Journal Description
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information
is an international, peer-reviewed, open access journal on geo-information. The journal is owned by the International Society for Photogrammetry and Remote Sensing (ISPRS) and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), GeoRef, PubAg, dblp, Astrophysics Data System, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Remote Sensing) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 36.2 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.8 (2023);
5-Year Impact Factor:
3.0 (2023)
Latest Articles
Sky-Scanning for Energy: Unveiling Rural Electricity Consumption Patterns through Satellite Imagery’s Convolutional Features
ISPRS Int. J. Geo-Inf. 2024, 13(10), 345; https://doi.org/10.3390/ijgi13100345 (registering DOI) - 26 Sep 2024
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The pursuit of the Sustainable Development Goals has highlighted rural electricity consumption patterns, necessitating innovative analytical approaches. This paper introduces a novel method for predicting rural electricity consumption by leveraging deep convolutional features extracted from satellite imagery. The study employs a pretrained remote
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The pursuit of the Sustainable Development Goals has highlighted rural electricity consumption patterns, necessitating innovative analytical approaches. This paper introduces a novel method for predicting rural electricity consumption by leveraging deep convolutional features extracted from satellite imagery. The study employs a pretrained remote sensing interpretation model for feature extraction, streamlining the training process and enhancing the prediction efficiency. A random forest model is then used for electricity consumption prediction, while the SHapley Additive exPlanations (SHAP) model assesses the feature importance. To explain the human geography implications of feature maps, this research develops a feature visualization method grounded in expert knowledge. By selecting feature maps with higher interpretability, the “black-box” model based on remote sensing images is further analyzed and reveals the geographical features that affect electricity consumption. The methodology is applied to villages in Xinxing County, Guangdong Province, China, achieving high prediction accuracy with a correlation coefficient of 0.797. The study reveals a significant positive correlations between the characteristics and spatial distribution of houses and roads in the rural built environment and electricity demand. Conversely, natural landscape elements, such as farmland and forests, exhibit significant negative correlations with electricity demand predictions. These findings offer new insights into rural electricity consumption patterns and provide theoretical support for electricity planning and decision making in line with the Sustainable Development Goals.
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Open AccessArticle
Pre-Dam Vltava River Valley—A Case Study of 3D Visualization of Large-Scale GIS Datasets in Unreal Engine
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Michal Janovský
ISPRS Int. J. Geo-Inf. 2024, 13(10), 344; https://doi.org/10.3390/ijgi13100344 - 26 Sep 2024
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This article explores the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale areas (1000 km2 and more) using GIS datasets. Unlike small-scale visualizations, large-scale visualizations are rare and often not public, which presents significant problems
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This article explores the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale areas (1000 km2 and more) using GIS datasets. Unlike small-scale visualizations, large-scale visualizations are rare and often not public, which presents significant problems since they present different challenges and require different approaches. This article presents several relevant scientific studies and projects that have successfully used game engines for similar purposes. This case study focuses on the computational techniques used in Unreal Engine for the 3D visualization of GIS data and the potential application of Unreal Engine in large-scale geo-visualizations. It explores the potential for using GIS data within a game engine, including plug-ins that provide additional functionality for working with GIS data, such as the Vitruvio plug-in to implement procedural modeling of buildings. The case study is applied to GIS datasets of the historical Vltava Valley covering an area of 1670 km2 to demonstrate the unique challenges of using Unreal Engine to create realistic visualizations of large-scale historical landscapes. The resulting visualizations are presented. The practical application of this research provides insights into the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale historical areas.
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Open AccessReview
The Status of the Implementation of the Building Information Modeling Mandate in Poland: A Literature Review
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Andrzej Szymon Borkowski, Wojciech Drozd and Krzysztof Zima
ISPRS Int. J. Geo-Inf. 2024, 13(10), 343; https://doi.org/10.3390/ijgi13100343 - 26 Sep 2024
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BIM is being strongly implemented in design companies. General contractors are using it during investment projects, and boards are using it for the maintenance and operation of buildings or infrastructure. Without the so-called BIM mandate (mandatory in public procurement), this is hard to
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BIM is being strongly implemented in design companies. General contractors are using it during investment projects, and boards are using it for the maintenance and operation of buildings or infrastructure. Without the so-called BIM mandate (mandatory in public procurement), this is hard to imagine, even though it has already been implemented in many countries. In Poland, work in this direction is still being carried out. Due to the high complexity of investment and construction processes, the multiplicity of stakeholder groups, and conflicting interests, work on BIM adoption at the national level is hampered. The paper conducts an in-depth literature review of BIM implementation in Poland and presents a critical analysis of the current state of work. As a result of the literature research, proposals for changes in the processes of implementing the BIM mandate in Poland were formulated. This paper presents an excerpt from a potential BIM strategy and the necessary steps on the road to making BIM use mandatory. The results of the study indicate strong grassroots activity conducted by NGOs, which, independent of government actions, lead to measurable results. The authors propose that these activities must be coordinated by a single leading entity at the government level. The study could influence decisions made in other countries in the region or with similar levels of BIM adoption. BIM is the basis of the idea of the digital twin, and its implementation is necessary to achieve the goals of the doctrine of sustainable development and circular economy.
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Open AccessArticle
Analysis of Guidance Signage Systems from a Complex Network Theory Perspective: A Case Study in Subway Stations
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Fei Peng, Zhe Zhang and Qingyan Ding
ISPRS Int. J. Geo-Inf. 2024, 13(10), 342; https://doi.org/10.3390/ijgi13100342 - 25 Sep 2024
Abstract
Guidance signage systems (GSSs) play a large role in pedestrian navigation for public buildings. A vulnerable GSS can cause wayfinding troubles for pedestrians. In order to investigate the robustness of GSSs, a complex network-based GSS robustness analysis framework is proposed in this paper.
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Guidance signage systems (GSSs) play a large role in pedestrian navigation for public buildings. A vulnerable GSS can cause wayfinding troubles for pedestrians. In order to investigate the robustness of GSSs, a complex network-based GSS robustness analysis framework is proposed in this paper. First, a method that can transform a GSS into a guidance service network (GSN) is proposed by analyzing the relationships among various signs, and signage node metrics are proposed to evaluate the importance of signage nodes. Second, two network performance metrics, namely, the level of visibility and guidance efficiency, are proposed to evaluate the robustness of the GSN under various disruption modes, and the most important signage node metrics are determined. Finally, a multi-objective optimization model is established to find the optimal weights of these metrics, and a comprehensive evaluation method is proposed to position the critical signage nodes that should receive increased maintenance efforts. A case study was conducted in a subway station and the GSS was transformed into a GSN successfully. The analysis results show that the GSN has scale-free characteristics, and recommendations for GSS design are proposed on the basis of robustness analysis. The signage nodes with high betweenness centrality play a greater role in the GSN than the signage nodes with high degree centrality. The proposed critical signage node evaluation method can be used to efficiently identify the signage nodes for which failure has the greatest effects on GSN performance.
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(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)
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Open AccessArticle
Urban Spatiotemporal Event Prediction Using Convolutional Neural Network and Road Feature Fusion Network
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Yirui Jiang, Shan Zhao, Hongwei Li, Huijing Wu and Wenjie Zhu
ISPRS Int. J. Geo-Inf. 2024, 13(10), 341; https://doi.org/10.3390/ijgi13100341 - 25 Sep 2024
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The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the
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The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the occurrence of urban events in advance is of utmost importance. However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. The method is divided into two stages: The first stage constructs feature map and structure of CNN then selects the optimal feature map and number of CNN layers. The second stage extracts urban road network information using multiscale convolution and incorporates the extracted road network feature information into the CNN. Some comparison experiments are conducted on the 2018–2019 urban patrol events dataset in Zhengzhou City, China. The CNN-rFFN method has an R2 value of 0.9430, which is higher than the CNN, CNN-LSTM, Dilated-CNN, ResNet, and ST-ResNet algorithms. The experimental results demonstrate that the CNN-rFFN method has better performance than other methods.
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Open AccessArticle
Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment
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Amil Aghayev, Tomáš Řezník and Milan Konečný
ISPRS Int. J. Geo-Inf. 2024, 13(10), 340; https://doi.org/10.3390/ijgi13100340 - 24 Sep 2024
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This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P2O5), potassium
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This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P2O5), potassium (K2O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security.
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Open AccessArticle
Urban–Rural Exposure to Flood Hazard and Social Vulnerability in the Conterminous United States
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Bishal Dhungana and Weibo Liu
ISPRS Int. J. Geo-Inf. 2024, 13(9), 339; https://doi.org/10.3390/ijgi13090339 - 22 Sep 2024
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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
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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.
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(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)
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Open AccessArticle
Commutative Encryption and Reversible Watermarking Algorithm for Vector Maps Based on Virtual Coordinates
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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
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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
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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.
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(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)
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Open AccessArticle
Nonlinear Influence of the Built Environment on the Attraction of the Third Activity: A Comparative Analysis of Inflow from Home and Work
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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
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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).
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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.
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Open AccessArticle
A New Subject-Sensitive Hashing Algorithm Based on Multi-PatchDrop and Swin-Unet for the Integrity Authentication of HRRS Image
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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
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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
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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.
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Open AccessArticle
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
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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
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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
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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.
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Open AccessArticle
Geographical Entity Management Model Based on Multi-Classification
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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
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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
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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.
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Open AccessArticle
Geometric Characterization of the Mateur Plain in Northern Tunisia Using Vertical Electrical Sounding and Remote Sensing Techniques
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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
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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
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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.
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(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)
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Open AccessSystematic Review
Potentials in Using VR for Facilitating Geography Teaching in Classrooms: A Systematic Review
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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
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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
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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.
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Open AccessArticle
Urban Internal Network Structure and Resilience Characteristics from the Perspective of Population Mobility: A Case Study of Nanjing, China
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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
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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
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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.
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Open AccessArticle
Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan
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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
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
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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.
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(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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Open AccessArticle
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
Abstract
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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
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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.
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Open AccessArticle
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
Abstract
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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
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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.
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Open AccessArticle
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
Abstract
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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,
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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.
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Open AccessArticle
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
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)
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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.
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(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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