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Volume 13
Issue 9
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ISPRS Int. J. Geo-Inf., Volume 13, Issue 9 (September 2024) – 42 articles

Cover Story (view full-size image): The concept of a Digital Twin (DT) is being applied in a growing number of application domains, each of which interprets what a DT is and what a DT does differently. Many of these application domains concern issues that vary spatially and temporally, such as DTs of cities, meaning DTs are increasingly dealing with geospatial concepts and data, whether directly stated or not. Efforts to characterize and define DTs to date do not consider this geospatial perspective, and as a result, there may be missed opportunities for collaboration between the geospatial and DT communities. This study systematically reviews real-world DT case studies in smart cities, manufacturing, energy, construction, and agriculture with the aim of progressing our understanding of the role of geospatial in DTs through defining a set of geospatial dimensions. View this paper
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17 pages, 15276 KiB  
Article
Urban–Rural Exposure to Flood Hazard and Social Vulnerability in the Conterminous United States
by Bishal Dhungana and Weibo Liu
ISPRS Int. J. Geo-Inf. 2024, 13(9), 339; https://doi.org/10.3390/ijgi13090339 - 22 Sep 2024
Cited by 1 | Viewed by 2080
Abstract
This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts in the Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing the American Community Survey (ACS) 2016–2020 data, we focused on 16 social factors representing socioeconomic status, household [...] Read more.
This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts in the Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing the American Community Survey (ACS) 2016–2020 data, we focused on 16 social factors representing socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation access. Principal Component Analysis (PCA) reduced these variables into five principal components: Socioeconomic Disadvantage, Elderly and Disability, Housing Density and Vehicle Access, Youth and Mobile Housing, and Group Quarters and Unemployment. An additive model created a comprehensive Social Vulnerability Index (SVI). Statistical analysis, including the Mann–Whitney U test, indicated significant differences in flood risk and social vulnerability between urban and rural areas. Spatial cluster analysis using Local Indicators of Spatial Association (LISA) revealed significant high flood risk and social vulnerability clusters, particularly in urban regions along the Gulf Coast, Atlantic Seaboard, and Mississippi River. Global and local regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), highlighted social vulnerability’s spatial variability and localized impacts on flood risk. The results showed substantial regional disparities, with urban areas exhibiting higher flood risks and social vulnerability, especially in southeastern urban centers. The analysis also revealed that Socioeconomic Disadvantage, Group Quarters and Unemployment, and Housing Density and Vehicle Access are closely related to flood risk in urban areas, while in rural areas, the relationship between flood risk and factors such as Elderly and Disability and Youth and Mobile Housing is more pronounced. This study underscores the necessity for targeted, region-specific strategies to mitigate flood risks and enhance resilience, particularly in areas where high flood risk and social vulnerability converge. These findings provide critical insights for policymakers and planners aiming to address environmental justice and promote equitable flood risk management across diverse geographic settings. 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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24 pages, 6069 KiB  
Article
Commutative Encryption and Reversible Watermarking Algorithm for Vector Maps Based on Virtual Coordinates
by Qianyi Dai, Baiyan Wu, Fanshuo Liu, Zixuan Bu and Haodong Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(9), 338; https://doi.org/10.3390/ijgi13090338 - 22 Sep 2024
Viewed by 594
Abstract
The combination of encryption and digital watermarking technologies is an increasingly popular approach to achieve full lifecycle data protection. Recently, reversible data hiding in the encrypted domain (RDHED) has greatly aroused the interest of many scholars. However, the fixed order of first encryption [...] Read more.
The combination of encryption and digital watermarking technologies is an increasingly popular approach to achieve full lifecycle data protection. Recently, reversible data hiding in the encrypted domain (RDHED) has greatly aroused the interest of many scholars. However, the fixed order of first encryption and then watermarking makes these algorithms unsuitable for many applications. Commutative encryption and watermarking (CEW) technology realizes the flexible combination of encryption and watermarking, and suits more applications. However, most existing CEW schemes for vector maps are not reversible and are unsuitable for high-precision maps. To solve this problem, here, we propose a commutative encryption and reversible watermarking (CERW) algorithm for vector maps based on virtual coordinates that are uniformly distributed on the number axis. The CERW algorithm consists of a virtual interval step-based encryption scheme and a coordinate difference-based reversible watermarking scheme. In the encryption scheme, the map coordinates are moved randomly by multiples of virtual interval steps defined as the distance between two adjacent virtual coordinates. In the reversible watermarking scheme, the difference expansion (DE) technique is used to embed the watermark bit into the coordinate difference, computed based on the relative position of a map coordinate in a virtual interval. As the relative position of a map coordinate in a virtual interval remains unchanged during the coordinate scrambling encryption process, the watermarking and encryption operations do not interfere with each other, and commutativity between encryption and watermarking is achieved. The results show that the proposed method has high security, high capacity, and good invisibility. In addition, the algorithm applies not only to polyline and polygon vector data, but also to sparsely distributed point data, which traditional DE watermarking algorithms often fail to watermark. Full article
(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)
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20 pages, 15342 KiB  
Article
Nonlinear Influence of the Built Environment on the Attraction of the Third Activity: A Comparative Analysis of Inflow from Home and Work
by Lin Luo, Xiping Yang, Xueye Chen, Jiayu Liu, Rui An and Jiyuan Li
ISPRS Int. J. Geo-Inf. 2024, 13(9), 337; https://doi.org/10.3390/ijgi13090337 - 22 Sep 2024
Viewed by 745
Abstract
Gaining an understanding of the intricate mechanisms between human activity and the built environment can help in promoting sustainable urban development. However, most scholars have focused on residents’ life and work behavior and have ignored the third activity (e.g., shopping, eating, and entertainment). [...] Read more.
Gaining an understanding of the intricate mechanisms between human activity and the built environment can help in promoting sustainable urban development. However, most scholars have focused on residents’ life and work behavior and have ignored the third activity (e.g., shopping, eating, and entertainment). In this study, a random forest algorithm and SHapley Additive exPlanation model were utilized to explore the nonlinear influence of the built environment on the attraction of the third activity (other than home and work). A comparative analysis of the inflow of the third activity from home and work was also carried out. The results show that the contributions of all built environment variables to the attraction of the third activity differ between home–other flow (HO) and work–other flow (WO) at the global scale, but their local effects are significantly similar. Furthermore, the nonlinear influence of the built environment on the attractions of the third activity can vary from one factor to another. A significant spatial heterogeneity can be observed on the built environment variables’ local effects on the attractions of the third activity. These findings can provide urban planners with insights that will help in the planning and optimization of communities for pursuing the third activity. Full article
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25 pages, 17970 KiB  
Article
A New Subject-Sensitive Hashing Algorithm Based on Multi-PatchDrop and Swin-Unet for the Integrity Authentication of HRRS Image
by Kaimeng Ding, Yingying Wang, Chishe Wang and Ji Ma
ISPRS Int. J. Geo-Inf. 2024, 13(9), 336; https://doi.org/10.3390/ijgi13090336 - 21 Sep 2024
Viewed by 599
Abstract
Transformer-based subject-sensitive hashing algorithms exhibit good integrity authentication performance and have the potential to ensure the authenticity and convenience of high-resolution remote sensing (HRRS) images. However, the robustness of Transformer-based subject-sensitive hashing is still not ideal. In this paper, we propose a Multi-PatchDrop [...] Read more.
Transformer-based subject-sensitive hashing algorithms exhibit good integrity authentication performance and have the potential to ensure the authenticity and convenience of high-resolution remote sensing (HRRS) images. However, the robustness of Transformer-based subject-sensitive hashing is still not ideal. In this paper, we propose a Multi-PatchDrop mechanism to improve the performance of Transformer-based subject-sensitive hashing. The Multi-PatchDrop mechanism determines different patch dropout values for different Transformer blocks in ViT models. On the basis of a Multi-PatchDrop, we propose an improved Swin-Unet for implementing subject-sensitive hashing. In this improved Swin-Unet, Multi-PatchDrop has been integrated, and each Swin Transformer block (except the first one) is preceded by a patch dropout layer. Experimental results demonstrate that the robustness of our proposed subject-sensitive hashing algorithm is not only stronger than that of the CNN-based algorithms but also stronger than that of Transformer-based algorithms. The tampering sensitivity is of the same intensity as the AGIM-net and M-net-based algorithms, stronger than other Transformer-based algorithms. Full article
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23 pages, 9431 KiB  
Article
Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model
by Zhen Lei, Shulei Zhou, Penggen Cheng and Yijie Xie
ISPRS Int. J. Geo-Inf. 2024, 13(9), 335; https://doi.org/10.3390/ijgi13090335 - 19 Sep 2024
Cited by 1 | Viewed by 772
Abstract
Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships [...] Read more.
Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps. Full article
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20 pages, 5271 KiB  
Article
Geographical Entity Management Model Based on Multi-Classification
by Lin Shi, Xiaoji Lan, Ming Xiao and Ning Liu
ISPRS Int. J. Geo-Inf. 2024, 13(9), 334; https://doi.org/10.3390/ijgi13090334 - 19 Sep 2024
Viewed by 591
Abstract
Scientific and logical classification is crucial for efficient information storage, management, and sharing. However, there are numerous existing classification systems for geographical entities, and the categories to which the same geographical entity belongs are often different in the business databases constructed according to [...] Read more.
Scientific and logical classification is crucial for efficient information storage, management, and sharing. However, there are numerous existing classification systems for geographical entities, and the categories to which the same geographical entity belongs are often different in the business databases constructed according to different classification systems, which brings great obstacles to the management and sharing of geographical information. This study analyzes the complexities of multiple classifications of geographical entities and proposes a multi-classification model for geographical entities based on directed hypergraph theory. This model integrates and transforms different classification systems for the same geographical entity, creating a unified method for expressing multiple classifications. We also designed a data structure to support this unified expression. By implementing this model, the study enables the effective management of geographical entity data, facilitating improved sharing and the exchange of geographical information across different industries and applications. In practical, the multi-classification model proposed in this paper allows geographical entities from different classification systems to be stored and managed within a single geographical database. Data views are then used to provide tailored services to various industry sectors and business applications. This approach effectively reduces data duplication and enhances the efficiency of managing and sharing geographical information. Using land use classification as an example, this study constructs a unified expression of three different land use classification systems based on the multi-classification model. An experiment managing land use data for a specific city was conducted using this model in PostgreSQL. The results indicate that the proposed method not only reduces data redundancy but also improves the query efficiency by over 10% on average compared to the mainstream relational database management mode. This confirms the effectiveness and practical value of the proposed method. Full article
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19 pages, 29485 KiB  
Article
Geometric Characterization of the Mateur Plain in Northern Tunisia Using Vertical Electrical Sounding and Remote Sensing Techniques
by Wissal Issaoui, Imen Hamdi Nasr, Dimitrios D. Alexakis, Wafa Bejaoui, Ismael M. Ibraheem, Ahmed Ezzine, Dhouha Ben Othman and Mohamed Hédi Inoubli
ISPRS Int. J. Geo-Inf. 2024, 13(9), 333; https://doi.org/10.3390/ijgi13090333 - 18 Sep 2024
Cited by 1 | Viewed by 762
Abstract
The Mateur aquifer system in Northern Tunisia was examined using data from 19 water boreholes, 69 vertical electrical sounding (VES) stations, and a Sentinel-2 satellite image. Available boreholes and their corresponding logs were compared to define precisely the multi-layer aquifer system, including the [...] Read more.
The Mateur aquifer system in Northern Tunisia was examined using data from 19 water boreholes, 69 vertical electrical sounding (VES) stations, and a Sentinel-2 satellite image. Available boreholes and their corresponding logs were compared to define precisely the multi-layer aquifer system, including the Quaternary and Campanian aquifers of the Mateur plain. Quantitative interpretation and qualitative evaluation of VES data were conducted to define the geometry of these reservoirs. These interpretations were enhanced by remote sensing imagery processing, which enabled the identification of the Mateur plain’s superficial lineaments. Based on well log information, the lithological columns show that the Quaternary series in the Ras El Ain region contains a layer of clayey, pebbly, and gravelly limestone. Additionally, in the Oued El Tine area, a clayey lithological unit has been identified as a multi-layer aquifer. The study area, exhibiting apparent resistivity values ranging between 20 and 170 Ohm·m, appears to be rich in groundwater resources. The correlation between the lithological columns and the interpreted VES data, presented as geoelectrical cross-sections, revealed variations in depth (8–106 m), thickness (10 to 55 m), and resistivity (20–98 Ohm·m) of a coarse unit corresponding to the Mateur aquifer. Twenty-three superficial lineaments were extracted from the Sentinel-2 image. Their common superposition indicated that both of them are in a good coincidence; these could be the result of normal faults, creating an aquifer system divided into raised and sunken blocks. 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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44 pages, 12171 KiB  
Systematic Review
Potentials in Using VR for Facilitating Geography Teaching in Classrooms: A Systematic Review
by Klára Czimre, Károly Teperics, Ernő Molnár, János Kapusi, Ikram Saidi, Deddy Gusman and Gyöngyi Bujdosó
ISPRS Int. J. Geo-Inf. 2024, 13(9), 332; https://doi.org/10.3390/ijgi13090332 - 17 Sep 2024
Viewed by 1513
Abstract
The application of virtual reality (VR) in geography education is regarded as a progressive and proactive method that has still not gained sufficient attention in the educational policy in Hungary. The aim of our review is to find the ways and means to [...] Read more.
The application of virtual reality (VR) in geography education is regarded as a progressive and proactive method that has still not gained sufficient attention in the educational policy in Hungary. The aim of our review is to find the ways and means to make it happen. We selected 47 works that are closely linked to geography teaching and analyzed their bibliometric (authorship and journal characteristics, types of works and applied methods, keywords, referencing, and co-citation networks) and contextual characteristics (research objectives, demographic, gender and social background, hardware and software specifications, advantages and disadvantages, conclusions, and predictions) which we expected to help us to understand the slow implementation and undeserved marginalization of VR in the curricular geography education. We used a mixed-method research analysis combining elements of quantitative and qualitative analysis using inductive reasoning. Our preliminary assumption that the application of VR technology is an effective and useful way of teaching geography was proved by our findings. The methods used by the authors of the reviewed empirical works, together with the recommended future research topics and strategies, can be applied to future empirical research on the use of VR in geography education. Full article
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20 pages, 16373 KiB  
Article
Urban Internal Network Structure and Resilience Characteristics from the Perspective of Population Mobility: A Case Study of Nanjing, China
by Zherui Li, Wen Chen, Wei Liu and Zhe Cui
ISPRS Int. J. Geo-Inf. 2024, 13(9), 331; https://doi.org/10.3390/ijgi13090331 - 17 Sep 2024
Viewed by 778
Abstract
In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and [...] Read more.
In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and streets within Nanjing City. Based on the characteristics of these network structures, the resilience of the network structure is measured from the perspectives of density, symmetry, and transmissibility through interruption simulation techniques. The results show that the intensity of population mobility within Nanjing presents a general decay from the central urban area to the outer layers. In the employment scenario, cross-river population mobility is more frequent, while in the recreational scenario, the natural barrier effect of the Yangtze River is prominent. Due to the concentration of employment centers and high spatial heterogeneity, the employment flow network exhibits greater vulnerability to sudden shocks. Townships and streets with weighted degree values ranking around 60 and 80 are of great importance for maintaining the normal operation of both employment and recreational flow networks. Strengthening the construction of resilient parks and village planning within resilient cities can enhance the risk resistance of employment and recreational flow networks. Full article
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15 pages, 1393 KiB  
Article
Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan
by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid and Amira Khattak
ISPRS Int. J. Geo-Inf. 2024, 13(9), 330; https://doi.org/10.3390/ijgi13090330 - 16 Sep 2024
Cited by 1 | Viewed by 1122
Abstract
While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the [...] Read more.
While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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18 pages, 11676 KiB  
Article
Mapping Localization Preferences for Residential Buildings
by Jacek Jabłoński, Łukasz Wielebski and Beata Medyńska-Gulij
ISPRS Int. J. Geo-Inf. 2024, 13(9), 329; https://doi.org/10.3390/ijgi13090329 - 15 Sep 2024
Viewed by 621
Abstract
In this study, we tried to gauge the trends of localization preferences for residential buildings among young adults. The pragmatic dimension of these studies is important in the process of real estate investment, where a location can be expressed using indicators and statistical [...] Read more.
In this study, we tried to gauge the trends of localization preferences for residential buildings among young adults. The pragmatic dimension of these studies is important in the process of real estate investment, where a location can be expressed using indicators and statistical data and then, using maps, indicate preferred areas for living in a small town. The aim of our research was to examine and visualize the preferences of young people for living locations in relation to access to services. We conducted an online survey using a Likert scale to determine which services and amenities are most important for young residents. Using multi-criteria evaluation (MCE) methods and their formulas, we calculated the attractiveness coefficient of the location of residential buildings, which we propose to call the RBLAF (Residential Building’s Localization Attractiveness Factor). The results of this research are maps: qualitative–quantitative with point symbols for the structure of services and quantitative isochromatics showing the preferences of potential future investors in real estate. Full article
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20 pages, 2989 KiB  
Article
A Review of Pakistan’s National Spatial Data Infrastructure Using Multiple Assessment Frameworks
by Munir Ahmad, Asmat Ali, Muhammad Nawaz, Farha Sattar and Hammad Hussain
ISPRS Int. J. Geo-Inf. 2024, 13(9), 328; https://doi.org/10.3390/ijgi13090328 - 14 Sep 2024
Viewed by 843
Abstract
Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through [...] Read more.
Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article
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24 pages, 3254 KiB  
Article
Construction and Inference Method of Semantic-Driven, Spatio-Temporal Derivation Relationship Network for Place Names
by Wenjie Dong, Xi Mao, Wenjuan Lu, Jizhou Wang and Yao Cheng
ISPRS Int. J. Geo-Inf. 2024, 13(9), 327; https://doi.org/10.3390/ijgi13090327 - 13 Sep 2024
Viewed by 567
Abstract
As the proper noun for geographical entities, place names provide an intuitive way to identify and access specific geographic locations, playing a key role in semantic expression and spatial retrieval. However, existing research has insufficiently explored the spatio-temporal derivation relationships of place names, [...] Read more.
As the proper noun for geographical entities, place names provide an intuitive way to identify and access specific geographic locations, playing a key role in semantic expression and spatial retrieval. However, existing research has insufficiently explored the spatio-temporal derivation relationships of place names, failing to fully utilize these relationships to enhance the connectivity between place names and improve spatial retrieval capabilities. Therefore, this paper conducts research on the spatio-temporal derivation relationships of place names, defines them in a standardized manner, clarifies the boundary conditions and identification methods, and then constructs a spatio-temporal derivation network of place names for expression and uses this network to carry out reasoning research on spatial adjacency relationships. Experiments and results showed that using the theory and methods of this paper to identify the spatio-temporal derivation relationships of Canadian place names achieves an accuracy rate of 98.5% and a recall rate of 93.4%, and the reasoning results can effectively improve the accuracy of query results. The research enriches the theoretical framework of spatio-temporal derivation relationships of place names, solves the current problems of unclear definition and inability to automatically identify spatio-temporal derivation relationships, and provides new perspectives and tools for the application practice in the field of geographical information science. Full article
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24 pages, 210044 KiB  
Article
Scale- and Resolution-Adapted Shaded Relief Generation Using U-Net
by Marianna Farmakis-Serebryakova, Magnus Heitzler and Lorenz Hurni
ISPRS Int. J. Geo-Inf. 2024, 13(9), 326; https://doi.org/10.3390/ijgi13090326 - 12 Sep 2024
Viewed by 1119
Abstract
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) [...] Read more.
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) relates to the neural network process and the maps used for training. Currently, there is no clear guidance on which DEM resolution to use to generate relief shading at specific scales. To address this gap, we trained the U-Net models on swisstopo manual relief shadings of Switzerland at four different scales and using four different resolutions of SwissALTI3D DEM. An interactive web application designed for this study allows users to outline a random area and compare histograms of varying brightness between predictions and manual relief shadings. The results showed that DEM resolution and output scale influence the appearance of the relief shading, with an overall scale/resolution ratio. We present guidelines for generating relief shading with neural networks for arbitrary areas and scales. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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30 pages, 2615 KiB  
Article
Evaluation of the Monitoring Capabilities of Remote Sensing Satellites for Maritime Moving Targets
by Weiming Li, Zhiqiang Du, Li Wang and Tiancheng Zhou
ISPRS Int. J. Geo-Inf. 2024, 13(9), 325; https://doi.org/10.3390/ijgi13090325 - 11 Sep 2024
Viewed by 776
Abstract
Although an Automatic Identification System (AIS) can be used to monitor trajectories, it has become a reality for remote sensing satellite clusters to monitor maritime moving targets. The increasing demand for monitoring poses challenges for the construction of satellites, the monitoring capabilities of [...] Read more.
Although an Automatic Identification System (AIS) can be used to monitor trajectories, it has become a reality for remote sensing satellite clusters to monitor maritime moving targets. The increasing demand for monitoring poses challenges for the construction of satellites, the monitoring capabilities of which urgently need to be evaluated. Conventional evaluation methods focus on the spatial characteristics of monitoring; however, the temporal characteristics and the target’s kinematic characteristics are neglected. In this study, an evaluation method that integrates the spatial and temporal characteristics of monitoring along with the target’s kinematic characteristics is proposed. Firstly, a target motion prediction model for calculating the transfer probability and a satellite observation information calculation model for obtaining observation strips and time windows are established. Secondly, an index system is established, including the target detection capability, observation coverage capability, proportion of empty window, dispersion of observation window, and deviation of observation window. Thirdly, a comprehensive evaluation is completed through combining the analytic hierarchy process and entropy weight method to obtain the monitoring capability score. Finally, simulation experiments are conducted to evaluate the monitoring capabilities of satellites for ship trajectories. The results show that the method is effective when the grid size is between 1.6 and 1.8 times the target size and the task duration is approximately twice the time interval between trajectory points. Furthermore, the method is proven to be usable in various environments. Full article
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21 pages, 18678 KiB  
Article
Determinants of Intra-City Residential Migration Patterns of Older Adults: A GIS and Decision Tree Analysis of Yancheng City, China
by Zhulin Hou, Xiangfeng Li and Xiaoming Li
ISPRS Int. J. Geo-Inf. 2024, 13(9), 324; https://doi.org/10.3390/ijgi13090324 - 7 Sep 2024
Viewed by 888
Abstract
This study investigates the spatial patterns of residential migration among older adults in the city center of Yancheng and the influencing factors using data on the home purchases of individuals aged 65 and older from 2016 to 2018, along with peripheral point of [...] Read more.
This study investigates the spatial patterns of residential migration among older adults in the city center of Yancheng and the influencing factors using data on the home purchases of individuals aged 65 and older from 2016 to 2018, along with peripheral point of interest (POI) data, analyzed with ArcGIS and a decision tree model. The results indicated that persons aged 60–65 accounted for 42.8% of the total sample and primarily chose to migrate in the early stages of retirement. The intra-city migration of older adults exhibits both centripetal and centrifugal patterns, with a greater tendency toward the city center. House prices, floor levels, and commercial facilities significantly impact their choice of migration destinations. Among these, house prices were the most critical determinant, with the majority of older adults migrating to neighborhoods with lower house prices. This study contributes by integrating residential migration and location choice research and constructing an analytical framework based on facility accessibility. The findings provide insights into the key determinants of location choice for intra-city residential migration among older adults and the construction of livable neighborhoods for them. Full article
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27 pages, 9443 KiB  
Article
Mapping Geospatial AI Flood Risk in National Road Networks
by Seyed M. H. S. Rezvani, Maria João Falcão Silva and Nuno Marques de Almeida
ISPRS Int. J. Geo-Inf. 2024, 13(9), 323; https://doi.org/10.3390/ijgi13090323 - 7 Sep 2024
Cited by 1 | Viewed by 2118
Abstract
Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within [...] Read more.
Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap’s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers. Full article
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24 pages, 7132 KiB  
Article
Identification and Analysis of Ecological Corridors in the Central Urban Area of Xuchang Based on Multi-Source Geospatial Data
by Wenyu Wei, Shaohua Wang, Xiao Li, Junyuan Zhou, Yang Zhong, Pengze Li and Zhidong Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(9), 322; https://doi.org/10.3390/ijgi13090322 - 6 Sep 2024
Viewed by 1033
Abstract
With the development of ecological civilization construction, urban planning and development in China have entered a phase in which optimizing and constructing ecological spaces is required. As a national livable city, Xuchang has experienced rapid economic development in recent years, leading to significant [...] Read more.
With the development of ecological civilization construction, urban planning and development in China have entered a phase in which optimizing and constructing ecological spaces is required. As a national livable city, Xuchang has experienced rapid economic development in recent years, leading to significant urban expansion that has impacted the layout of ecological space networks in the central urban area and its surroundings. Therefore, identifying and optimizing the spatial layout of ecological corridors in Xuchang City are crucial for ecological development and park city construction. This study utilizes multisource geospatial data to identify and extract ecological corridors in the central urban area of Xuchang City. Ecological resistance and gravity models are employed to identify and verify that the primary ecological corridor pattern in Xuchang City is situated in Weidu District, which is a central urban area. Finally, 11 main ecological corridors in the central urban area are delineated. In response to the identification of ecological corridors, this study integrates spatial analysis methods and text analysis methods to evaluate the characteristics of urban ecological corridors. The results indicate that Xudu Park extends outward, serving as the hub of the ecological network, and that West Lake Park and Luming Lake Park form the core of the urban park system. Finally, based on the spatial relationships, ecological benefits, and citizen experience of each ecological corridor and the green parks it traverses, strategies for optimizing the layout of urban ecological corridors are proposed. Full article
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20 pages, 873 KiB  
Article
An Efficient and Expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption Scheme for Satellite Service Systems
by Jiaoli Shi, Chao Hu, Shunli Zhang, Qing Zhou, Zhuolin Mei, Shimao Yao and Anyuan Deng
ISPRS Int. J. Geo-Inf. 2024, 13(9), 321; https://doi.org/10.3390/ijgi13090321 - 5 Sep 2024
Viewed by 605
Abstract
Satellite service systems transfer data from satellite providers to the big data industry, which includes data traders and data analytics companies. This system needs to provide access to numerous users whose specific identities are unknown. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows unidentified users with [...] Read more.
Satellite service systems transfer data from satellite providers to the big data industry, which includes data traders and data analytics companies. This system needs to provide access to numerous users whose specific identities are unknown. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows unidentified users with the proper attributes to decrypt data, providing fine-grained access control of data. However, traditional CP-ABE does not protect access policies. Access policies are uploaded to the cloud, stored, and downloaded in plain text, making them vulnerable to privacy breaches. When the access policy is completely hidden, users need to use their own attributes to try matching one by one, which is an inefficient process. In order to efficiently hide the access policy fully, this paper introduces a new efficient and expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption scheme (CP-ABE-FPH), which integrates the 2-way handshake O-PSI method with the ROBDD method. The integration offers advantages: (1) High efficiency and high expressiveness. The access policy using ROBDD is highly expressive but computationally intensive due to its recursive nature. This shortcoming is overcome in CP-ABE-FPH using the proposed O-PSI method, and the access policy is matched quickly and secretly. (2) High flexibility. The decryption process does not require the owner or the Key Generation Center (KGC) to be online, and system attributes can be added at any time. Security analysis shows that the access policy is fully hidden. Efficiency analysis and simulation results show that the proposed scheme is highly efficient in decryption compared with existing schemes. Full article
(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)
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29 pages, 1971 KiB  
Article
Characterizing the Role of Geospatial Science in Digital Twins
by Jack Metcalfe, Claire Ellul, Jeremy Morley and Jantien Stoter
ISPRS Int. J. Geo-Inf. 2024, 13(9), 320; https://doi.org/10.3390/ijgi13090320 - 5 Sep 2024
Cited by 1 | Viewed by 1377
Abstract
Delivering value from digital concepts such as Digital Twins is necessary to address systemic national and global issues, such as achieving Net Zero. However, there is still a lack of consensus over what a Digital Twin (DT) is and efforts to clarify this [...] Read more.
Delivering value from digital concepts such as Digital Twins is necessary to address systemic national and global issues, such as achieving Net Zero. However, there is still a lack of consensus over what a Digital Twin (DT) is and efforts to clarify this do not consider the Geospatial perspective. With the aspiration for national- and international-scale DTs, it is important that the Geospatial community understands its role in supporting the realisation of the value of these DTs. Here, a systematic literature review is used to gather DT case studies that use, or are inferred to use, elements of the Geospatial discipline. A total of 77 DT case studies about smart cities, manufacturing, energy, construction and agriculture are reviewed in full, and 24 Geospatial DT dimensions are defined and then compared with existing DT dimensions. The results indicate a considerable use of Geospatial Science in DTs that is not explicitly stated, meaning that there are possibly missed opportunities for collaboration between the Geospatial and DT communities. We conclude that the role of Geospatial Science in DTs is larger than stated and needs to be understood further. Full article
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28 pages, 37910 KiB  
Article
Cultural Heritage in Times of Crisis: Damage Assessment in Urban Areas of Ukraine Using Sentinel-1 SAR Data
by Ute Bachmann-Gigl and Zahra Dabiri
ISPRS Int. J. Geo-Inf. 2024, 13(9), 319; https://doi.org/10.3390/ijgi13090319 - 5 Sep 2024
Viewed by 897
Abstract
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the [...] Read more.
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the application of Earth observation (EO) synthetic aperture radar (SAR) technology, and in particular Sentinel-1 SAR coherence time-series analysis, to monitor spatial and temporal changes related to the recent Russian–Ukrainian war in the urban areas of Mariupol and Kharkiv, Ukraine. The study considers key events during the siege of Mariupol and the battle of Kharkiv from February to May 2022. Built-up areas and cultural property were identified using freely available OpenStreetMap (OSM) data. Semi-automated coherent change-detection technique (CCD) that utilize difference analysis of pre- and co-conflict coherences were capable of highlighting areas of major impact on the urban structures. The study applied a logistic regression model (LRM) for the discrimination of damaged and undamaged buildings based on an estimated likelihood of damage occurrence. A good agreement was observed with the reference data provided by the United Nations Satellite Centre (UNOSAT) in terms of the overall extent of damage. Damage maps enable the localization of buildings and cultural assets in areas with a high probability of damage and can serve as the basis for a high-resolution follow-up investigation. The study reveals the benefits of Sentinel-1 SAR CCD in the sense of unsupervised delineation of areas affected by armed conflict. However, limitations arise in the detection of local and single-building damage compared to regions with large-scale destruction. The proposed semi-automated multi-temporal Sentinel-1 data analysis using CCD methodology shows its applicability for the timely investigation of damage to buildings and cultural heritage, which can support the response to crises. Full article
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26 pages, 8508 KiB  
Article
Post-Occupancy Evaluation of the Improved Old Residential Neighborhood Satisfaction Using Principal Component Analysis: The Case of Wuxi, China
by Jing Zhao, Faziawati Abdul Aziz, Ziyi Cheng, Norsidah Ujang, Hui Zhang, Jiajun Xu, Yi Xiao and Lin Shi
ISPRS Int. J. Geo-Inf. 2024, 13(9), 318; https://doi.org/10.3390/ijgi13090318 - 4 Sep 2024
Viewed by 1174
Abstract
Recently, many Chinese cities have initiated improvement projects aimed at enhancing living conditions in older residential neighborhoods. Urban improvement should be closely linked to the needs of occupants to determine “what to improve”. Governmental initiatives and the various stakeholders involved in the project [...] Read more.
Recently, many Chinese cities have initiated improvement projects aimed at enhancing living conditions in older residential neighborhoods. Urban improvement should be closely linked to the needs of occupants to determine “what to improve”. Governmental initiatives and the various stakeholders involved in the project influence the impact of improvement efforts. The objectives of the study are essential to identify the factors influencing occupants’ satisfaction and to evaluate whether the occupants are satisfied with the improved old residential neighborhoods. This study conducts a post-occupancy evaluation (POE) of improved outdoor spaces in old residential neighborhoods, focusing on neighborhoods in Wuxi, China. A principal component analysis (PCA) was used to evaluate residents’ efficacy and satisfaction with the enhancements implemented in outdoor spaces. The methodology involved collecting data through surveys and on-site observations, which were then analyzed to identify the pivotal factors impacting the effectiveness of these improvements. The results indicated that enhancing outdoor spaces had a substantial positive impact on residents’ quality of life, social interactions, and physical activity levels. Additionally, the PCA identified accessibility, safety, and aesthetic quality as the main factors contributing to resident satisfaction. This study offers valuable insights for urban planners and policymakers aiming to rejuvenate aging residential districts, emphasizing the importance of data-driven approaches to improve the design and functionality of outdoor spaces. Full article
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22 pages, 8611 KiB  
Article
GIS-Based Analytical Hierarchy Process for Identifying Groundwater Potential Zones in Punjab, Pakistan
by Maira Naeem, Hafiz Umar Farid, Muhammad Arbaz Madni, Raffaele Albano, Muhammad Azhar Inam, Muhammad Shoaib, Muhammad Shoaib, Tehmena Rashid, Aqsa Dilshad and Akhlaq Ahmad
ISPRS Int. J. Geo-Inf. 2024, 13(9), 317; https://doi.org/10.3390/ijgi13090317 - 3 Sep 2024
Cited by 1 | Viewed by 1129
Abstract
The quality and level of groundwater tables have rapidly declined because of intensive pumping in Punjab (Pakistan). For sustainable groundwater supplies, there is a need for better management practices. So, the identification of potential groundwater recharge zones is crucial for developing effective management [...] Read more.
The quality and level of groundwater tables have rapidly declined because of intensive pumping in Punjab (Pakistan). For sustainable groundwater supplies, there is a need for better management practices. So, the identification of potential groundwater recharge zones is crucial for developing effective management systems. The current research is based on integrating seven contributing factors, including geology, soil map, land cover/land use, lineament density, drainage density, slope, and rainfall to categorize the area into various groundwater recharge potential zones using remote sensing, geographic information system (GIS), and analytical hierarchical process (AHP) for Punjab, Pakistan. The weights (for various thematic layers) and rating values (for sub-classes) in the overlay analysis were assigned for thematic layers and then modified and normalized using the AHP. The result indicates that about 17.88% of the area falls under the category of very high groundwater potential zones (GWPZs). It was found that only 12.27% of the area falls under the category of very low GWPZs. The results showed that spatial technologies like remote sensing and geographic information system (GIS), when combined with AHP technique, provide a robust platform for studying GWPZs. This will help the public and government sectors to understand the potential zone for sustainable groundwater management. Full article
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20 pages, 3686 KiB  
Article
Association between Built Environment and Bus Usage among Older Adults: Urban–Rural Differences in the Nonlinearities
by Bozhezi Peng, Lanjing Wang, Jiani Wu, Chaoyang Li, Tao Wang, Shengqiang Yuan and Yi Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(9), 316; https://doi.org/10.3390/ijgi13090316 - 2 Sep 2024
Cited by 2 | Viewed by 794
Abstract
Public transport improves mobility and well-being for the rapidly aging population. However, few planning interventions have addressed the urban–rural disparity in bus usage among older adults. Using data from Zhongshan, China, this study adopts the eXtreme Gradient Boosting (XGBoost) model to examine urban–rural [...] Read more.
Public transport improves mobility and well-being for the rapidly aging population. However, few planning interventions have addressed the urban–rural disparity in bus usage among older adults. Using data from Zhongshan, China, this study adopts the eXtreme Gradient Boosting (XGBoost) model to examine urban–rural differences in the nonlinear relationship between built environment and daily bus usage among elderly adults. The results indicate nonlinearities across all built environment variables and stronger effects of the built environment in rural areas. Distance to transit contributes the most in urban neighborhoods but least in rural ones. Furthermore, dwelling unit density and green space accessibility play the biggest roles in the rural context. Additionally, the most effective ranges of intersection density, land use mixture, and CBD accessibility are greater in rural areas. The findings facilitate fine-grained and diversified planning interventions to facilitate bus usage among older adults in both urban and rural areas. Full article
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21 pages, 9545 KiB  
Article
Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions
by Uroš Durlević, Aleksandar Valjarević, Ivan Novković, Filip Vujović, Nemanja Josifov, Jelka Krušić, Blaž Komac, Tatjana Djekić, Sudhir Kumar Singh, Goran Jović, Milan Radojković and Marko Ivanović
ISPRS Int. J. Geo-Inf. 2024, 13(9), 315; https://doi.org/10.3390/ijgi13090315 - 1 Sep 2024
Viewed by 1169
Abstract
Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the [...] Read more.
Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures. 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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19 pages, 9783 KiB  
Article
Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal Characteristics
by Guanyao Li, Ruyu Xu, Tingyan Shi, Xingdong Deng, Yang Liu, Deshi Di, Chuanbao Zhao and Guochao Liu
ISPRS Int. J. Geo-Inf. 2024, 13(9), 314; https://doi.org/10.3390/ijgi13090314 - 30 Aug 2024
Viewed by 1004
Abstract
A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart [...] Read more.
A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart city applications, such as effective urban planning and public transportation system optimization. In this work, we study the problem of detecting fine-grained metro trips from cellular trajectory data. Existing trip-detection approaches designed for GPS trajectories are often not applicable to cellular data due to the issues of location noise and irregular data sampling in cellular data. Moreover, most cellular data-based methods focus on identifying coarse-grained transportation modes, failing to detect fine-grained metro trips accurately. To address the limitations of existing works, we propose a novel and efficient fine-grained metro-trip detection (FGMTD) model in this work. By considering both the local and global spatial–temporal characteristics of a trajectory and the metro network, FGMTD can effectively mitigate the effects of location noise and irregular data sampling, ultimately improving the accuracy and reliability of the detection process. In particular, FGMTD employs a spatial–temporal hidden Markov model with efficient index strategies to capture local spatial–temporal characteristics from individual positions and metro stations, and a weighted trip-route similarity measure to consider global spatial–temporal characteristics from the entire trajectory and metro route. We conduct extensive experiments on two real datasets to evaluate the effectiveness and efficiency of our proposed approaches. The first dataset contains cellular data from 30 volunteers, including their actual trip details, while the second dataset consists of data from 4 million users. The experiments illustrate the significant accuracy of our approach (with a precision of 87.80% and a recall of 84.28%). Moreover, we demonstrate that FGMTD is efficient in detecting fine-grained trips from a large amount of cellular data, achieving this task within 90 min of processing a day’s data from 4 million users. Full article
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26 pages, 5025 KiB  
Article
Navigating Immovable Assets: A Graph-Based Spatio-Temporal Data Model for Effective Information Management
by Muhammad Syafiq, Suhaibah Azri and Uznir Ujang
ISPRS Int. J. Geo-Inf. 2024, 13(9), 313; https://doi.org/10.3390/ijgi13090313 - 30 Aug 2024
Viewed by 668
Abstract
Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both [...] Read more.
Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling. Full article
(This article belongs to the Topic Geospatial Knowledge Graph)
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24 pages, 32875 KiB  
Article
Integrating Sequential Backward Selection (SBS) and CatBoost for Snow Avalanche Susceptibility Mapping at Catchment Scale
by Sinem Cetinkaya and Sultan Kocaman
ISPRS Int. J. Geo-Inf. 2024, 13(9), 312; https://doi.org/10.3390/ijgi13090312 - 29 Aug 2024
Viewed by 768
Abstract
Snow avalanche susceptibility (AS) mapping is a crucial step in predicting and mitigating avalanche risks in mountainous regions. The conditioning factors used in AS modeling are diverse, and the optimal set of factors depends on the environmental and geological characteristics of the region. [...] Read more.
Snow avalanche susceptibility (AS) mapping is a crucial step in predicting and mitigating avalanche risks in mountainous regions. The conditioning factors used in AS modeling are diverse, and the optimal set of factors depends on the environmental and geological characteristics of the region. Using a sub-optimal set of input features with a data-driven machine learning (ML) method can lead to challenges like dealing with high-dimensional data, overfitting, and reduced model generalization. This study implemented a robust framework involving the Sequential Backward Selection (SBS) algorithm and a decision-tree based ML model, CatBoost, for the automatic selection of predictive variables for AS mapping. A comprehensive inventory of a large avalanche period, previously derived from satellite images, was used for the investigations in three distinct catchment areas in the Swiss Alps. The integrated SBS-CatBoost approach achieved very high classification accuracies between 94% and 97% for the three catchments. In addition, the Shapley additive explanations (SHAP) method was employed to analyze the contributions of each feature to avalanche occurrences. The proposed methodology revealed the benefits of integrating advanced feature selection algorithms with ML techniques for AS assessment. We aimed to contribute to avalanche hazard knowledge by assessing the impact of each feature in model learning. 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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21 pages, 4584 KiB  
Article
CSMNER: A Toponym Entity Recognition Model for Chinese Social Media
by Yuyang Qi, Renjian Zhai, Fang Wu, Jichong Yin, Xianyong Gong, Li Zhu and Haikun Yu
ISPRS Int. J. Geo-Inf. 2024, 13(9), 311; https://doi.org/10.3390/ijgi13090311 - 29 Aug 2024
Viewed by 618
Abstract
In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity [...] Read more.
In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity recognition (CSMNER) model to improve the accuracy and robustness of toponym recognition in Chinese social media texts. By combining the BERT (Bidirectional Encoder Representations from Transformers) pre-trained model with an improved IDCNN-BiLSTM-CRF (Iterated Dilated Convolutional Neural Network- Bidirectional Long Short-Term Memory- Conditional Random Field) architecture, this study innovatively incorporates a boundary extension module to effectively extract the local boundary features and contextual semantic features of the toponym, successfully addressing the recognition challenges posed by noise interference and language expression variability. To verify the effectiveness of the model, experiments were carried out on three datasets: WeiboNER, MSRA, and the Chinese social named entity recognition (CSNER) dataset, a self-built named entity recognition dataset. Compared with the existing models, CSMNER achieves significant performance improvement in toponym recognition tasks. Full article
(This article belongs to the Special Issue Unlocking the Power of Geospatial Data: Semantic Information Extraction, Ontology Engineering, and Deep Learning for Knowledge Discovery)
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24 pages, 7145 KiB  
Article
On the Theoretical Link between Optimized Geospatial Conflation Models for Linear Features
by Zhen Lei, Zhangshun Yuan and Ting L. Lei
ISPRS Int. J. Geo-Inf. 2024, 13(9), 310; https://doi.org/10.3390/ijgi13090310 - 29 Aug 2024
Viewed by 619
Abstract
Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem [...] Read more.
Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem (conceptualized in the 1980s) is one of the earliest conflation methods, in which GIS features from two maps are matched by minimizing their total discrepancy or distance. Recently, more flexible optimization models have been proposed. This includes conflation models based on the network flow problem and new models based on Mixed Integer Linear Programming (MILP). A natural question is: how are these models related or different, and how do they compare? In this study, an analytic review of major optimized conflation models in the literature is conducted and the structural linkages between them are identified. Moreover, a MILP model (the base-matching problem) and its bi-matching version are presented as a common basis. Our analysis shows that the assignment problem and all other optimized conflation models in the literature can be viewed or reformulated as variants of the base models. For network-flow based models, proof is presented that the base-matching problem is equivalent to the network-flow based fixed-charge-matching model. The equivalence of the MILP reformulation is also verified experimentally. For the existing MILP-based models, common notation is established and used to demonstrate that they are extensions of the base models in straight-forward ways. The contributions of this study are threefold. Firstly, it helps the analyst to understand the structural commonalities and differences of current conflation models and to choose different models. Secondly, by reformulating the network-flow models (and therefore, all current models) using MILP, the presented work eases the practical application of conflation by leveraging the many off-the-shelf MILP solvers. Thirdly, the base models can serve as a common ground for studying and writing new conflation models by allowing a modular and incremental way of model development. Full article
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