Journal Description
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information
is an international, peer-reviewed, open access journal on geo-information. The journal is owned by the International Society for Photogrammetry and Remote Sensing (ISPRS) and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), GeoRef, PubAg, dblp, Astrophysics Data System, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Remote Sensing) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 35.8 days after submission; acceptance to publication is undertaken in 2.2 days (median values for papers published in this journal in the second half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.8 (2023);
5-Year Impact Factor:
3.0 (2023)
Latest Articles
PixelQuery: Efficient Distance Range Join Query Technique for Visualization Analysis
ISPRS Int. J. Geo-Inf. 2025, 14(5), 193; https://doi.org/10.3390/ijgi14050193 - 5 May 2025
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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
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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.
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Open AccessArticle
Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation
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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,
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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))
Open AccessArticle
Pattern Recognition in Urban Maps Based on Graph Structures
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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
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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.
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Open AccessArticle
Spatiotemporal Distribution and Evolution of Global World Cultural Heritage, 1972–2024
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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
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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
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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.
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Open AccessArticle
Spatiotemporal Typhoon Damage Assessment: A Multi-Task Learning Method for Location Extraction and Damage Identification from Social Media Texts
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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
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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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Open AccessArticle
Heterogeneity Analysis of Resident Demands and Public Service Facilities in Megacities of China from the Perspective of Urban Health Examination
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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
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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
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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.
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Open AccessArticle
RouteLAND: An Integrated Method and a Geoprocessing Tool for Characterizing the Dynamic Visual Landscape Along Highways
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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
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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.
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(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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Analysis of Spatial and Driving Factors of National Sanitary Resources in China Using GIS
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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,
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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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Open AccessArticle
Geospatial Framework for Assessing the Suitability and Demand for Agricultural Digital Solutions in Europe: A Tool for Informed Decision-Making
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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
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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
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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.
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Open AccessArticle
Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China
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Haihong Li and Li He
ISPRS Int. J. Geo-Inf. 2025, 14(5), 184; https://doi.org/10.3390/ijgi14050184 - 24 Apr 2025
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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
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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.
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Open AccessArticle
The Urban–Rural Education Divide: A GIS-Based Assessment of the Spatial Accessibility of High Schools in Romania
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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
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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
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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.
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Open AccessArticle
Hybrid Learning Model of Global–Local Graph Attention Network and XGBoost for Inferring Origin–Destination Flows
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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
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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
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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.
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Open AccessArticle
The Integration of Geospatial Data for the BIM-Based Inventory of a Skatepark—A Case Study
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Przemysław Klapa and Maciej Małek
ISPRS Int. J. Geo-Inf. 2025, 14(5), 181; https://doi.org/10.3390/ijgi14050181 - 24 Apr 2025
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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
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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.
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Open AccessReview
Georeferencing Building Information Models for BIM/GIS Integration: A Review of Methods and Tools
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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
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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
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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.
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Open AccessArticle
Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach
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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
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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
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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.
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(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)
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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
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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
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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.
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(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)
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A Study of the Non-Linear Relationship Between Urban Morphology and Vitality in Heritage Areas Based on Multi-Source Data and Machine Learning: A Case Study of Dalian
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He Li and Li Miao
ISPRS Int. J. Geo-Inf. 2025, 14(4), 177; https://doi.org/10.3390/ijgi14040177 - 18 Apr 2025
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The preservation of historic heritage not only fosters cultural significance and socio-economic development, but also enhances urban competitiveness. Investigating the vitality of historic urban areas is crucial for measuring their developmental attractiveness, contributing to more effective preservation and planning. However, existing research primarily
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The preservation of historic heritage not only fosters cultural significance and socio-economic development, but also enhances urban competitiveness. Investigating the vitality of historic urban areas is crucial for measuring their developmental attractiveness, contributing to more effective preservation and planning. However, existing research primarily focuses on urban areas, leaving the applicability of urban form elements to heritage sites and their influence mechanisms unclear. This study employs XGBoost and SHAP, utilizing geographic big data and deep learning techniques, to determine whether the urban form elements impacting the vitality of heritage and urban areas are the same or exhibit different spatial distributions and diurnal variations. Empirical analysis of Dalian reveals significant diurnal variations in the factors affecting vitality, along with distinct key elements for both heritage and urban areas. This study is innovative in being the first to apply deep learning methods to analyze the factors influencing the vitality of Dalian’s heritage areas at the district scale, providing theoretical support for enhancing vitality and promoting urban development.
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Open AccessArticle
Static–Dynamic Analytical Framework for Urban Health Resilience Evaluation and Influencing Factor Exploration from the Perspective of Public Health Emergencies—Case Study of 61 Cities in Mainland China
by
Meijie Chen, Mingjun Peng, Bowen Li, Zhongliang Cai and Rui Li
ISPRS Int. J. Geo-Inf. 2025, 14(4), 176; https://doi.org/10.3390/ijgi14040176 - 17 Apr 2025
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With the acceleration of urbanization, citizens are facing more pandemic challenges. A deeper understanding of constructing more resilient cities can help citizens be better prepared for potential future pandemics or disasters. In this study, a static–dynamic analytical framework for urban health resilience evaluation
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With the acceleration of urbanization, citizens are facing more pandemic challenges. A deeper understanding of constructing more resilient cities can help citizens be better prepared for potential future pandemics or disasters. In this study, a static–dynamic analytical framework for urban health resilience evaluation and influencing factor exploration was proposed, which helped not only to analyze the basic static urban health resilience (SUHRI) under normal conditions but also to evaluate the dynamic urban health resilience (DURHI) under an external epidemic shock. The epidemic dynamic model could reasonably simulate the epidemic change trend and quantitatively evaluate resistance and recovery capacity, and the proposed influencing factor exploration model improved the model fitness by filtering out the influence of population flow autocorrelation existing in the residuals. SUHRI and DUHRI, and their corresponding key influencing factors, were compared and discussed. The results of the static–dynamic integration and difference score showed that 60.6% cities within the study area had a similar performance on SUHRI and DUHRI, but there was also a typical difference: some regional hubs exhibited high SUHRI but had only mid-level DUHRI, which was attributed to stronger external disturbances such as higher population mobility. The key influencing factors for static and dynamic urban health resilience also vary. Hospital capacity and income had the strongest influence on static urban health resilience but a relatively weaker or even non-significant correlation with dynamic urban health resilience sub-indices. The extracted population flow eigenvector collection had the strongest influence on dynamic urban health resilience, as it represents the population flow connection among cities, which could reflect the information of policy response, such as policy stringency and support intensity. We hope that our study will shed some light on constructing more resilient urban systems and being prepared for future public health emergencies.
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Open AccessArticle
Three-Dimensional Outdoor Pedestrian Road Network Map Construction Based on Crowdsourced Trajectory Data
by
Jianbo Tang, Tianyu Zhang, Junjie Ding, Ke Tao, Chen Yang, Jianbing Xiang and Xia Ning
ISPRS Int. J. Geo-Inf. 2025, 14(4), 175; https://doi.org/10.3390/ijgi14040175 - 17 Apr 2025
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Due to the complexity of outdoor environments, we still face challenges in collecting up-to-date outdoor road network maps because of high data collection costs, resulting in a lack of navigation road network maps in outdoor scenarios. Existing road network extraction methods are mainly
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Due to the complexity of outdoor environments, we still face challenges in collecting up-to-date outdoor road network maps because of high data collection costs, resulting in a lack of navigation road network maps in outdoor scenarios. Existing road network extraction methods are mainly divided into trajectory data-based and remote sensing image-based methods. Due to factors such as tree occlusion, methods based on remote sensing images struggle to extract complete road information in outdoor environments. The methods based on trajectory data mainly use vehicle trajectories to extract two-dimensional roads, lacking three-dimensional (3D) road information such as elevation and slope, which are important for navigation path planning in outdoor scenarios. Given this, this paper proposes a hierarchical map construction method for extracting the three-dimensional outdoor pedestrian road network based on crowdsourced trajectory data. This method models the pedestrian road network as a graph composed of pedestrian areas, intersections, and road segments connecting these areas. Three-dimensional roads within and between the intersection areas are generated hierarchically. Experiments and comparative analyses were conducted using real-world outdoor trajectory datasets. Results show that the proposed method has higher accuracy in extracting 3D road information than existing methods.
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Open AccessArticle
Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China
by
Yingxi Chen, Yan Xu and Nannan Ye
ISPRS Int. J. Geo-Inf. 2025, 14(4), 174; https://doi.org/10.3390/ijgi14040174 - 17 Apr 2025
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Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level
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Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level units and employing spatial autocorrelation analysis, multiple linear regression, and mixed geographically weighted regression (MGWR), this study reveals the spatial heterogeneity and key driving factors of non-grainization. The results indicate strong spatial dependence, with persistent high–high clusters in economically developed southern/coastal areas and low–low clusters in northern regions. Furthermore, the driving mechanism shifted significantly over the two decades. Early constraints from natural endowments (e.g., elevation’s positive impact significantly weakened post 2010) and individual economics diminished with technological progress, while macroeconomic development became dominant, influencing both scale and structure. Infrastructure improvements (reflected by rural electricity use) consistently limited non-grainization; some factors showed phased effects, and annual mean precipitation emerged as a significant influence in 2020. MGWR revealed substantial, dynamic spatial heterogeneity in these drivers’ impacts across different periods. These findings highlight the importance of geoinformation tools in managing regional disparities. Integrating spatial and socio-economic analysis offers practical insights for policymakers to develop targeted strategies that balance food security with agricultural diversification.
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