ISPRS International Journal of Geo-Information

23 pages, 14452 KiB

Open AccessArticle

Evaluation of Urban Accessibility Through Geomarketing Techniques: Case Study in Valencia (Spain)

by Néstor Villanueva-Durbán, Edgar Lorenzo-Sáez, Victoria Lerma-Arce and Eloina Coll-Aliaga

ISPRS Int. J. Geo-Inf. 2025, 14(2), 60; https://doi.org/10.3390/ijgi14020060 (registering DOI) - 3 Feb 2025

Today’s world is becoming increasingly urbanised, with populations concentrated in cities. This trend underscores the need to monitor urban growth and its potential adverse effects. The 2030 Agenda for Sustainable Development, the European Urban Agenda, various local agendas, and the “15-Minute City” concept [...] Read more.

Today’s world is becoming increasingly urbanised, with populations concentrated in cities. This trend underscores the need to monitor urban growth and its potential adverse effects. The 2030 Agenda for Sustainable Development, the European Urban Agenda, various local agendas, and the “15-Minute City” concept aim to mitigate these effects, particularly climate change-related ones. This paper explored the role of accessibility to public transport, services, and green urban areas (GUAs) in achieving the goals of SDG 11: Sustainable cities and communities and examined the feasibility of establishing 15-min cities by evaluating urban indicators. The methodology applied geomarketing techniques within geographic information systems (GISs) using high spatial resolution and influence buffers rather than conventional buffers for a more accurate assessment. These results offer a comprehensive and specific view of the city’s situation, based on the case study of Valencia (Spain), and provide urban planning tools for decision-makers with accessibility evaluated as a percentage at the block level. Full article

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23 pages, 11090 KiB

Open AccessArticle

Enhancing Spatial Awareness and Collaboration: A Guide to VR-Ready Survey Data Transformation

by Joseph Kevin McDuff, Armin Agha Karimi and Zahra Gharineiat

ISPRS Int. J. Geo-Inf. 2025, 14(2), 59; https://doi.org/10.3390/ijgi14020059 (registering DOI) - 2 Feb 2025

Abstract

Surveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces [...] Read more.

Surveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces while preserving the accuracy and quality crucial to professional surveying. The study leverages Blender, an open-source 3D creation tool, to develop a procedural guide for creating VR-ready models from high-quality survey data. The case study focuses on silos located in Yelarbon, Southeast Queensland, Australia. UAV mapping is utilised to gather the data necessary for 3D modelling with a few minor alterations in the photo capturing angle and processing. Key findings reveal that while Blender excels as a visualisation tool, it struggles with geospatial precision, particularly when handling large numbers coming from coordinate systems, leading to rounding errors seen within the VR model. Blender’s strength lies in creating immersive experiences for public engagement but is constrained by its lack of capability to hold survey metadata, hindering its applicability for professional survey-grade outputs. The results highlight the need for further development into possible Blender plugins that integrate geospatial accuracy with VR outputs. This study underscores the potential of VR to enhance how survey data are visualised, offering opportunities for future innovations in both the technical and creative aspects of the surveying profession. Full article

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

18 pages, 3401 KiB

Open AccessArticle

A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method

by Saeed Mehri, Navid Hooshangi and Navid Mahdizadeh Gharakhanlou

ISPRS Int. J. Geo-Inf. 2025, 14(2), 58; https://doi.org/10.3390/ijgi14020058 (registering DOI) - 1 Feb 2025

Abstract

Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environmental, and semantic contexts to capture all [...] Read more.

Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environmental, and semantic contexts to capture all contextual aspects of moving objects. Then, we propose the Context-Aware Douglas–Peucker (CADP) method for trajectory compression. These facts are confirmed by experiments with real AIS data showing that, while CADP preserves the same computational efficiency of DP (i.e., at O(n²)), it outperforms DP and two-stage Context-Aware Piecewise Linear Segmentation (two-stage CPLS) methods in preserving agent movement behavior, obtaining compressed trajectories that are closer to the original ones and that are much more useful in base analyses such as trajectory prediction. Specifically, the LSTM-based models trained on CADP-compressed trajectories have relatively lower RMSEs than others compressed by either DP or two-stage CPLS. Therefore, CADP is more scalable and efficient, thus making it more practical for large-scale engineering applications; with the improvement in trajectory analysis accuracy achieved by the suggested method, a wide range of critical engineering applications can be potentially improved, such as collision avoidance and route planning. Future work will focus on spatial auto-correlation and uncertainty to extend the robustness and applicability of the approach. Full article

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25 pages, 17627 KiB

Open AccessArticle

The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data

by Julieber T. Bersabe and Byong-Woon Jun

ISPRS Int. J. Geo-Inf. 2025, 14(2), 57; https://doi.org/10.3390/ijgi14020057 (registering DOI) - 1 Feb 2025

Abstract

In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors [...] Read more.

In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. 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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8 pages, 194 KiB

Open AccessEditorial

Advancing AI-Driven Geospatial Analysis and Data Generation: Methods, Applications and Future Directions

by Hartwig H. Hochmair, Levente Juhász and Hao Li

ISPRS Int. J. Geo-Inf. 2025, 14(2), 56; https://doi.org/10.3390/ijgi14020056 (registering DOI) - 1 Feb 2025

Abstract

Recent years have witnessed a revolution of artificial intelligence (AI) technologies, highlighted by the rise of generative AI and geospatial artificial intelligence (GeoAI) [...] Full article

23 pages, 2134 KiB

Open AccessArticle

Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM

by Di Cao, Xinran Yan, Jingjing Li, Jiayao Li and Lili Wu

ISPRS Int. J. Geo-Inf. 2025, 14(2), 55; https://doi.org/10.3390/ijgi14020055 - 30 Jan 2025

Abstract

Map symbols play a crucial role in cartographic representation. Among these symbols, icons are particularly valued for their vivid and intuitive designs, making them widely utilized in tourist maps. However, the diversity and complexity of these symbols present significant challenges to cartographic workflows. [...] Read more.

Map symbols play a crucial role in cartographic representation. Among these symbols, icons are particularly valued for their vivid and intuitive designs, making them widely utilized in tourist maps. However, the diversity and complexity of these symbols present significant challenges to cartographic workflows. Icon design often relies on manual drawing, which is not only time-consuming but also heavily dependent on specialized skills. Automating the extraction of symbols from existing maps could greatly enhance the map symbol database, offering a valuable resource to support both symbol design and map production. Nevertheless, the intricate shapes and dense distribution of symbols in tourist maps complicate the accurate and efficient detection and extraction using existing methods. Previous studies have shown that You Only Look Once (YOLO) series models demonstrate strong performance in object detection, offering high accuracy and speed. However, these models are less effective in fine-grained boundary segmentation. To address this limitation, this article proposes integrating YOLO models with the Segment Anything Model (SAM) to tackle the challenges of combining efficient detection with precise segmentation. This article developed a dataset consisting of both paper-based and digital tourist maps, with annotations for five main categories of symbols: human landscapes, natural sceneries, humans, animals, and cultural elements. The performance of various YOLO model variants was systematically evaluated using this dataset. Additionally, a user interaction mechanism was incorporated to review and refine detection results, which were subsequently used as prompts for the SAM to perform precise symbol segmentation. The results indicate that the YOLOv8x model achieved excellent performance on the tourist map dataset, with an average detection accuracy of 94.4% across the five symbol categories, fully meeting the requirements for symbol detection tasks. The inclusion of a user interaction mechanism enhanced the reliability and flexibility of detection outcomes, while the integration of the SAM significantly improved the precision of symbol boundary extraction. In conclusion, the integration of YOLOv8x and SAM provides a robust and effective solution for automating the extraction of map symbols. This approach not only reduces the manual workload involved in dataset annotation, but also offers valuable theoretical and practical insights for enhancing cartographic efficiency. Full article

9 pages, 1526 KiB

Open AccessArticle

Multi-Instance Zero-Watermarking Algorithm for Vector Geographic Data

by Qifei Zhou, Lin Yan, Zihao Wang, Na Ren and Changqing Zhu

ISPRS Int. J. Geo-Inf. 2025, 14(2), 54; https://doi.org/10.3390/ijgi14020054 - 30 Jan 2025

Abstract

To address the variability and complexity of attack types, this paper proposes a multi-instance zero-watermarking algorithm that goes beyond the conventional one-to-one watermarking approach. Inspired by the class-instance paradigm in object-oriented programming, this algorithm constructs multiple zero watermarks from a single vector geographic [...] Read more.

To address the variability and complexity of attack types, this paper proposes a multi-instance zero-watermarking algorithm that goes beyond the conventional one-to-one watermarking approach. Inspired by the class-instance paradigm in object-oriented programming, this algorithm constructs multiple zero watermarks from a single vector geographic dataset to enhance resilience against diverse attacks. Normalization is applied to eliminate dimensional and deformation inconsistencies, ensuring robustness against non-uniform scaling attacks. Feature triangle construction and angle selection are further utilized to provide resistance to interpolation and compression attacks. Moreover, angular features confer robustness against translation, uniform scaling, and rotation attacks. Experimental results demonstrate the superior robustness of the proposed algorithm, with normalized correlation values consistently maintaining 1.00 across various attack scenarios. Compared with existing methods, the algorithm exhibits superior comprehensive robustness, effectively safeguarding the copyright of vector geographic data. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

30 pages, 5698 KiB

Open AccessArticle

A Blockchain Copyright Protection Model Based on Vector Map Unique Identification

by Heyan Wang, Nannan Tang, Changqing Zhu, Na Ren and Changhong Wang

ISPRS Int. J. Geo-Inf. 2025, 14(2), 53; https://doi.org/10.3390/ijgi14020053 - 30 Jan 2025

Abstract

Combining blockchain technology with digital watermarking presents an efficient solution for safeguarding vector map files. However, the large data volume and stringent confidentiality requirements of vector maps pose significant challenges for direct registration on blockchain platforms. To overcome these limitations, this paper proposes [...] Read more.

Combining blockchain technology with digital watermarking presents an efficient solution for safeguarding vector map files. However, the large data volume and stringent confidentiality requirements of vector maps pose significant challenges for direct registration on blockchain platforms. To overcome these limitations, this paper proposes a blockchain-based copyright protection model utilizing unique identifiers (BCPM-UI). The model employs a distance ratio-based quantization watermarking algorithm to embed watermark information into vector maps and then generates unique identifiers based on their topological and geometric parameters. These identifiers, rather than the vector maps themselves, are securely registered on the blockchain. To ensure reliable copyright verification, a bit error rate (BER)-based matching algorithm is introduced, enabling accurate comparison between the unique identifiers of suspected infringing data and those stored on the blockchain. Experimental results validate the model’s effectiveness, demonstrating the high uniqueness and robustness of the identifiers generated. Additionally, the proposed approach reduces blockchain storage requirements for map data by a factor of 200, thereby meeting confidentiality standards while maintaining practical applicability in terms of copyright protection for vector maps. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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33 pages, 5497 KiB

Open AccessArticle

Use of Semantic Web Technologies to Enhance the Integration and Interoperability of Environmental Geospatial Data: A Framework Based on Ontology-Based Data Access

by Sajith Ranatunga, Rune Strand Ødegård, Knut Jetlund and Erling Onstein

ISPRS Int. J. Geo-Inf. 2025, 14(2), 52; https://doi.org/10.3390/ijgi14020052 - 28 Jan 2025

Abstract

Abstract: This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is [...] Read more.

Abstract: This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a common challenge within these domains due to data heterogeneity and semantic discrepancies. The proposed framework uses semantic web technologies to enhance data interoperability, accessibility, and usability. Several practical examples were demonstrated to validate its effectiveness. These examples were based in Lake Mjøsa, Norway, addressing both spatial and non-spatial scenarios to test the framework’s potential. By extending the GeoSPARQL ontology, the framework supports SPARQL queries to retrieve information based on user requirements. A web-based SPARQL Query Interface (SQI) was developed to execute queries and display the retrieved data in tabular and visual format. Utilizing free and open-source software (FOSS), the framework is easily replicable for stakeholders and researchers. Despite some limitations, the study concludes that the framework is able to enhance cross-domain data integration and semantic querying in various informed decision-making scenarios. Full article

27 pages, 2368 KiB

Open AccessArticle

Digital Transformation and Location Data Interoperability Skills for Small and Medium Enterprises

by Monica De Martino, Giacomo Martirano, Alfonso Quarati, Francesco Varni and Mayte Toscano Domínguez

ISPRS Int. J. Geo-Inf. 2025, 14(2), 51; https://doi.org/10.3390/ijgi14020051 - 28 Jan 2025

Abstract

In the dynamic landscape of digital transformation, data interoperability—particularly for location data—is a key enabler of operational efficiency, innovation, and collaboration for Small and Medium Enterprises (SMEs). Despite their strategic importance, SMEs face significant challenges in integrating and utilizing location data, which puts [...] Read more.

In the dynamic landscape of digital transformation, data interoperability—particularly for location data—is a key enabler of operational efficiency, innovation, and collaboration for Small and Medium Enterprises (SMEs). Despite their strategic importance, SMEs face significant challenges in integrating and utilizing location data, which puts them at a disadvantage in the increasingly digital global market. As part of the European DIS4SME project, this study proposes a methodology to address these challenges, characterized by the rigorous development of a training curriculum aimed at upskilling and retraining SME owners and employees. The curriculum emphasizes practical learning through real business case studies and is aligned with European policies such as the INSPIRE Directive and the European Data Strategy. Accordingly, ten courses were designed, forming a modular and hierarchical curriculum that addresses SMEs’ diverse needs. Initial feedback from the first managers’ pilot implementation suggests that the structured training program effectively equips managers with strategic decision-making skills to address location data interoperability challenges. Full article

23 pages, 2244 KiB

Open AccessArticle

Identify Optimal Pedestrian Flow Forecasting Methods in Great Britain Retail Areas: A Comparative Study of Time Series Forecasting on a Footfall Dataset

by Roberto Murcio and Yujue Wang

ISPRS Int. J. Geo-Inf. 2025, 14(2), 50; https://doi.org/10.3390/ijgi14020050 - 27 Jan 2025

Abstract

The UK retail landscape has undergone significant changes over the past decade, driven by factors such as the rise of online shopping, economic downturns, and, more recently, the COVID-19 pandemic. Accurately measuring pedestrian flows in retail areas with high spatial and temporal resolution [...] Read more.

The UK retail landscape has undergone significant changes over the past decade, driven by factors such as the rise of online shopping, economic downturns, and, more recently, the COVID-19 pandemic. Accurately measuring pedestrian flows in retail areas with high spatial and temporal resolution is essential for selecting the most appropriate forecasting model for different retail locations. However, several studies have adopted a one-size-fits-all approach, overlooking important local characteristics that are only occasionally captured by the best global model. In this work, using data generated by the SmartStreetSensor project, a large network of sensors installed across UK cities that collect Wi-Fi probe requests generated by mobile devices, we examine the optimal forecasting method to predict pedestrian footfall in various retail areas across Great Britain. After assessing six representative time series forecasting models, our results show that the LSTM model outperforms traditional methods in most areas. However, pedestrian counts at certain locations with specific spatial characteristics are better forecasted by other algorithms. Full article

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22 pages, 19013 KiB

Open AccessArticle

Exploring Inequality: A Multi-Scale Analysis of China’s Consumption Carbon Footprint

by Feng Xu, Xinqi Zheng, Minrui Zheng, Dongya Liu, Yin Ma, Jizong Peng, Ye Shen, Xu Han and Mengdi Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(2), 49; https://doi.org/10.3390/ijgi14020049 - 26 Jan 2025

Abstract

Carbon emission inequality has become a critical factor constraining the coordinated development of socio-economic systems and the natural environment. This inequality exacerbates the disparity in carbon emissions across regions, hindering efforts to achieve sustainable development and environmental justice. Previous research has primarily focused [...] Read more.

Carbon emission inequality has become a critical factor constraining the coordinated development of socio-economic systems and the natural environment. This inequality exacerbates the disparity in carbon emissions across regions, hindering efforts to achieve sustainable development and environmental justice. Previous research has primarily focused on the structure of carbon footprints and their influencing factors, but there has been limited quantitative research on carbon emission inequality, particularly from a multi-scale perspective. This study constructs a 250 m-high-resolution consumption-based carbon footprint grid for China and uses the Theil index to reveal significant spatial inequalities in carbon footprints. The results indicate that smaller-scale analyses better reveal the spatiotemporal heterogeneity of carbon footprints within regions. At the county level, carbon footprints exhibit significant inequalities, with hotspots concentrated in regions such as Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Pearl River Delta. The top 5% of areas with the highest carbon footprints (139 cities) contributed 19.6% of the national total, indicating a concentration in a few large cities. The decomposition of the Theil index shows that county-level cities contributed 55% of the national carbon inequality. The study also reveals the complex relationship between carbon footprints and income, as well as urban-rural disparities. The underdeveloped central and western regions exhibit a pronounced spatial lag effect, with the growth rate of carbon footprints in rural areas surpassing that of urban areas. Carbon footprints in impoverished areas and inter-provincial marginal areas overlap significantly with low-emission zones, demonstrating characteristics of “low-carbon growth”. To achieve carbon peak and carbon neutrality targets, China must adopt comprehensive measures to reduce carbon footprints and their inequalities, including strengthening multi-scale carbon inequality monitoring, implementing differentiated carbon reduction policies, and promoting coordinated emission reduction development at the county level. Full article

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19 pages, 7112 KiB

Open AccessArticle

The Coordinated Development Characteristics of Rural Industry and Employment: A Case Study of Chongqing, China

by Guoqin Ge, Yong Huang and Qianting Chen

ISPRS Int. J. Geo-Inf. 2025, 14(2), 48; https://doi.org/10.3390/ijgi14020048 - 26 Jan 2025

Abstract

Developing industries and promoting employment are essential for rural revitalization. This study establishes a theoretical framework to support the coordinated development of rural industry and employment (RIE) with Chongqing, China as the study area. Methods include GIS spatial analysis, the entropy-weighted TOPSIS method, [...] Read more.

Developing industries and promoting employment are essential for rural revitalization. This study establishes a theoretical framework to support the coordinated development of rural industry and employment (RIE) with Chongqing, China as the study area. Methods include GIS spatial analysis, the entropy-weighted TOPSIS method, a coupled coordination degree model, and an optimal-parameter-based GeoDetector. The analysis examines the spatio-temporal evolution and driving mechanisms of the coordinated development of RIE. The main findings are as follows. (1) During the study period, Chongqing’s RIE improved significantly overall, although rural industry is relatively lagging. (2) The evolution characteristics of the coordinated development of RIE exhibit “spatio-temporal ripple” and “spindle-shaped” patterns, and the spatial agglomeration has been enhanced. The growth of RIE is accompanied by the spatial diffusion of rural industry and the spatial echo of rural employment. (3) The primary driving mechanism for the coordinated development of RIE is “human-centered, natural resource-based socio-economic development.” Finally, this study discusses employment-centered strategies for rural industrial development, providing a theoretical foundation for policy-making in rural industrial development. Full article

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

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18 pages, 4425 KiB

Open AccessArticle

Enhancing Precision Beekeeping by the Macro-Level Environmental Analysis of Crowdsourced Spatial Data

by Daniels Kotovs, Agnese Krievina and Aleksejs Zacepins

ISPRS Int. J. Geo-Inf. 2025, 14(2), 47; https://doi.org/10.3390/ijgi14020047 - 25 Jan 2025

Abstract

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to [...] Read more.

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. Full article

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

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25 pages, 8908 KiB

Open AccessArticle

Cyber Potential Metaphorical Map Method Based on GMap

by Dongyu Si, Bingchuan Jiang, Qing Xia, Tingting Li, Xiao Wang and Jingxu Liu

ISPRS Int. J. Geo-Inf. 2025, 14(2), 46; https://doi.org/10.3390/ijgi14020046 - 25 Jan 2025

Abstract

Cyberspace maps facilitate the understanding of complex, abstract cyberspace. Due to the exponential growth of the Internet, the complexity of cyberspace has escalated dramatically. Traditional cyberspace maps are primarily for professionals and thus remain challenging for non-professionals to interpret. Ordinary users often find [...] Read more.

Cyberspace maps facilitate the understanding of complex, abstract cyberspace. Due to the exponential growth of the Internet, the complexity of cyberspace has escalated dramatically. Traditional cyberspace maps are primarily for professionals and thus remain challenging for non-professionals to interpret. Ordinary users often find themselves overwhelmed by the vast amount of information and the complexity of cyberspace. This renders traditional visualization tools inadequate for the general public, thereby highlighting the urgent need for more intuitive and accessible representations. This study uses the metaphor of cyberspace as a familiar geographical space to simplify the understanding of its internal relationships. Based on Autonomous System (AS) connectivity data, a “node-link” model is created to illustrate cyber interactions and dependencies, forming a foundation for analysis. The GMap algorithm visualizes AS connectivity data of countries, converting it into an intuitive map that clearly illustrates the cyber composition and dynamics. Cyber potential and national influence are considered to enhance map practicality and accuracy. A cyber-geography metaphor model integrates scientific and geographical elements, improving readability. The optimized GMap algorithm includes a holisticcyberspace strength index, showing both connectivity and relative country strength in cyberspace. This metaphorical approach aims to reduce information complexity, making cyberspace more comprehensible to the general public. Full article

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16 pages, 3803 KiB

Open AccessArticle

Correlation Characteristics Between Urban Fires and Urban Functional Spaces: A Study Based on Point of Interest Data and Ripley’s K-Function

by Yaobin Xiong and Gongquan Li

ISPRS Int. J. Geo-Inf. 2025, 14(2), 45; https://doi.org/10.3390/ijgi14020045 - 25 Jan 2025

Abstract

This paper investigates the dependency relationship and spatial patterns between urban fires and the distribution of urban functional spaces, using the Futian District in Shenzhen as a case study. This study utilizes univariate and bivariate Ripley’s K functions along with Point of Interest [...] Read more.

This paper investigates the dependency relationship and spatial patterns between urban fires and the distribution of urban functional spaces, using the Futian District in Shenzhen as a case study. This study utilizes univariate and bivariate Ripley’s K functions along with Point of Interest (POI) data to analyze the variation in the spatial clustering of urban fires across scales ranging from 0 to 2500 m. It explores the overall distribution trends and localized relationships between urban fires and five types of urban functional spaces: commercial, tourism, residential, public services, and transportation services. The results indicate that the clustering of urban fires increases at spatial scales of 0–1050 m and decreases at scales of 1050–2500 m. The overall distribution trend between urban fires and urban functional spaces demonstrates a bidirectional clustering pattern. The overall correlation shows that commercial service spaces have the strongest association with urban fire clustering, followed in order by residential services, public services, transportation services, and tourist service spaces. The clustering of urban fires in local areas is significantly associated with commercial and residential service spaces, and moderately related to public service and transportation service spaces, and shows no significant correlation with tourism service spaces. This research contributes to the understanding of urban fire risk through spatial analysis and offers insights for urban planning and fire safety management. Full article

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27 pages, 3300 KiB

Open AccessArticle

Spatial Dynamics and Drivers of Urban Growth in Thua Thien Hue Province, Vietnam: Insights for Urban Sustainability in the Global South

by Olabisi S. Obaitor, Oluwafemi Michael Odunsi, Thanh Bien Vu, Lena C. Grobusch, Michael Schultz, Volker Hochschild, Linh Nguyen Hoang Khanh and Matthias Garschagen

ISPRS Int. J. Geo-Inf. 2025, 14(2), 44; https://doi.org/10.3390/ijgi14020044 - 25 Jan 2025

Abstract

Investigating the historical patterns of urban growth and their drivers is crucial to informing sustainable urban planning policies, especially in cities of the Global South. In Vietnam, most studies focus primarily on city extents, offering little insight into urban growth across various provinces. [...] Read more.

Investigating the historical patterns of urban growth and their drivers is crucial to informing sustainable urban planning policies, especially in cities of the Global South. In Vietnam, most studies focus primarily on city extents, offering little insight into urban growth across various provinces. This study, therefore, combined categorical land use and land cover change detection, Random Forest classification and expert interviews to quantify the urban growth between 2000 and 2020, assess urban encroachment upon other land uses, and identify key drivers shaping this growth in Thua Thien Hue province. Findings show that the urban land areas were 27.94 km², 82.97 km², and 209.80 km² in 2000, 2010, and 2020, respectively. Urban encroachment upon other land use types, especially cropland, barren land, rice paddies, shrubs, and forests, was observed in these periods. Additionally, accessibility to built-up areas, DEM, proximity to rice paddies, slope, proximity to street roads, accessibility to social areas, and proximity to cropland are the major spatial drivers of urban growth in the province. The study concludes that rapid urban expansion is evident in the province at the expense of other land use types, especially agricultural land use types, which may impact food security and livelihoods in the province. Full article

15 pages, 5853 KiB

Open AccessArticle

Multi-View Three-Dimensional Reconstruction Based on Feature Enhancement and Weight Optimization Network

by Guobiao Yao, Ziheng Wang, Guozhong Wei, Fengqi Zhu, Qingqing Fu, Qian Yu and Min Wei

ISPRS Int. J. Geo-Inf. 2025, 14(2), 43; https://doi.org/10.3390/ijgi14020043 - 24 Jan 2025

Abstract

Aiming to address the issue that existing multi-view stereo reconstruction methods have insufficient adaptability to the repetitive and weak textures in multi-view images, this paper proposes a three-dimensional (3D) reconstruction algorithm based on Feature Enhancement and Weight Optimization MVSNet (Abbreviated as FEWO-MVSNet). To [...] Read more.

Aiming to address the issue that existing multi-view stereo reconstruction methods have insufficient adaptability to the repetitive and weak textures in multi-view images, this paper proposes a three-dimensional (3D) reconstruction algorithm based on Feature Enhancement and Weight Optimization MVSNet (Abbreviated as FEWO-MVSNet). To obtain accurate and detailed global and local features, we first develop an adaptive feature enhancement approach to obtain multi-scale information from the images. Second, we introduce an attention mechanism and a spatial feature capture module to enable high-sensitivity detection for weak texture features. Third, based on the 3D convolutional neural network, the fine depth map for multi-view images can be predicted and the complete 3D model is subsequently reconstructed. Last, we evaluated the proposed FEWO-MVSNet through training and testing on the DTU, BlendedMVS, and Tanks and Temples datasets. The results demonstrate significant superiorities of our method for 3D reconstruction from multi-view images, with our method ranking first in accuracy and second in completeness when compared to the existing representative methods. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)

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42 pages, 2221 KiB

Open AccessArticle

A Novel Evolutionary Deep Learning Approach for PM_2.5Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran

by Mehrdad Kaveh, Mohammad Saadi Mesgari and Masoud Kaveh

ISPRS Int. J. Geo-Inf. 2025, 14(2), 42; https://doi.org/10.3390/ijgi14020042 - 23 Jan 2025

Abstract

Forecasting particulate matter with a diameter of 2.5 μm (PM_2.5) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM_2.5 data, their limited number and geographic coverage [...] Read more.

Forecasting particulate matter with a diameter of 2.5 μm (PM_2.5) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM_2.5 data, their limited number and geographic coverage present significant challenges. Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM_2.5 levels, offering a broader spatial coverage and higher resolution. Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. To address these challenges, this study leverages geographic information systems (GIS), remote sensing (RS), and a hybrid LSTM architecture to predict PM_2.5 concentrations. Training LSTM models, however, is an NP-hard problem, with gradient-based methods facing limitations such as getting trapped in local minima, high computational costs, and the need for continuous objective functions. To overcome these issues, we propose integrating the novel orchard algorithm (OA) with LSTM to optimize air pollution forecasting. This paper utilizes meteorological data, topographical features, PM_2.5 pollution levels, and satellite imagery from the city of Tehran. Data preparation processes include noise reduction, spatial interpolation, and addressing missing data. The performance of the proposed OA-LSTM model is compared to five advanced machine learning (ML) algorithms. The proposed OA-LSTM model achieved the lowest root mean square error (RMSE) value of 3.01 µg/m³ and the highest coefficient of determination (R²) value of 0.88, underscoring its effectiveness compared to other models. This paper employs a binary OA method for sensitivity analysis, optimizing feature selection by minimizing prediction error while retaining critical predictors through a penalty-based objective function. The generated maps reveal higher PM_2.5 concentrations in autumn and winter compared to spring and summer, with northern and central areas showing the highest pollution levels. Full article

Journal Description

ISPRS International Journal of Geo-Information

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