape and cartography by designing maps that are particularly suitable for characterizing the visible landscape and are potentially meaningful for overall landscape evaluation. Thus, the present work mainly focuses on the consecutive computation of vistas along highways, incorporating actual landscape composition—as the landscape is perceived from an egocentric perspective by observers moving along highway routes in peri-urban landscapes. To this end, we developed an integrated method and a Python (version 2.7.16) tool, named “RouteLAND”, for implementing an algorithmic geoprocessing procedure; through this geoprocessing tool, sequences of composite dynamic geospatial analyses and geometric calculations are automatically implemented. The final outputs are interactive web maps, whereby the segments of highway routes are characterized according to the dominant element of the visible landscape by employing (spatial) aggregation techniques. The developed geoprocessing tool and the generated interactive map provide a cartographic exploratory tool for summarizing the landscape character of highways in any peri-urban landscape, while hypothetically moving in a vehicle. In addition, RouteLAND can potentially aid in the assessment of existing or future highways’ scenic level and in the sustainable design of new highways based on the minimization of intrusive artificial structures’ vistas; in this sense, RouteLAND can serve as a valuable tool for landscape evaluation and sustainable spatial planning and development. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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25 pages, 11699 KiB

Open AccessArticle

Analysis of Spatial and Driving Factors of National Sanitary Resources in China Using GIS

by Yujia Deng, Lixia Feng, Jeremy Cenci, Jiazhen Zhang and Jun Cai

ISPRS Int. J. Geo-Inf. 2025, 14(5), 186; https://doi.org/10.3390/ijgi14050186 - 30 Apr 2025

Abstract

Promoting health equity is key to achieving sustainable urban development. The National Sanitary Cities in China (NSCC) policy is a critical development model aimed at improving urban environments and enhancing public health. This study evaluates the selection criteria and policy impact of NSCCs, [...] Read more.

Promoting health equity is key to achieving sustainable urban development. The National Sanitary Cities in China (NSCC) policy is a critical development model aimed at improving urban environments and enhancing public health. This study evaluates the selection criteria and policy impact of NSCCs, using the nearest neighbour index, geographic concentration index, imbalance index, and kernel density estimation to analyze their distribution characteristics. Additionally, it explores influencing factors using a geodetector model and spatial overlay analysis. The findings indicate a shift in NSCC selection criteria from urban sanitation to urban health, reflecting China’s strategic focus on achieving health equity. The spatial distribution analysis indicates that NSCCs exhibit a clustered pattern, characterized by dual cores, dual centres, multiple scattered points, and regional extensions. NSCCs are influenced by both natural and socioeconomic factors, with economy and population, technological innovation, and informatization exerting greater influences. This study is valuable for understanding the spatial patterns of NSCCs, providing a scientific basis for promoting equitable and sustainable health resource allocation and policymaking. Full article

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

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29 pages, 74025 KiB

Open AccessArticle

Geospatial Framework for Assessing the Suitability and Demand for Agricultural Digital Solutions in Europe: A Tool for Informed Decision-Making

by Theodoros Chalazas, Antonis Koukourikos, Jan Bauwens, Nick Berkvens, Jonathan Van Beek, Nikos Kalatzis, George Papadopoulos, Panagiotis Ilias, Nikolaos Marianos and Christopher Brewster

ISPRS Int. J. Geo-Inf. 2025, 14(5), 185; https://doi.org/10.3390/ijgi14050185 - 25 Apr 2025

Abstract

This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific [...] Read more.

This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific levels. By leveraging open-source Earth observations and socioeconomic data, we develop multiple performance, environmental, and socioeconomic similarity indexes that compare regions based on shared characteristics, such as soil quality, climate, and socioeconomic factors. Using advanced statistical and multi-criteria analysis tools, these indexes are tailored to different stages of agricultural production, enabling region-specific assessments that identify and prioritize the needs for digital solutions across Europe. The results indicate that the developed indexes effectively categorize regions based on comparable characteristics, facilitating the targeted recommendation of ADSs. Additionally, a connectivity performance index is created to assess the local deployment model of agricultural digital solutions (cloud, edge, or mixed), ensuring that the recommendations for technological implementation are feasible and effective given the local connectivity conditions. Full article

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22 pages, 9048 KiB

Open AccessArticle

Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China

by Haihong Li and Li He

ISPRS Int. J. Geo-Inf. 2025, 14(5), 184; https://doi.org/10.3390/ijgi14050184 - 24 Apr 2025

Abstract

Scientific site selection for urban parks is an important way to increase urban resilience and safeguard people’s well-being. Aiming at the lack of systematic consideration in the traditional park siting research, this study utilizes geographically weighted regression to explore the various characteristic factors [...] Read more.

Scientific site selection for urban parks is an important way to increase urban resilience and safeguard people’s well-being. Aiming at the lack of systematic consideration in the traditional park siting research, this study utilizes geographically weighted regression to explore the various characteristic factors affecting the spatial distribution of parks, and based on this, combines the random forest model and the interpretable model to accurately assess the potential of parks on urban land in Shenzhen and provide the basis for site selection. The study indicates that: ① Shenzhen’s parks exhibit complex differentiation characteristics in terms of natural landscape elements and the intensity of economic activities; ② The geographically weighted random forest (GWRF) model has better learning and generalization capabilities compared to the random forest (RF) model, and the average accuracy of the GWRF model is improved by 0.04 compared to the traditional RF model; ③ The park’s development potential is divided according to the results of the GWRF model, with 52.01% denoted as the potential incubation zone, 21.15% the potential accumulation zone, 8.25% the potential growth zone, and 18.59% the potential core zone; ④ Through interpretability analysis, it is identified that vegetation coverage, the density of tourist attractions or points of interest (POI), slope, elevation, and nighttime light intensity are the most significant factors affecting park development potential, while the distance to roads and the distance to bodies of water are the least influential factors. The research systematically explores a quantitative evaluation framework for the development potential of Shenzhen’s parks, opening new theoretical pathways and practical paradigms for the sustainable development planning of Shenzhen under the “Park City” concept. Full article

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20 pages, 5374 KiB

Open AccessArticle

The Urban–Rural Education Divide: A GIS-Based Assessment of the Spatial Accessibility of High Schools in Romania

by Angelo Andi Petre, Liliana Dumitrache, Alina Mareci and Alexandra Cioclu

ISPRS Int. J. Geo-Inf. 2025, 14(5), 183; https://doi.org/10.3390/ijgi14050183 - 24 Apr 2025

Abstract

Educational achievement plays a significant role in the labour market, benefiting individuals and society. Graduating from high school is a key step towards better employment opportunities and a prerequisite for higher education attainment. In 2023, only 22.5% of the Romanian population graduated tertiary [...] Read more.

Educational achievement plays a significant role in the labour market, benefiting individuals and society. Graduating from high school is a key step towards better employment opportunities and a prerequisite for higher education attainment. In 2023, only 22.5% of the Romanian population graduated tertiary education, while 16.6% left education or training early. The Romanian public high school network comprises 1558 units, mostly located in urban areas. The high school enrolment rate is 83.5% in urban areas, and it drops to less than 60% in rural areas, with the country registering the highest out-of-school rate in the EU for the 15-year-old population. Spatial accessibility may influence enrolment in high schools, particularly for students living in rural or remote areas, who often face financial challenges fuelled by long distances and limited transportation options. Hence, travel distance may represent a potential barrier to completing the educational process or may determine inequalities in educational opportunities and outcomes. This paper aims to assess the spatial accessibility of the public high school network in Romania by using distance data provided by the Open Street Map API (Application Programming Interface). We examine variations in spatial accessibility based on the distribution of high school units and road network characteristics considering three variables: travel distance to the nearest high school, the average distance to three different categories of high schools, and the number of high schools located within a 20 km buffer zone. The results highlight a significant urban–rural divide in the availability of public high school facilities, with 84.1% (n = 1311) located in urban areas while 49.1% of the high school-aged population lives in rural areas. Many rural communities lack adequate educational facilities, often having limited options for high school education. The findings also show that 32% of the high school-aged population has to travel more than 10 km to the nearest high school, and 7% has no high school options within a 20 km buffer zone. This study provides insights into the educational landscape in Romania, pointing out areas with limited access to high schools, which contributes to further inequalities in educational attainment. The findings may serve as a basis for developing policies and practices to bridge the urban–rural divide in educational opportunities and foster a more equitable and inclusive education system. Full article

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23 pages, 4309 KiB

Open AccessArticle

Hybrid Learning Model of Global–Local Graph Attention Network and XGBoost for Inferring Origin–Destination Flows

by Zhenyu Shan, Fei Yang, Xingzi Shi and Yaping Cui

ISPRS Int. J. Geo-Inf. 2025, 14(5), 182; https://doi.org/10.3390/ijgi14050182 - 24 Apr 2025

Abstract

Origin–destination (OD) flows are essential for urban studies, yet their acquisition is often hampered by high costs and privacy constraints. Prevailing inference methodologies inadequately address latent spatial dependencies between non-contiguous and distant areas, which are useful for understanding modern transportation systems with expanding [...] Read more.

Origin–destination (OD) flows are essential for urban studies, yet their acquisition is often hampered by high costs and privacy constraints. Prevailing inference methodologies inadequately address latent spatial dependencies between non-contiguous and distant areas, which are useful for understanding modern transportation systems with expanding regional interactions. To address these challenges, this paper propose a hybrid learning model with the Global–Local Graph Attention Network and XGBoost (GLGAT-XG) to infer OD flows from both global and local geographic contextual information. First, we represent the study area as an undirected weighted graph. Second, we design the GLGAT to encode spatial correlation and urban feature information into the embeddings within a multitask setup. Specifically, the GLGAT employs a graph transformer to capture global spatial correlations and a graph attention network to extract local spatial correlations followed by weighted fusion to ensure validity. Finally, OD flow inference is performed by XGBoost based on the GLGAT-generated embeddings. The experimental results of multiple real-world datasets demonstrate an 8% improvement in RMSE, 7% in MAE, and 10% in CPC over baselines. Additionally, we produce a multi-scale OD dataset in Xian, China, to further reveal spatial-scale effects. This research builds on existing OD flow inference methodologies and offers significant practical implications for urban planning and sustainable development. Full article

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17 pages, 10950 KiB

Open AccessArticle

The Integration of Geospatial Data for the BIM-Based Inventory of a Skatepark—A Case Study

by Przemysław Klapa and Maciej Małek

ISPRS Int. J. Geo-Inf. 2025, 14(5), 181; https://doi.org/10.3390/ijgi14050181 - 24 Apr 2025

Abstract

Sports facilities encompass diverse spaces tailored to various sports disciplines, each characterized by unique shapes and sizes. Skateparks, renowned for their avant-garde designs, are meticulously crafted to exude distinctiveness, featuring an array of constructions, surfaces, and intricate shapes. Traditional measurement methods often struggle [...] Read more.

Sports facilities encompass diverse spaces tailored to various sports disciplines, each characterized by unique shapes and sizes. Skateparks, renowned for their avant-garde designs, are meticulously crafted to exude distinctiveness, featuring an array of constructions, surfaces, and intricate shapes. Traditional measurement methods often struggle to capture the spatial, structural, and architectural diversity of these facilities. Constructing 3D models, particularly with Building Information Modeling (BIM) technology, faces inherent challenges due to the complex and individualistic nature of skateparks. The crux lies in acquiring credible and comprehensive spatial and construction-related information. Geospatial data emerges as a viable solution, effectively addressing the skatepark’s myriad forms while upholding information accuracy and reliability. By gathering, processing, and integrating Terrestrial Laser Scanning and drone-based photogrammetry point cloud data, a precise spatial foundation is established for BIM model generation. Leveraging the integrated point cloud and photographic data aids in identifying elements and construction materials, facilitating the creation of detailed technical documentation and life-like visualizations. This not only supports condition assessment and maintenance planning, but also assists in strategically planning facility expansions, renovations, or component replacements. Moreover, BIM technology streamlines facility information management by preserving vital object-related data in a structured database, enhancing overall efficiency and effectiveness. Full article

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39 pages, 7188 KiB

Open AccessReview

Georeferencing Building Information Models for BIM/GIS Integration: A Review of Methods and Tools

by Peyman Azari, Songnian Li, Ahmed Shaker and Shahram Sattar

ISPRS Int. J. Geo-Inf. 2025, 14(5), 180; https://doi.org/10.3390/ijgi14050180 - 22 Apr 2025

Abstract

With the rise of urban digital twins and smart cities, the integration of building information modeling (BIM) and geospatial information systems (GISs) have captured the interest of researchers. Although significant advancements have been achieved in this field, challenges persist in the georeferencing of [...] Read more.

With the rise of urban digital twins and smart cities, the integration of building information modeling (BIM) and geospatial information systems (GISs) have captured the interest of researchers. Although significant advancements have been achieved in this field, challenges persist in the georeferencing of BIM models, which is one of the fundamental challenges in integrating BIM and GIS models. These challenges stem from dissimilarities between the BIM and GIS domains, including different georeferencing definitions, different coordinate systems utilization, and a lack of correspondence between the engineering system of BIM and the project’s geographical location. This review critically examines the significance of georeferencing within this integration, outlines and compares various methods for georeferencing BIM data in detail, and surveys existing software tools that facilitate this process. The findings underscore the need for increased attention to georeferencing issues from both domains, aiming to enhance the seamless integration of BIM and GIS. Full article

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25 pages, 11681 KiB

Open AccessArticle

Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach

by Aitor Salas-Peña and Juan Carlos García-Palomares

ISPRS Int. J. Geo-Inf. 2025, 14(4), 179; https://doi.org/10.3390/ijgi14040179 - 20 Apr 2025

Abstract

Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to [...] Read more.

Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to simulate co-evolution in a complex social network. A case study is proposed for the modelling of contractual relationships between road freight transport companies. The model employs empirical data from a survey of transport companies located in the Basque Country (Spain) and utilises the DBSCAN community detection algorithm to simulate the effect of cluster size in the network. Additionally, a local spatial association indicator is employed to identify potentially favourable environments. The model enables the evolution of the network, leading to more complex collaborative structures. By means of iterative simulations, the study demonstrates how collaborative networks self-organise by distributing activity and knowledge and evolving into complex polarised systems. Furthermore, the simulations with different minimum cluster sizes indicate that clusters benefit the agents that are part of them, although they are not a determining factor in the network participation of other non-clustered agents. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

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25 pages, 6045 KiB

Open AccessArticle

Spatial Analysis of Urban Expansion and Energy Consumption Using Nighttime Light Data: A Comparative Study of Google Earth Engine and Traditional Methods for Improved Living Spaces

by Thidapath Anucharn, Phongsakorn Hongpradit, Niti Iamchuen and Supattra Puttinaovarat

ISPRS Int. J. Geo-Inf. 2025, 14(4), 178; https://doi.org/10.3390/ijgi14040178 - 18 Apr 2025

Abstract

This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to [...] Read more.

This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to 2023 to assess urban growth dynamics. The primary objectives are to (1) evaluate the performance of GEE and UNSUP in nighttime light data processing, (2) validate urban area classification accuracy using multiple assessment metrics, and (3) examine the relationship between nighttime light intensity and electricity consumption through Pearson’s correlation analysis, thereby establishing urban growth patterns. The methodological framework incorporates a dual-threshold classification mechanism in GEE and K-means clustering in traditional geospatial software. Accuracy assessment is conducted using 256 stratified random sampling points, complemented by land use and land cover (LULC) data for ground truth validation. The results indicate that GEE consistently outperforms UNSUP, achieving overall accuracy values between 0.80 and 0.82, compared to 0.73 and 0.76 for UNSUP. The Kappa coefficient for GEE ranges from 0.60 to 0.65, whereas UNSUP demonstrates lower agreement with ground truth data (0.44–0.52). Furthermore, both approaches reveal a significant correlation between electricity consumption and nighttime light intensity, with R² = 0.9744 for GEE and R² = 0.9759 for UNSUP, confirming the efficacy of nocturnal illumination data in urban expansion monitoring. The findings indicate that urban areas in Chiang Mai have expanded by approximately 70% over the study period. This research contributes to the field by demonstrating the effectiveness of integrated geospatial methodologies in urban development analysis. The findings offer urban planners and policymakers critical insights for sustainable urban growth management and decision-making. Full article

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

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ISPRS International Journal of Geo-Information

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