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Volume 14
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ISPRS Int. J. Geo-Inf., Volume 14, Issue 6 (June 2025) – 28 articles

Cover Story (view full-size image): Integrating spatio-temporal kriging with machine learning enhances overall estimation accuracy by addressing complex spatial and temporal variations. However, the improvements are largely attributed to the effective modeling of global trends, which is not sufficient to justify the selection of kriging methods. This study evaluates the effectiveness of combining these approaches, focusing on their impact on overall accuracy and the contributions to global trends and spatio-temporal interactions. The results indicate the integration improves overall accuracy. Polynomial regression produces smoother patterns by capturing gradual variations in global trends, whereas random forest and boosting yield more abrupt patterns. This study highlights the importance of considering the sources of variation in phenomena when selecting kriging methods. View this paper
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27 pages, 1354 KiB  
Review
High-Resolution Global Land Cover Maps and Their Assessment Strategies
by Qiongjie Xu, Vasil Yordanov, Lorenzo Bruzzone and Maria Antonia Brovelli
ISPRS Int. J. Geo-Inf. 2025, 14(6), 235; https://doi.org/10.3390/ijgi14060235 - 18 Jun 2025
Viewed by 1295
Abstract
Global High-Resolution Land Cover Maps (GHRLCs), characterized by spatial resolutions higher than 30 m per pixel, have become essential tools for environmental monitoring, urban planning, and climate modeling. Over the past two decades, new GHRLCs have emerged, offering increasingly detailed and timely representations [...] Read more.
Global High-Resolution Land Cover Maps (GHRLCs), characterized by spatial resolutions higher than 30 m per pixel, have become essential tools for environmental monitoring, urban planning, and climate modeling. Over the past two decades, new GHRLCs have emerged, offering increasingly detailed and timely representations of Earth’s surface. This review provides an in-depth analysis of recent developments by examining the data sources, methodologies, and validation techniques utilized in 19 global binary and multi-class land cover products. The evolution of GHRLC production techniques is analyzed, starting from the use of singular source input data, such as multi-temporal remotely sensed optical imagery, to the integration of satellite radar and other geospatial data. The article highlights significant advances in data pre-processing and processing, showcasing a shift from classical methods to modern approaches, including machine learning (ML) and deep learning techniques (e.g., neural networks and transformers), and their direct application on powerful cloud-computing platforms. A comprehensive analysis of the temporal dimension of land cover products, where available, is conducted, highlighting a shift from decadal intervals to production intervals of less than a month. This review also addresses the ongoing challenge of land cover legend harmonization, a topic that remains crucial for ensuring consistency and comparability across datasets. Validation remains another critical aspect of GHRLC production. The methods used to assess map accuracy and reliability, including statistical techniques and visual inspections, are briefly discussed. The validation approaches adopted in recent studies are summarized, with an emphasis on their importance in maintaining data integrity and addressing emerging needs, such as the development of common validation datasets. Ultimately, this review aims to provide a comprehensive overview of the current state and future directions of GHRLC production and validation, highlighting the advancements that have shaped this rapidly evolving field. Full article
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26 pages, 11841 KiB  
Article
Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach
by Fengwei Jiao, Longgang Xiang and Yuanyuan Deng
ISPRS Int. J. Geo-Inf. 2025, 14(6), 234; https://doi.org/10.3390/ijgi14060234 - 17 Jun 2025
Viewed by 598
Abstract
The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of [...] Read more.
The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of overlapping roads as intersections. To address these limitations, we propose a forward and reverse tracking method for high-accuracy road interchange network generation. First, raw crowdsourced trajectory data is preprocessed by filtering out non-interchange trajectories and removing abnormal data based on both static and dynamic characteristics of the trajectories. Next, road subgraphs are extracted by identifying potential transition nodes, which are verified using directional and distribution information. Trajectory bifurcation is then performed at these nodes. Finally, a two-stage fusion process combines forward and reverse tracking results to produce a geometrically complete and topologically accurate road interchange network. Experiments using crowdsourced trajectory data from Shenzhen demonstrated highly accurate results, with 95.26% precision in geometric road network alignment and 90.06% accuracy in representing the connectivity of road interchange structures. Compared to existing methods, our approach enhanced accuracy in spatial alignment by 13.3% and improved the correctness of structural connections by 12.1%. The approach demonstrates strong performance across different types of interchanges, including cloverleaf, turbo, and trumpet interchanges. Full article
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21 pages, 14149 KiB  
Article
Spatial Proximity Relations-Driven Semantic Representation for Geospatial Entity Categories
by Yongbin Tan, Hong Wang, Rongfeng Cai, Lingling Gao, Zhonghai Yu and Xin Li
ISPRS Int. J. Geo-Inf. 2025, 14(6), 233; https://doi.org/10.3390/ijgi14060233 - 16 Jun 2025
Viewed by 274
Abstract
Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge and help [...] Read more.
Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge and help achieve the deep fusion of geospatial data. In this paper, a method for the semantic representation of the geospatial entity categories (denoted as feature embedding) is presented, taking advantage of the characteristic that regions with similar distributions of geospatial entity categories also have a certain level of similarity. To construct the entity category embedding, a spatial proximity graph of entities and an adjacency matrix of entity categories are created using a large number of geospatial entities obtained from OSM (OpenStreetMap). The cosine similarity algorithm is then employed to measure the similarity between these embeddings. Comparison experiments are then conducted by comparing the similarity results from the standard model. The results show that the results of this model are basically consistent with the standard model (Pearson correlation coefficient = 0.7487), which verifies the effectiveness of the feature embedding extracted by this model. Based on this, this paper applies the feature embedding to the regional similarity task, which verifies the feasibility of using the model in the downstream task. It provides a new idea for realizing the formal expression of the unsupervised entity category semantics. Full article
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24 pages, 153371 KiB  
Article
A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis
by Maximilian Kleebauer, Stefan Karamanski, Doron Callies and Martin Braun
ISPRS Int. J. Geo-Inf. 2025, 14(6), 232; https://doi.org/10.3390/ijgi14060232 - 12 Jun 2025
Viewed by 608
Abstract
Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a [...] Read more.
Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap (OSM) data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. Of this, more than 3.6 GW is currently operational. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development. The dataset is publicly available. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))
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23 pages, 11228 KiB  
Article
R-MLGTI: A Grid- and R-Tree-Based Hybrid Index for Unevenly Distributed Spatial Data
by Yuqin Li, Jining Yan, Xiaohui Huang, Xiangyou He, Ze Deng and Yunliang Chen
ISPRS Int. J. Geo-Inf. 2025, 14(6), 231; https://doi.org/10.3390/ijgi14060231 - 12 Jun 2025
Viewed by 323
Abstract
In recent years, with the development of sensor technology, the volume of spatial data has grown exponentially. However, this data is often unevenly distributed, and traditional indexing methods cannot predict the overall data distribution when data are continuously inserted into the database. This [...] Read more.
In recent years, with the development of sensor technology, the volume of spatial data has grown exponentially. However, this data is often unevenly distributed, and traditional indexing methods cannot predict the overall data distribution when data are continuously inserted into the database. This makes them inefficient for indexing large-scale, unevenly distributed spatial data. This paper proposes a hybrid indexing method based on the grid-indexing and R-tree methods, called R-MLGTI (R-Multi-Level Grid–Tree Index). The method first divides the two-dimensional space using the Z-curve to form multiple sub-grid regions. When incrementally inserting data, R-MLGTI calculates the grid encoding of the data and computes the c(G) of the corresponding grid G to measure the sparsity or density within the grid region, where c(G) is a metric that quantifies the data density within grid G. All data in sparse grids are indexed by R-trees associated with grid encodings. In dense grid areas, a finer-grained space-filling curve is recursively applied for further spatial division. This process forms multiple sub-grids until the data within all sub-grids becomes sparse, at which point the original data is re-indexed according to the sparse grids. Finally, this paper presents a prototype system of the in-memory R-MLGTI and conducts benchmark tests for incremental data import and range queries. The incremental data insertion performance of R-MLGTI is lower than that of the grid-indexing and R-tree methods; however, on various unevenly distributed simulated datasets, the average query time for different query regions in R-MLGTI is about 6.49% faster than that of the grid-indexing method and about 51.78% faster than that of the R-tree method. On a real dataset, Landsat 7 EMT, which contains 2,585,203 records, the average query time for various query ranges is 61.39% faster than that of the grid-indexing method and 17.01% faster than that of the R-tree method. Experiments show that R-MLGTI performs better than the traditional R-tree and grid-indexing methods in large-scale, unevenly distributed spatial data query requests. Full article
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21 pages, 6295 KiB  
Article
A Fourier Fitting Method for Floating Vehicle Trajectory Lines
by Yun Shuai, Pengcheng Liu and Hao Han
ISPRS Int. J. Geo-Inf. 2025, 14(6), 230; https://doi.org/10.3390/ijgi14060230 - 11 Jun 2025
Viewed by 324
Abstract
With the advancement of spatial positioning technology, trajectory data have been growing rapidly. Trajectory data record the spatiotemporal information and behavioral characteristics of moving objects, and in-depth analysis can provide decision support for urban transportation. This paper explores effective methods for trajectory data [...] Read more.
With the advancement of spatial positioning technology, trajectory data have been growing rapidly. Trajectory data record the spatiotemporal information and behavioral characteristics of moving objects, and in-depth analysis can provide decision support for urban transportation. This paper explores effective methods for trajectory data representation, with a focus on the study of data fitting methods. Data fitting can extract key information and reveal underlying patterns, and the use of fitting methods can significantly improve the efficiency and accuracy of spatiotemporal trajectory data analysis, offering new perspectives and methodological support for related research fields. This paper integrates road network data to enhance trajectory data, treating trajectory data as a dynamic signal that changes over time. Through Fourier transformation, the data are converted from the time domain to the frequency domain, and trajectory points are fitted in the frequency spectrum domain, transforming discrete trajectory points into time-continuous linear elements. By referencing the minimum visually discernible distance and velocity precision requirements at a specific scale, thresholds for positional and velocity errors are set. The similarity between the Fourier-fitted trajectory and the original trajectory is measured in both spatial and temporal dimensions. By calculating the number of expansion terms of the Fourier series at a specific spatiotemporal scale, a functional relationship between the number of expansion terms, duration, and distance is fitted within the set threshold range (R2 = 0.8424). This enables the Fourier series representation of any trajectory data under specific positional and velocity error thresholds. The errors in position and velocity obtained using this expression are significantly lower than the theoretical errors. The experimental results demonstrate that the Fourier fitting method exhibits strong generality and precision, effectively approximating the original trajectory, and provides a robust mathematical foundation for the quantification and detailed analysis of trajectory data. Full article
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16 pages, 2754 KiB  
Article
A Spatial Decision Support Model for Fire Station Construction Prioritization Under Resource Constraints
by Yuan Zeng, Dingli Liu, Diping Yuan, Weijun Liu, Guohua Wu and Xiao Lei
ISPRS Int. J. Geo-Inf. 2025, 14(6), 229; https://doi.org/10.3390/ijgi14060229 - 10 Jun 2025
Viewed by 341
Abstract
Governments often plan multiple fire stations simultaneously to improve firefighting capabilities, but constructing them within limited resources and time remains a challenge. A spatial decision support model is proposed in this study to determine the prioritized construction sequence of urban fire stations. Two [...] Read more.
Governments often plan multiple fire stations simultaneously to improve firefighting capabilities, but constructing them within limited resources and time remains a challenge. A spatial decision support model is proposed in this study to determine the prioritized construction sequence of urban fire stations. Two simulation environments were established: one with only existing fire stations and another with both existing and proposed stations as fire service supply points (FSSPs). Response times were simulated using real-time traffic data. The construction urgency of the proposed fire stations was assessed using the construction sequence scoring equation. To validate the model, a case study of Shaoyang City, China, was conducted. A total of 30,968 fire service demand points were gathered, with 20 existing fire stations and 13 proposed fire stations designated as FSSPs. Twenty-five evaluation scenarios were established, resulting in 1,297,025 valid simulation results. The scoring results revealed a maximum score of 119,320, a minimum of 23,420, and an average of 61,412. Based on these results, recommendations for the construction sequence of proposed fire stations in Shaoyang City were made, and the improvements in fire protection levels were calculated. By prioritizing the construction of higher-performance fire stations, this model maximizes resource efficiency and enhances public safety. Full article
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19 pages, 11978 KiB  
Article
Spatiotemporal Patterns of Greening and Their Correlation with Surface Radiative Forcing on the Tibetan Plateau from 1982 to 2021
by Junshan Guo, Kai Wu, Han Yang and Yao Shen
ISPRS Int. J. Geo-Inf. 2025, 14(6), 228; https://doi.org/10.3390/ijgi14060228 - 10 Jun 2025
Viewed by 385
Abstract
Vegetation change profoundly influences ecosystem sustainability and human activities, with solar radiation serving as a primary driver. However, the effects of surface radiative forcing (SRF) and related factors on vegetation dynamics remain poorly understood. The Tibetan Plateau, a climate-sensitive region, offers a unique [...] Read more.
Vegetation change profoundly influences ecosystem sustainability and human activities, with solar radiation serving as a primary driver. However, the effects of surface radiative forcing (SRF) and related factors on vegetation dynamics remain poorly understood. The Tibetan Plateau, a climate-sensitive region, offers a unique context to investigate these relationships. This study analyzes the association between vegetation greening and SRF on the Tibetan Plateau from 1982 to 2021. Using forecast albedo (FAL) and surface solar radiation downwards (SSRD), we calculated SRF and explored its correlation with the Normalized Difference Vegetation Index (NDVI) and land cover data. The results indicate a gradual increase in growing-season NDVI, suggesting vegetation greening. Both FAL and SSRD exhibit decreasing trends, yet neither shows a statistically significant correlation with NDVI. The correlations between FAL/SSRD and NDVI weaken with increasing altitude, declining by 0.035 × 10−1 per 500 m and 0.021 × 10−1 per 500 m, respectively. Among vegetation types, FAL correlates most strongly with shrubland NDVI and weakest with forest NDVI, while SSRD demonstrates the highest correlation with grassland NDVI and lowest with forest NDVI. The impact of SRF on NDVI changes is evident in 52.881% of the plateau, showing a positive correlation between SRF and ΔNDVI, compared to 39.589% for SSRD and ΔNDVI. This research enhances the understanding of vegetation responses to FAL, SSRD, and SRF, providing a scientific basis for ecological conservation and climate adaptation strategies, and also emphasizes radiation–vegetation feedback, providing guidance for conservation strategies in other alpine ecosystems globally, such as the Andes and Alps, where elevation gradients and vegetation-type-specific responses to radiative forcing may similarly govern ecological outcomes. Full article
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23 pages, 2863 KiB  
Article
A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses
by Pengpeng Li, Qing Zhu, Jiping Liu, Tao Liu, Ping Du, Shuangtong Liu and Yuting Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 227; https://doi.org/10.3390/ijgi14060227 - 9 Jun 2025
Viewed by 410
Abstract
Accurate address matching is crucial for the analysis, integration, and intelligent management of urban geospatial data and is also a key step in achieving geocoding. However, due to the complexity, diversity, and irregularity of address expression, address matching becomes a challenging task. This [...] Read more.
Accurate address matching is crucial for the analysis, integration, and intelligent management of urban geospatial data and is also a key step in achieving geocoding. However, due to the complexity, diversity, and irregularity of address expression, address matching becomes a challenging task. This paper proposes a multi-semantic feature fusion method for complex address matching of Chinese addresses that formulates address matching as a classification task that directly predicts whether two addresses refer to the same location, without relying on predefined similarity thresholds. First, the address is resolved into address elements, and the Word2vec model is trained to generate word vector representations using these address elements. Then, multi-semantic features of the addresses are extracted using a Text Recurrent Convolutional Neural Network (Text-RCNN) and a Graph Attention Network (GAT). Finally, the Enhanced Sequential Inference Model (ESIM) is used to perform both local inference and inference composition on the multi-semantic features of the addresses to achieve accurate matching of addresses. Experiments were conducted using Points of Interest (POI) address data from Baidu Maps, Tencent Maps, and Amap within the Chengdu area. The results demonstrate that the proposed method outperforms existing address matching methods, with precision, recall, and F1 values all exceeding 95%. In addition, transfer experiments using datasets from five other cities including Beijing, Shanghai, Xi’an, Guangzhou, and Wuhan show that the model maintains strong generalization ability, achieving F1 values above 84% in cities such as Xi’an and Wuhan. Full article
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21 pages, 4318 KiB  
Article
A Network Approach for Discovering Spatially Associated Objects
by Changfeng Jing, Tao Liang, Yunlong Feng, Jianing Li, Sensen Wu, Jiale Ding, Gaoran Xu and Yang Hu
ISPRS Int. J. Geo-Inf. 2025, 14(6), 226; https://doi.org/10.3390/ijgi14060226 - 8 Jun 2025
Viewed by 420
Abstract
Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. [...] Read more.
Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an approach named the Weighted Similarity measure method considering Topological Accessibility (WSTA) is proposed to measure object association. The WSTA can capture both spatial interaction patterns and topological relationships in complex urban environments, thereby improving the accuracy of spatially associated object discovery. The proposed approach is validated using real-world point-of-interest (POI) datasets from Beijing city. The results suggest that integrating topological relationship approaches yields significant accuracy improvements in existing baseline methods, thereby enriching geospatial data retrieval in the era of big geospatial data. Full article
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27 pages, 9628 KiB  
Article
Exploring the Nonlinear Impacts of Built Environment on Urban Vitality from a Spatiotemporal Perspective at the Block Scale in Chongqing
by Jiayu Yang and Enxu Wang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 225; https://doi.org/10.3390/ijgi14060225 - 7 Jun 2025
Viewed by 561
Abstract
Examining the relationship between built environment (BE) and urban vitality (UV) is beneficial for promoting urban planning, as it deepens the understanding of how spatial design shapes urban life and activity patterns. However, the nonlinear effects of BE on UV from a spatiotemporal [...] Read more.
Examining the relationship between built environment (BE) and urban vitality (UV) is beneficial for promoting urban planning, as it deepens the understanding of how spatial design shapes urban life and activity patterns. However, the nonlinear effects of BE on UV from a spatiotemporal perspective have not been fully explored. In this study, the central urban area of Chongqing at the block scale is selected as a research case. The Gradient Boosting Decision Tree with SHapley Additive exPlanations (GBDT-SHAP) model is used to examine the nonlinear impacts of BE on UV. The results show the following: (1) The BE has a stronger overall impact on UV during holidays. Road intersection density (RID) has the greatest impact on UV on weekdays and holidays, building density (BD) has the greatest impact on weekend mornings, cultural and leisure accessibility (CLA) has the greatest impact on weekend afternoons, and commercial accessibility (CA) has the most significant impact on weekend evenings; (2) the impacts of the BE on UV exhibit significant nonlinear characteristics, with BD and park and square accessibility (PSA) showing a first increasing and then inhibiting effect on UV; lower CA, CLA, and MSA have inhibitory effects on UV, with higher normalized difference vegetation index (NDVI) values similarly demonstrating such effects; building height (BH), bus stop density (BSD), road network density (RD), and RID have enhancing effects on UV; functional mix degree (FMD) and water proximity index (WPI) show different trends in different time periods; (3) there are significant interactive effects among BE such as BD and BH, CA; RD and WPI, MSA; FMD and BH, PSA; PSA and CLA. A comprehensive understanding of these interactive relationships is crucial for optimizing the BE to enhance UV. This study provides a theoretical basis for urban planners to develop more effective, time-sensitive strategies. Future research should explore these nonlinear and interactive effects across different cities and scales to further generalize the findings. Full article
(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—2nd Edition)
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13 pages, 2203 KiB  
Article
Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation
by Min Jeong and Hyeongmo Koo
ISPRS Int. J. Geo-Inf. 2025, 14(6), 224; https://doi.org/10.3390/ijgi14060224 - 5 Jun 2025
Viewed by 605
Abstract
Integrating spatio-temporal kriging with machine learning improves estimation accuracy by addressing complex spatial and temporal variations in spatio-temporal phenomena. The improvement can be attributed to the enhanced flexibility of machine learning in capturing non-linear global trends, which traditional methods struggle to model, while [...] Read more.
Integrating spatio-temporal kriging with machine learning improves estimation accuracy by addressing complex spatial and temporal variations in spatio-temporal phenomena. The improvement can be attributed to the enhanced flexibility of machine learning in capturing non-linear global trends, which traditional methods struggle to model, while kriging remains effective in representing spatio-temporal interactions. However, differences in the estimated global trends and spatio-temporal interactions resulting from applying machine learning may influence the spatio-temporal variation patterns of the kriging results. Therefore, this study evaluates the effectiveness of machine learning in spatio-temporal kriging using NO2 concentrations in Seoul, focusing on its impact on overall accuracy and the contributions to global trends and spatio-temporal interactions. The results show that integrating machine learning enhances overall accuracy relative to ordinary spatio-temporal kriging. Global trend estimates differ by the models, with polynomial regression producing smoother patterns but larger errors, while random forest and boosting yield more abrupt patterns with smaller errors. These differences lead to smoother kriging outcomes in the polynomial model and more discrete patterns in the ensemble-based models. This study highlights the importance of considering both overall estimation accuracy and spatio-temporal patterns when selecting kriging methods. Full article
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18 pages, 6877 KiB  
Article
Machine Learning-Enhanced 3D GIS Urban Noise Mapping with Multi-Modal Factors
by Jianping Pan, Yuzhe He, Wei Ma, Shengwang An, Lu Li, Dan Huang and Dunxin Jia
ISPRS Int. J. Geo-Inf. 2025, 14(6), 223; https://doi.org/10.3390/ijgi14060223 - 4 Jun 2025
Viewed by 595
Abstract
Geographic Information System (GIS)-based noise management is crucial in urban environments as it provides precise spatial analysis, helping to identify noise hotspots and optimize noise control measures. By integrating noise propagation models with GIS technology, dynamic simulation and visualization of noise distribution can [...] Read more.
Geographic Information System (GIS)-based noise management is crucial in urban environments as it provides precise spatial analysis, helping to identify noise hotspots and optimize noise control measures. By integrating noise propagation models with GIS technology, dynamic simulation and visualization of noise distribution can be achieved, offering scientific support for urban planning and noise management. Most existing noise prediction models fail to fully account for three-dimensional (3D) spatial information and a wide range of environmental factors. As a result, there are often discrepancies between the actual noise measurements at monitoring points and the predicted values generated by these models. Furthermore, there is a lack of a system that can effectively integrate noise data with three-dimensional scenes for simulation. This paper proposes a new method to simulate urban noise propagation, aiming to achieve more accurate noise prediction and visualization in a three-dimensional environment. First, we computed the preliminary noise propagation based on a traffic noise model. Next, machine learning techniques were applied to analyze the relationship between noise discrepancies and multi-modal factors, thereby improving the accuracy of environmental noise level estimation. Based on this, we developed an urban noise simulation system. The system integrates functions such as noise simulation, traffic simulation, and weather changes, enabling accurate noise visualization within a three-dimensional virtual environment. Experimental results demonstrate that this method enhances the accuracy of urban noise prediction and visualization, providing users with a more comprehensive understanding of the spatial distribution of urban noise. Full article
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22 pages, 7036 KiB  
Article
Clustering Method for Edge and Inner Buildings Based on DGI Model and Graph Traversal
by Hesheng Huang and Yijun Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 222; https://doi.org/10.3390/ijgi14060222 - 3 Jun 2025
Viewed by 307
Abstract
Accurate clustering of buildings is a prerequisite for map generalization in densely populated urban data. Edge buildings at the edge of building groups, identified through human-eye recognition, may serve as boundary constraints for clustering. This paper proposes the use of seven Gestalt factors [...] Read more.
Accurate clustering of buildings is a prerequisite for map generalization in densely populated urban data. Edge buildings at the edge of building groups, identified through human-eye recognition, may serve as boundary constraints for clustering. This paper proposes the use of seven Gestalt factors to distinguish edge buildings from other buildings. Employing the DGI model to produce high-quality node embeddings, optimize the mutual information between the local node representation and the global summary vector. We then conduct training to identify edge buildings in the two test datasets using eight feature combinations. This research introduces a modified distance metric called the ‘m_dis’ feature, which is used to describe the closeness between two adjacent buildings. Finally, the clusters of edge and inner buildings are determined through a constrained graph traversal that is based on the ‘m_dis’ feature. This method is capable of effectively identifying and distinguishing densely distributed building groups in Chengdu City, China, as demonstrated by experimental results. It offers novel concepts for edge building recognition in dense urban areas, confirms the significance of the LOF factor and the ‘m_dis’ feature, and achieves superior clustering results in comparison to other methods. Additionally, this semi-supervised clustering method (DGI-EIC) has the potential to achieve an ARI index of approximately 0.5. Full article
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23 pages, 8631 KiB  
Article
Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times
by Mikel Barrena-Herrán, Itziar Modrego-Monforte and Olatz Grijalba
ISPRS Int. J. Geo-Inf. 2025, 14(6), 221; https://doi.org/10.3390/ijgi14060221 - 3 Jun 2025
Viewed by 750
Abstract
Extensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through geotemporal information, providing deeper insights into the underlying causes of urban vibrancy. This study [...] Read more.
Extensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through geotemporal information, providing deeper insights into the underlying causes of urban vibrancy. This study presents a methodology for analyzing the spatiotemporal use of cities and identifying occupancy patterns taking into consideration urban form and function. The analysis relies on data obtained from Google Popular Times (GPT), transforming the relative occupancy of a large number of points of interest (POI) classified into five categories, for estimating the number of people aggregated within urban nodes during a typical day. As a result, this research assesses the utility of this data source for evaluating the changing dynamics of a city across both space and time. The methodology employs geographic information system (GIS) tools and artificial intelligence techniques. The results demonstrate that by analyzing geotemporal data, we can classify urban nodes according to their hourly activity patterns. These patterns, in turn, relate to city form and urban activities, showing a certain spatial concentration. This research contributes to the growing body of knowledge on machine learning (ML) methods for spatiotemporal modeling, laying the groundwork for future studies that can further explore the complexity of urban phenomena. Full article
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15 pages, 3095 KiB  
Article
A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island
by Pavlos Fylaktos, George P. Petropoulos and Ioannis Lemesios
ISPRS Int. J. Geo-Inf. 2025, 14(6), 220; https://doi.org/10.3390/ijgi14060220 - 2 Jun 2025
Viewed by 538
Abstract
The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, [...] Read more.
The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline. Full article
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17 pages, 3458 KiB  
Article
Viewpoint Selection for 3D Scenes in Map Narratives
by Shichuan Liu, Yong Wang, Qing Tang and Yaoyao Han
ISPRS Int. J. Geo-Inf. 2025, 14(6), 219; https://doi.org/10.3390/ijgi14060219 - 31 May 2025
Viewed by 295
Abstract
Narrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection is heavily reliant on the cartographer’s subjective interpretation of the event. Even with fixed-angle settings, the task [...] Read more.
Narrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection is heavily reliant on the cartographer’s subjective interpretation of the event. Even with fixed-angle settings, the task of ensuring that selected viewpoints align with the narrative theme remains challenging. To address this, an automated viewpoint selection method constrained by narrative relevance and visual information is proposed. Narrative relevance is determined by calculating spatial distances between each element and the thematic element within the scene. Visual information is quantified by assessing the visual salience of elements as the ratio of their projected area on the view window to their total area. Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. Experimental results indicate that, compared to the maximum projected area method and fixed-value method, a higher viewpoint fitness is achieved by this approach. The narrative views generated by this method were positively recognized by approximately two-thirds of invited professionals. This process aligns effectively with narrative visualization needs, enhances 3D narrative map creation efficiency, and offers a robust strategy for viewpoint selection in 3D scene-based narrative mapping. Full article
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19 pages, 8169 KiB  
Article
Exploring the Application of NeRF in Enhancing Post-Disaster Response: A Case Study of the Sasebo Landslide in Japan
by Jinge Zhang, Yan Du, Yujing Jiang, Sunhao Zhang, Hongbin Chen and Dongqi Shang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 218; https://doi.org/10.3390/ijgi14060218 - 30 May 2025
Viewed by 391
Abstract
Rapid acquisition of 3D reconstruction models of landslides is crucial for post-disaster emergency response and rescue operations. This study explores the application potential of Neural Radiance Fields (NeRF) technology for rapid post-disaster site modeling and performs a comparative analysis with traditional photogrammetry methods. [...] Read more.
Rapid acquisition of 3D reconstruction models of landslides is crucial for post-disaster emergency response and rescue operations. This study explores the application potential of Neural Radiance Fields (NeRF) technology for rapid post-disaster site modeling and performs a comparative analysis with traditional photogrammetry methods. Taking a landslide induced by heavy rainfall in Sasebo City, Japan, as a case study, this research utilizes drone-acquired video imagery data and employs two different 3D reconstruction techniques to create digital models of the landslide area. Visual realism and point cloud detail were compared. The results indicate that the high-capacity NeRF model (NeRF 24G) approaches or even surpasses traditional photogrammetry in visual realism under certain scenarios; however, the generated point clouds are inferior in terms of detail compared to those produced by traditional photogrammetry. Nevertheless, NeRF significantly reduces the modeling time. NeRF 6G can generate a point cloud of engineering-useful accuracy in only 45 min, providing a 3D overview of the disaster site to support emergency response efforts. In the future, integrating the advantages of both methods could enable rapid and precise post-disaster 3D reconstruction. Full article
(This article belongs to the Topic Geotechnics for Hazard Mitigation)
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22 pages, 25402 KiB  
Article
Site Selection Analysis and Prediction of New Retail Stores from an Urban Commercial Space Perspective: A Case Study of Luckin Coffee and Starbucks in Shanghai
by Zhengxu Zhao, Gang Chen, Jianshu Duan and Youheng Xu
ISPRS Int. J. Geo-Inf. 2025, 14(6), 217; https://doi.org/10.3390/ijgi14060217 - 30 May 2025
Viewed by 1085
Abstract
In the context of digital transformation, examining the differences in commercial site selection and the factors influencing these decisions holds significant practical value for understanding market adaptation strategies across varying business models and predicting future industry trends. This study divides the research area [...] Read more.
In the context of digital transformation, examining the differences in commercial site selection and the factors influencing these decisions holds significant practical value for understanding market adaptation strategies across varying business models and predicting future industry trends. This study divides the research area into 100 m × 100 m grids and employs a random forest model and related interpretability methods to conduct an empirical analysis of the site selection and influencing factors of Luckin Coffee and Starbucks stores in Shanghai. By integrating the prediction results with existing planning documents, this study achieves a coupling between urban spatial structure and location strategies. The findings indicate the following: (1) The random forest model demonstrates high accuracy in predicting new retail store locations, with an accuracy rate of 90.0% for Luckin Coffee and 92.2% for Starbucks. (2) The influence of traditional factors on the expansion of new retail coffee stores is declining, while Luckin Coffee’s layout demonstrates a stronger reliance on urban functional zones. (3) Relative suitability is derived by calculating the difference between the predicted probability values and the normalized kernel density values. In the central activity areas of the city, the relationship between site selection probability and suitability exhibits an inverse correlation, with Starbucks generally showing higher relative suitability overall. (4) Suitable areas for both brands’ site selections are spatially contiguous and integrated within the urban fabric, which suggests significant growth potential for both brands in the main urban areas. This study not only focuses on commercial optimization but also offers theoretical and methodological insights by exploring how different retail models interact with urban spatial structures, thereby contributing to the fields of retail geography and spatial governance. Full article
(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)
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21 pages, 20433 KiB  
Article
Micro-Terrain Recognition Method of Transmission Lines Based on Improved UNet++
by Feng Yi and Chunchun Hu
ISPRS Int. J. Geo-Inf. 2025, 14(6), 216; https://doi.org/10.3390/ijgi14060216 - 30 May 2025
Viewed by 313
Abstract
Micro-terrain recognition plays a crucial role in the planning, design, and safe operation of transmission lines. To achieve intelligent and automatic recognition of micro-terrain surrounding transmission lines, this paper proposes an improved semantic segmentation model based on UNet++. This model expands the single [...] Read more.
Micro-terrain recognition plays a crucial role in the planning, design, and safe operation of transmission lines. To achieve intelligent and automatic recognition of micro-terrain surrounding transmission lines, this paper proposes an improved semantic segmentation model based on UNet++. This model expands the single encoder into multiple encoders to accommodate the input of multi-source geographic features and introduces a gated fusion module (GFM) to effectively integrate the data from diverse sources. Additionally, the model incorporates a dual attention network (DA-Net) and a deep supervision strategy to enhance performance and robustness. The multi-source dataset used for the experiment includes the Digital Elevation Model (DEM), Elevation Coefficient of Variation (ECV), and profile curvature. The experimental results of the model comparison indicate that the improved model outperforms common semantic segmentation models in terms of multiple evaluation metrics, with pixel accuracy (PA) and intersection over union (IoU) reaching 92.26% and 85.63%, respectively. Notably, the performance in identifying the saddle and alpine watershed types has been enhanced significantly by the improved model. The ablation experiment results confirm that the introduced modules contribute to enhancing the model’s segmentation performance. Compared to the baseline network, the improved model enhances PA and IoU by 1.75% and 2.96%, respectively. Full article
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26 pages, 4590 KiB  
Article
Hierarchical Data Visualization Based on Rectangular Cartograms
by Lina Wang, Haoxun Yuan, Xiang Li, Yaru Li, Danfei Zhang and Haoqi Hu
ISPRS Int. J. Geo-Inf. 2025, 14(6), 215; https://doi.org/10.3390/ijgi14060215 - 30 May 2025
Viewed by 365
Abstract
As the diversity and complexity of geographic statistical data continue to increase, it becomes increasingly important to present multi-level information in order to meet a broader range of needs. In response to the limitations of existing visualization methods in representing the geographic distribution [...] Read more.
As the diversity and complexity of geographic statistical data continue to increase, it becomes increasingly important to present multi-level information in order to meet a broader range of needs. In response to the limitations of existing visualization methods in representing the geographic distribution of statistical data, this paper proposes a geographical hierarchical data visualization method based on rectangular cartograms. First, a new rectangular cartograms construction algorithm is adopted in this paper, which can effectively preserve relatively accurate orientation and adjacency relationships between geographic regions, while also effectively preserving the statistical data features. Then, a treemap layout algorithm is applied within the rectangular cartogram to further partition the geographic regions, thereby visualizing the hierarchical structure of the data. Through experimental validation using real datasets and usability testing, the results demonstrate that the method presented in this paper excels in geographic distribution representation, hierarchical relationship visualization, and information readability. Compared to traditional thematic map methods, this approach demonstrates significant advantages in terms of information transmission efficiency and shows promising performance in expressive effectiveness, providing strong support for the analysis and decision making of geographical hierarchical data. Full article
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22 pages, 8463 KiB  
Article
GeoFAN: Point Pattern Recognition in Spatial Vector Data
by Zhuoyi Yang, Zeyi Li, Haitao Zhang, Wei Zhang, Yanwei Wang and Yihang Huang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 214; https://doi.org/10.3390/ijgi14060214 - 29 May 2025
Viewed by 304
Abstract
The recognition of point patterns in spatial vector data has important applications in geographic mapping and formation recognition. However, the application of traditional methods to spatial vector data faces two difficulties. Firstly, these data are low signal-to-noise ratio data in which the point [...] Read more.
The recognition of point patterns in spatial vector data has important applications in geographic mapping and formation recognition. However, the application of traditional methods to spatial vector data faces two difficulties. Firstly, these data are low signal-to-noise ratio data in which the point patterns are mixed with a large number of normal point clusters; thus, it is difficult to recognize point patterns from these unstructured data using traditional clustering or machine learning methods. Secondly, the lack of edge connectivity relationships in spatial vector data directly hinders the application of graph models. Few studies have systematically solved the above difficulties. In this article, we propose a geometric feature attention scheme to overcome the above challenges. We also present an implementation of the scheme based on the graph method, termed GeoFAN, to extract and classify point patterns simultaneously in spatial vector data. Firstly, the raw data are transformed into a graph structure consisting of adjacency and attribute matrices. Secondly, a geometric feature attention module is proposed to enhance the feature representation of point patterns. Finally, the recognition results of all points are output via GeoFAN. The macro precision, recall, and F1 score of five simulated point pattern types with different attributes and point numbers are 92.8%, 90.3%, and 91.5%, respectively, and GeoFAN is trained with simulated data to recognize real location-based point patterns successfully. The proposed GeoFAN showed superior performance and generalization ability in point pattern recognition. Full article
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24 pages, 4868 KiB  
Article
Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes
by Taiyang Yang, Pengxin Zhang, Daozhu Xu, Pengcheng Liu and Min Yang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 213; https://doi.org/10.3390/ijgi14060213 - 29 May 2025
Viewed by 316
Abstract
Recognizing functionally cohesive building groups is crucial for urban analysis, geospatial intelligence, and smart city applications. Traditional methods rely heavily on geometric information and often overlook the functional and semantic coherence of buildings, leading to their incorrect recognition. To overcome these challenges, this [...] Read more.
Recognizing functionally cohesive building groups is crucial for urban analysis, geospatial intelligence, and smart city applications. Traditional methods rely heavily on geometric information and often overlook the functional and semantic coherence of buildings, leading to their incorrect recognition. To overcome these challenges, this study introduces a flow-based community search approach, which models morphological, functional, and spatial relationships with a graph-based representation. The approach consists of graph representation learning, flow-based community generation, and community quality assessment, enabling adaptive building group recognition based on both structural coherence and functional similarity. Experimental results on commercial complex recognition demonstrate that our approach consistently outperforms traditional methods, achieving an improvement of over 5.4% in F1 score compared to the second-best method. Furthermore, its strong performance on limited training datasets highlights its robustness. These findings establish the proposed approach as an effective and reliable tool for recognizing functionally cohesive building groups, with practical viability in urban planning and policy formulation. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))
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25 pages, 24232 KiB  
Article
Topology-Aware Multi-View Street Scene Image Matching for Cross-Daylight Conditions Integrating Geometric Constraints and Semantic Consistency
by Haiqing He, Wenbo Xiong, Fuyang Zhou, Zile He, Tao Zhang and Zhiyuan Sheng
ISPRS Int. J. Geo-Inf. 2025, 14(6), 212; https://doi.org/10.3390/ijgi14060212 - 29 May 2025
Viewed by 399
Abstract
While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly in multi-source street-view scenarios. This paper presents a novel illumination-invariant framework that synergistically integrates geometric [...] Read more.
While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly in multi-source street-view scenarios. This paper presents a novel illumination-invariant framework that synergistically integrates geometric topology and semantic consistency to achieve robust multi-view matching for cross-daylight urban perception. We first design a self-supervised learning paradigm to extract illumination-agnostic features by jointly optimizing local descriptors and global geometric structures across multi-view images. To address extreme perspective variations, a homography-aware transformation module is introduced to stabilize feature representation under large viewpoint changes. Leveraging a graph neural network with hierarchical attention mechanisms, our method dynamically aggregates contextual information from both local keypoints and semantic topology graphs, enabling precise matching in occluded regions and repetitive-textured urban scenes. A dual-branch learning strategy further refines similarity metrics through supervised patch alignment and unsupervised spatial consistency constraints derived from Delaunay triangulation. Finally, a topology-guided multi-plane expansion mechanism propagates initial matches by exploiting the inherent structural regularity of street scenes, effectively suppressing mismatches while expanding coverage. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods, achieving a 6.4% improvement in matching accuracy and a 30.5% reduction in mismatches under cross-daylight conditions. These advancements establish a new benchmark for reliable multi-source image retrieval and localization in dynamic urban environments, with direct applications in autonomous driving systems and large-scale 3D city reconstruction. Full article
(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)
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32 pages, 20803 KiB  
Article
Synergistic Mechanisms Between Elderly Oriented Community Activity Space Morphology and Microclimate Performance: An Integrated Learning and Multi-Objective Optimization Approach
by Fang Wen, Lu Zhang, Ling Jiang, Rui Tang and Bo Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 211; https://doi.org/10.3390/ijgi14060211 - 28 May 2025
Viewed by 427
Abstract
This study collected site and spatial morphological data from 63 typical aging community activity spaces and extracted 12 spatial types through statistical analysis. A parametric modeling tool was used to generate spatial models. Based on clearly defined design variables and constraints, the NSGA-II [...] Read more.
This study collected site and spatial morphological data from 63 typical aging community activity spaces and extracted 12 spatial types through statistical analysis. A parametric modeling tool was used to generate spatial models. Based on clearly defined design variables and constraints, the NSGA-II multi-objective optimization algorithm was applied to minimize summer thermal discomfort, maximize winter thermal comfort, and maximize annual average sunlight duration, resulting in 342 Pareto optimal solutions. The study first explored the linear relationships between spatial morphology and environmental performance using the Spearman method. It then integrated ensemble learning and the interpretable machine learning model SHAP to reveal nonlinear relationships and boundary effects. The results of the two methods complemented and reinforced each other. Based on a comparison of these two approaches, morphological indicators showing significant differences were selected for attribution and sensitivity analyses, clarifying the mechanisms by which spatial morphological parameters influence environmental performance and identifying their critical thresholds. Key findings include the following: (1) the UTCI-S exhibits significant negative linear correlations with the open space ratio (OSR) and spatial crowding density (SCD); the UTCI-W shows negative linear correlations with canopy coverage (CVH) and wind speed (WS); and a positive linear correlation exists between the sky view factor (SVF) and AV.SH. (2) Boundary effects and threshold intervals of critical morphological parameters were identified as follows. The open space ratio should be controlled to 10–15%, the shrub–tree layer coverage to 0.013–0.0165%, and the average building height to 3.1–3.8 m. (3) Spatial layout principles demonstrate that placing fully enclosed spaces (E-2) and semi-enclosed spaces (S-1/S-3) on the northern side, as well as semi-enclosed spaces (S-1/S-2) and circulation spaces (C-3) on the southern side, significantly enhance microclimatic performance. These findings provide quantitative guidelines for community space design in cold regions and offer data support for creating outdoor environments that meet the comfort needs of the elderly. Full article
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25 pages, 5086 KiB  
Article
A Playful Participatory Planning System (P-PPS): A Framework for Collecting and Analyzing Player-Generated Spatial Data from Minecraft Worlds
by Ítalo Sousa de Sena, Lasith Niroshan, Jonáš Rosecký, Vojtěch Brůža, Micheál Butler and Chiara Cocco
ISPRS Int. J. Geo-Inf. 2025, 14(6), 210; https://doi.org/10.3390/ijgi14060210 - 24 May 2025
Viewed by 715
Abstract
Digital tools, especially games, are increasingly important for enabling citizen participation in urban planning. Among these, Minecraft has been widely utilized to engage children, leveraging its virtual environment to represent geospatial data. However, systematic methods for collecting and analyzing player-generated data within Minecraft [...] Read more.
Digital tools, especially games, are increasingly important for enabling citizen participation in urban planning. Among these, Minecraft has been widely utilized to engage children, leveraging its virtual environment to represent geospatial data. However, systematic methods for collecting and analyzing player-generated data within Minecraft remain underexplored. Playful Participatory Planning System (P-PPS) framework that transforms player actions (e.g., building, removing, planting) within Minecraft, using OpenStreetMap (OSM) data to create game environments, back into geospatial data for analysis. The framework’s applicability was demonstrated through two case studies, one with 58 schoolchildren and 18 adults in Ireland. The results reveal that schoolchildren, while highly engaged, demonstrated a high density of actions within limited areas, suggesting a need for guidance on spatial distribution and ecological considerations. In contrast, adults prioritized the urban context and exhibited greater spatial consistency in their actions. Challenges emerged in managing online interactions, emphasizing the need for clear guidelines and moderation strategies. This research demonstrates the potential of Minecraft as a platform for participatory urban planning, exploring its use as a collaborative immersive mapping tool. Full article
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32 pages, 11121 KiB  
Article
Construction of a Cold Island Spatial Pattern from the Perspective of Landscape Connectivity to Alleviate the Urban Heat Island Effect
by Qianli Ouyang, Bohong Zheng, Junyou Liu, Xi Luo, Shengyan Wu and Zhaoqian Sun
ISPRS Int. J. Geo-Inf. 2025, 14(6), 209; https://doi.org/10.3390/ijgi14060209 - 23 May 2025
Viewed by 648
Abstract
This study presents an innovative approach to mitigating the urban heat island (UHI) effect by constructing a cold island spatial pattern (CSP) from the perspective of landscape connectivity, integrating three-dimensional (3D) urban morphology and meteorological factors for the first time. Unlike traditional studies [...] Read more.
This study presents an innovative approach to mitigating the urban heat island (UHI) effect by constructing a cold island spatial pattern (CSP) from the perspective of landscape connectivity, integrating three-dimensional (3D) urban morphology and meteorological factors for the first time. Unlike traditional studies that focus on isolated patches or single-city scales, we propose a hierarchical framework for urban agglomerations, combining morphological spatial pattern analysis (MSPA), landscape connectivity assessment, and circuit theory to a construct CSP at the scale of urban agglomeration. By incorporating wind environment data and 3D building features (e.g., height, density) into the resistance surface, we enhance the accuracy of cooling network identification, revealing 39 cold island sources, 89 cooling corridors, and optimal corridor widths (600 m) in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA). Ultimately, a three-tiered heat island mitigation framework for urban agglomerations was established based on the CSP. This study offers an innovative perspective on urban climate adaptability planning within the context of contemporary urbanization. Our methodology and findings provide critical insights for future studies to integrate multiscale, multidimensional, and climate-adaptive approaches in urban thermal environment governance, fostering sustainable urbanization under escalating climate challenges. Full article
(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives, 2nd Edition)
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21 pages, 4062 KiB  
Article
Comprehensive Assessment and Obstacle Factor Recognition of Waterlogging Disaster Resilience in the Historic Urban Area
by Fangjie Cao, Qianxin Wang, Yun Qiu and Xinzhuo Wang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 208; https://doi.org/10.3390/ijgi14060208 - 23 May 2025
Viewed by 371
Abstract
As climate change intensifies, cities are experiencing more severe rainfall and frequent waterlogging. When rainfall exceeds the carrying capacity of urban drainage networks, it poses a significant risk to urban facilities and public safety, seriously affecting sustainable urban development. Compared with general urban [...] Read more.
As climate change intensifies, cities are experiencing more severe rainfall and frequent waterlogging. When rainfall exceeds the carrying capacity of urban drainage networks, it poses a significant risk to urban facilities and public safety, seriously affecting sustainable urban development. Compared with general urban built-up areas, they demonstrate greater vulnerability to rainfall-induced waterlogging due to their obsolete infrastructure and high heritage value, making it imperative to comprehensively enhance their waterlogging resilience. In this study, Qingdao’s historic urban area is selected as a sample case to analyze the interaction between rainfall intensity, the built environment, and population and business characteristics and the mechanism of waterlogging disaster in the historic urban area by combining with the concept of resilience; then construct a resilience assessment system for waterlogging in the historic urban area in terms of dangerousness, vulnerability, and adaptability; and carry out a measurement study. Specifically, the CA model is used as the basic model for simulating the possibility of waterlogging, and the waterlogging resilience index is quantified by combining the traditional research data and the emerging open-source geographic data. Furthermore, the waterlogging resilience and obstacle factors of the 293 evaluation units were quantitatively evaluated by varying the rainfall characteristics. The study shows that the low flooding resilience in the historic city is found in the densely built-up areas within the historic districts, which are difficult to penetrate, because of the high vulnerability of the buildings themselves, their adaptive capacity to meet the high intensity of tourism and commercial activities, and the relatively weak resilience of the built environment to disasters. Based on the measurement results, targeted spatial optimization strategies and planning adjustments are proposed. 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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