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 (Geography, Physical) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 35.5 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- 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:
3.4 (2022);
5-Year Impact Factor:
3.5 (2022)
Latest Articles
Comprehension of City Map Pictograms Designed for Specific Tourists’ Needs
ISPRS Int. J. Geo-Inf. 2024, 13(4), 137; https://doi.org/10.3390/ijgi13040137 - 18 Apr 2024
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This study investigated the effectiveness of new point-of-interest (POI) pictograms on tourist maps within the Croatian and Slovenian contexts, focusing on enhancing the tourist experience in urban settings for individuals with specific needs. Despite the widespread use of tourist maps, there is a
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This study investigated the effectiveness of new point-of-interest (POI) pictograms on tourist maps within the Croatian and Slovenian contexts, focusing on enhancing the tourist experience in urban settings for individuals with specific needs. Despite the widespread use of tourist maps, there is a lack of research evaluating POI pictograms tailored to the needs of tourists with specific dietary, health-related, and sustainable clothing purchases. To fill this gap, we designed six new pictograms in three domains: healthcare, food, and apparel. The pictograms were evaluated using an online questionnaire involving 138 participants with a diverse range of ages and educational backgrounds. The results on comprehension and subjective assessments of the pictograms’ qualities revealed insights into the subtle cultural and lifestyle influences on pictogram perception. The findings provide a basis for considering the potential of new pictogram designs in improving navigational experiences with geospatial information and encouraging sustainable tourist behaviors.
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Open AccessArticle
Scale Distribution of Retail Formats in the Central Districts of Chinese Cities: A Study Analysis of Ten Cities
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Yi Shi, Yidian Wang, Yifan Ren, Chunyu Zhou and Xinyu Hu
ISPRS Int. J. Geo-Inf. 2024, 13(4), 136; https://doi.org/10.3390/ijgi13040136 - 18 Apr 2024
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Analyses of urban hierarchy and scale distribution are crucial in urban research, as they examine the laws of urban development and the functional layout of urban spatial systems. However, previous studies have focused on the macro-spatial distribution of the economy, businesses, and population
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Analyses of urban hierarchy and scale distribution are crucial in urban research, as they examine the laws of urban development and the functional layout of urban spatial systems. However, previous studies have focused on the macro-spatial distribution of the economy, businesses, and population at the regional level, whereas systematic research on the scale distribution of retail formats in central urban areas is lacking. Therefore, this study investigated the hierarchical scale distribution of retail formats in the top ten cities in China by GDP, using the Public Service Facilities Index Method to define central district boundaries, using scale as an epistemological framework of order and analyzing the spatial distribution patterns of retail formats. The results revealed that the spatial hierarchical scale follows a power law within a certain range; the spatial distribution exhibits stage characteristics, providing a quantitative method for defining retail centres; and the dominant functions, development directions, and morphological characteristics of central districts influence the hierarchical scale distribution of retail formats.
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Open AccessArticle
A Hierarchy-Aware Geocoding Model Based on Cross-Attention within the Seq2Seq Framework
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Linlin Liang, Yuanfei Chang, Yizhuo Quan and Chengbo Wang
ISPRS Int. J. Geo-Inf. 2024, 13(4), 135; https://doi.org/10.3390/ijgi13040135 - 17 Apr 2024
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Geocoding converts unstructured geographic text into structured spatial data, which is crucial in fields such as urban planning, social media spatial analysis, and emergency response systems. Existing approaches predominantly model geocoding as a geographic grid classification task but struggle with the output space
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Geocoding converts unstructured geographic text into structured spatial data, which is crucial in fields such as urban planning, social media spatial analysis, and emergency response systems. Existing approaches predominantly model geocoding as a geographic grid classification task but struggle with the output space dimensionality explosion as the grid granularity increases. Furthermore, these methods generally overlook the inherent hierarchical structure of geographical texts and grids. In this paper, we propose a hierarchy-aware geocoding model based on cross-attention within the Seq2Seq framework, incorporating S2 geometry to model geocoding as a task for generating grid labels and predicting S2 tokens (labels of S2 grids) character-by-character. By incorporating a cross-attention mechanism into the decoder, the model dynamically perceives the address contexts at the hierarchical level that are most relevant to the current character prediction based on the input address text. Results show that the proposed model significantly outperforms previous approaches across multiple metrics, with a median and mean distance error of 41.46 m and 93.98 m, respectively. Furthermore, our method achieves superior results compared to others in regions with sparse data distribution, reducing the median and mean distance error by 16.27 m and 7.52 m, respectively, suggesting that our model has effectively mitigated the issue of insufficient learning in such regions.
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Open AccessArticle
Spatiotemporal Analysis of Water Body in the Chongming Island Region over the Past Decade Based on the ISUNet Model
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Lizhi Miao, Xinkai Feng, Lijun Yang, Yanhui Ren, Yamei Deng and Tian Hang
ISPRS Int. J. Geo-Inf. 2024, 13(4), 134; https://doi.org/10.3390/ijgi13040134 - 17 Apr 2024
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Chongming Island and its surrounding areas are highly significant coastal regions in China. However, the regions undergo continuous changes owing to various factors, such as the sedimentation from the Yangtze River, human activities, and tidal movements. Chongming Island is part of the Yangtze
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Chongming Island and its surrounding areas are highly significant coastal regions in China. However, the regions undergo continuous changes owing to various factors, such as the sedimentation from the Yangtze River, human activities, and tidal movements. Chongming Island is part of the Yangtze River Delta, which is one of the most economically developed regions in China. Studying the water body changes in this area is of great importance for decision making in water resource conservation, coastal resource management, and ecological environmental protection. In this study, we propose an improved ISUNet model by enhancing the skip-connection operations in the traditional UNet architecture. We extracted and analyzed the water bodies in Chongming Island and its surrounding areas from 2013 to 2022, providing a detailed spatiotemporal analysis of the water body area over the years. The results indicate that the water body area in the study area has decreased by 267.8 km2 over the past decade, showing a gradually fluctuating downward trend with an average annual reduction of nearly 27 km2. The analysis suggests that the reduction in the water body area is mainly attributed to sedimentation near river channels and ports, the formation of sandbars owing to channel erosion, and the artificial construction of ports and coastal areas. The influencing factors include human activities and sedimentation from the Yangtze River Estuary. Specifically, human activities such as land reclamation, port construction, and aquaculture play a major role in causing changes in the area.
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Open AccessArticle
An LLM-Based Inventory Construction Framework of Urban Ground Collapse Events with Spatiotemporal Locations
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Yanan Hao, Jin Qi, Xiaowen Ma, Sensen Wu, Renyi Liu and Xiaoyi Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(4), 133; https://doi.org/10.3390/ijgi13040133 - 16 Apr 2024
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Historical news media reports serve as a vital data source for understanding the risk of urban ground collapse (UGC) events. At present, the application of large language models (LLMs) offers unprecedented opportunities to effectively extract UGC events and their spatiotemporal information from a
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Historical news media reports serve as a vital data source for understanding the risk of urban ground collapse (UGC) events. At present, the application of large language models (LLMs) offers unprecedented opportunities to effectively extract UGC events and their spatiotemporal information from a vast amount of news reports and media data. Therefore, this study proposes an LLM-based inventory construction framework consisting of three steps: news reports crawling, UGC event recognition, and event attribute extraction. Focusing on Zhejiang province, China, as the test region, a total of 27 cases of collapse events from 637 news reports were collected for 11 prefecture-level cities. The method achieved a recall rate of over 60% and a precision below 35%, indicating its potential for effectively and automatically screening collapse events; however, the accuracy needs to be improved to account for confusion with other urban collapse events, such as bridge collapses. The obtained UGC event inventory is the first open access inventory based on internet news reports, event dates and locations, and collapse co-ordinates derived from unstructured contents. Furthermore, this study provides insights into the spatial pattern of UGC frequency in Zhejiang province, effectively supplementing the statistical data provided by the local government.
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(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)
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Evaluating the Impact of Human Activities on Vegetation Restoration in Mining Areas Based on the GTWR
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Li Guo, Jun Li, Chengye Zhang, Yaling Xu, Jianghe Xing and Jingyu Hu
ISPRS Int. J. Geo-Inf. 2024, 13(4), 132; https://doi.org/10.3390/ijgi13040132 - 16 Apr 2024
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The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (
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The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas.
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(This article belongs to the Topic Advances in Earth Observation and Geosciences)
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Unveiling the Non-Linear Influence of Eye-Level Streetscape Factors on Walking Preference: Evidence from Tokyo
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Lu Huang, Takuya Oki, Sachio Muto and Yoshiki Ogawa
ISPRS Int. J. Geo-Inf. 2024, 13(4), 131; https://doi.org/10.3390/ijgi13040131 - 15 Apr 2024
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Promoting walking is crucial for sustainable development and fosters individual health and well-being. Therefore, comprehensive investigations of factors that make walking attractive are vital. Previous research has linked streetscapes at eye-level to walking preferences, which usually focuses on simple linear relationships, neglecting the
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Promoting walking is crucial for sustainable development and fosters individual health and well-being. Therefore, comprehensive investigations of factors that make walking attractive are vital. Previous research has linked streetscapes at eye-level to walking preferences, which usually focuses on simple linear relationships, neglecting the complex non-linear dynamics. Additionally, the varied effects of streetscape factors across street segments and intersections and different street structures remain largely unexplored. To address these gaps, this study explores how eye-level streetscapes influence walking preferences in various street segments and intersections in Setagaya Ward, Tokyo. Using street view data, an image survey, and computer vision algorithms, we measured eye-level streetscape factors and walking preferences. The Extreme Gradient Boosting (XGBoost) model was then applied to analyze their non-linear relationships. This study identified key streetscape factors influencing walking preferences and uncovered non-linear trends within various factors, showcasing a variety of patterns, including upward, downward, and threshold effects. Moreover, our findings highlight the heterogeneity of the structural characteristics of street segments and intersections, which also impact the relationship between eye-level streetscapes and walking preferences. These insights can significantly inform decision-making in urban streetscape design, enhancing pedestrian perceptions.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Multi-Criteria Framework for Routing on Access Land: A Case Study on Dartmoor National Park
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Rafael Felipe Sprent, James Haworth, Stefano Cavazzi and Ilya Ilyankou
ISPRS Int. J. Geo-Inf. 2024, 13(4), 130; https://doi.org/10.3390/ijgi13040130 - 14 Apr 2024
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Creating routes across open areas is challenging due to the absence of a defined routing network and the complexity of the environment, in which multiple criteria may affect route choice. In the context of urban environments, research has found Visibility and Spider-Grid subgraphs
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Creating routes across open areas is challenging due to the absence of a defined routing network and the complexity of the environment, in which multiple criteria may affect route choice. In the context of urban environments, research has found Visibility and Spider-Grid subgraphs to be effective approaches that generate realistic routes. However, the case studies presented typically focus on plazas or parks with defined entry and exit points; little work has been carried out to date on creating routes across open areas in rural settings, which are complex environments with varying terrain and obstacles and undefined entry or exit points. To address this gap, this study proposes a method for routing across open areas based on a Spider-Grid subgraph using queen contiguity. The method leverages a Weighted Sum–Dijkstra’s algorithm to allow multiple criteria such as surface condition, total time, and gradient to be considered when creating routes. The method is tested on the problem of routing across two areas of Dartmoor National Park, United Kingdom. The generated routes are compared with benchmark algorithms and real paths created by users of the Ordnance Survey’s Maps App. The generated routes are found to be more realistic than those of the benchmark methods and closer to the real paths. Furthermore, the routes are able to bypass hazards and obstacles while still providing realistic and flexible routes to the user.
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Spatial Relationship of Inter-City Population Movement and Socio-Economic Determinants: A Case Study in China Using Multiscale Geographically Weighted Regression
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Sihan Liu and Xinyi Niu
ISPRS Int. J. Geo-Inf. 2024, 13(4), 129; https://doi.org/10.3390/ijgi13040129 - 12 Apr 2024
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In the current field of regional studies, there is a growing focus on regional spatial relationships from the perspective of functional linkages between cities. Inter-city population movement serves as an embodiment of the integrated functionality of cities within a region, and this is
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In the current field of regional studies, there is a growing focus on regional spatial relationships from the perspective of functional linkages between cities. Inter-city population movement serves as an embodiment of the integrated functionality of cities within a region, and this is closely tied to the socio-economic development of urban areas. This study utilized Location-Based Services (LBSs) to collect the scale of inter-city population movement across 355 cities in China. Additionally, socio-economic data published by local governments were incorporated. By establishing a Multiscale Geographically Weighted Regression (MGWR) model, this research explores the spatial relationships between inter-city population movement and socio-economic influencing factors in China. This study aims to elucidate the spatial scales of the relationships between various variables. Our research findings indicate that the relationship between inter-city population movement and potential socio-economic determinants exhibits spatial non-stationarity. It is better to explore this spatial relationship through the MGWR model as there are different determinants operating on inter-city population movement at different spatial scales. The spatial distribution of the coefficient estimates shows significant regional differences and numerical variations. In China’s economically developed coastal regions, there is relatively balanced development among cities, with advanced manufacturing and producer service industries acting as significant drivers of mobility. In inland regions of China, city size is the most influential variable, directing a substantial flow of human and economic resources towards regional socio-economic hubs such as provincial capitals. The main contribution of this study is the re-examination of the relationship between inter-city population movement and socio-economic factors from the perspective of spatial scales. This approach will help China to consider the heterogeneity of different regions more extensively when formulating regional development policies, thereby facilitating the targeted promotion of regional element flow.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Search Engine for Open Geospatial Consortium Web Services Improving Discoverability through Natural Language Processing-Based Processing and Ranking
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Elia Ferrari, Friedrich Striewski, Fiona Tiefenbacher, Pia Bereuter, David Oesch and Pasquale Di Donato
ISPRS Int. J. Geo-Inf. 2024, 13(4), 128; https://doi.org/10.3390/ijgi13040128 - 12 Apr 2024
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The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation
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The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques.
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(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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Map Reading and Analysis with GPT-4V(ision)
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Jinwen Xu and Ran Tao
ISPRS Int. J. Geo-Inf. 2024, 13(4), 127; https://doi.org/10.3390/ijgi13040127 - 11 Apr 2024
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In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geography, and spatial data science. In this study, we explore
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In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geography, and spatial data science. In this study, we explore reading and analyzing maps with the latest version of GPT-4-vision-preview (GPT-4V), to fully evaluate its advantages and disadvantages in comparison with human eye-based visual inspections. We found that GPT-4V is able to properly retrieve information from various types of maps in different scales and spatiotemporal resolutions. GPT-4V can also perform basic map analysis, such as identifying visual changes before and after a natural disaster. It has the potential to replace human efforts by examining batches of maps, accurately extracting information from maps, and linking observed patterns with its pre-trained large dataset. However, it is encumbered by limitations such as diminished accuracy in visual content extraction and a lack of validation. This paper sets an example of effectively using GPT-4V for map reading and analytical tasks, which is a promising application for large multimodal models, large language models, and artificial intelligence.
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(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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Comparison of Different Green Space Measures and Their Impact on Dementia Cases in South Korea: A Spatial Panel Analysis
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Wulan Salle Karurung, Kangjae Lee and Wonhee Lee
ISPRS Int. J. Geo-Inf. 2024, 13(4), 126; https://doi.org/10.3390/ijgi13040126 - 09 Apr 2024
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Dementia has become a profound public health problem due to the number of patients increasing every year. Previous studies have reported that environmental factors, including greenness, may influence the development and progression of dementia. Studies have found that exposure to green space is
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Dementia has become a profound public health problem due to the number of patients increasing every year. Previous studies have reported that environmental factors, including greenness, may influence the development and progression of dementia. Studies have found that exposure to green space is associated with a lower incidence of dementia. However, many definitions of green space exist, and the effects of its use may differ with the type of green space. Therefore, two types of green space measures were considered in this study to assess the differences in their impact on the prevalence of dementia among females and males. This study used five years of data (2017–2021) from 235 districts in South Korea. The two green space measures used were open space density and normalized difference vegetation index (NDVI), which were derived from satellite images. The analysis utilized a combination of traditional and spatial panel analyses to account for the spatial and temporal effects of independent variables on dementia prevalence. The spatial autocorrelation results revealed that both measures of greenness were spatially correlated with dementia prevalence. The spatial panel regression results revealed a significant positive association between NDVI and dementia prevalence, and open space had a negative association with dementia prevalence in both genders. The difference in the findings can serve as the basis for further research when choosing a greenspace measure, as it affects the analysis results, depending on the objective of the study. This study adds to the knowledge regarding improving dementia studies and the application of spatial panel analysis in epidemiological studies.
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(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism
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Amel Oubara, Falin Wu, Reza Maleki, Boyi Ma, Abdenour Amamra and Gongliu Yang
ISPRS Int. J. Geo-Inf. 2024, 13(4), 125; https://doi.org/10.3390/ijgi13040125 - 09 Apr 2024
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Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged
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Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged areas in object CD datasets, such as buildings, poses a critical issue affecting DL model efficacy. To address this issue, this paper proposes a change detection enhancement method using artificial image generation and attention mechanism. Firstly, the content of the imbalanced CD dataset is enhanced using a data augmentation strategy that synthesizes effective building CD samples using artificial RS image generation and building label creation. The created building labels, which serve as new change maps, are fed into a generator model based on a conditional Generative Adversarial Network (c-GAN) to generate high-resolution RS images featuring building changes. The generated images with their corresponding change maps are then added to the CD dataset to create the balance between changed and unchanged samples. Secondly, a channel attention mechanism is added to the proposed Adversarial Change Detection Network (Adv-CDNet) to boost its performance when training on the imbalanced dataset. The study evaluates the Adv-CDNet using WHU-CD and LEVIR-CD datasets, with WHU-CD exhibiting a higher degree of sample imbalance compared to LEVIR-CD. Training the Adv-CDNet on the augmented dataset results in a significant 16.5% F1-Score improvement for the highly imbalanced WHU-CD. Moreover, comparative analysis showcases the superior performance of the Adv-CDNet when complemented with the attention module, achieving a 6.85% F1-Score enhancement.
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Open AccessArticle
Mapmaking Process Reading from Local Distortions in Historical Maps: A Geographically Weighted Bidimensional Regression Analysis of a Japanese Castle Map
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Naoto Yabe
ISPRS Int. J. Geo-Inf. 2024, 13(4), 124; https://doi.org/10.3390/ijgi13040124 - 09 Apr 2024
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Shoho Castle Maps are maps of castle towns throughout Japan drawn by Kano School painters on the order of the shogun in 1644. The Shoho Castle Map of Takada, Joetsu City, Niigata Prefecture was used to visualize local distortions in historical maps and
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Shoho Castle Maps are maps of castle towns throughout Japan drawn by Kano School painters on the order of the shogun in 1644. The Shoho Castle Map of Takada, Joetsu City, Niigata Prefecture was used to visualize local distortions in historical maps and to scrutinize the mapmaking process. A novel method, geographically weighted bidimensional regression, was developed and applied to visualize the local distortions of the map. Exaggerated expressions by mapmakers that have not been identified in previous studies were revealed. That is, in addition to the castle being drawn enlarged, the town where the merchants and artisans lived was drawn larger than the castle. Therefore, the Takada Shoho Castle Map reflects mapmakers’ intentions, besides enlarging military facilities, which appear to have emphasized the pictorial composition of the map by placing the main gate to the castle at the center and drawing the map area evenly from the center in a well-balanced layout.
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Open AccessArticle
Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia
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Hadis Pakdel, Sreeni Chadalavada, Md Jahangir Alam, Dev Raj Paudyal and Majid Vazifedoust
ISPRS Int. J. Geo-Inf. 2024, 13(4), 123; https://doi.org/10.3390/ijgi13040123 - 08 Apr 2024
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The severity and frequency of extremes are changing; thus, it is becoming necessary to evaluate the impacts of land cover changes and urbanisation along with climate change. A framework of the Generalised Extreme Value (GEV) method, Google Earth Engine (GEE), and land cover
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The severity and frequency of extremes are changing; thus, it is becoming necessary to evaluate the impacts of land cover changes and urbanisation along with climate change. A framework of the Generalised Extreme Value (GEV) method, Google Earth Engine (GEE), and land cover patterns’ classification including Random Forest (RF) and Support Vector Machine (SVM) can be useful for streamflow impact analysis. For this study, we developed a unique framework consisting of a hydrological model in line with the Process-informed Nonstationary Extreme Value Analysis (ProNEVA) GEV model and an ensemble of General Circulation Models (GCMs), mapping land cover patterns using classification methods within the GEE platform. We applied these methods in Southeast Queensland (SEQ) to analyse the maximum instantaneous floods in non-stationary catchment conditions, considering the physical system in terms of cause and effect. Independent variables (DEM, population, slope, roads, and distance from roads) and an integrated RF, SVM methodology were utilised as spatial maps to predict their influences on land cover changes for the near and far future. The results indicated that physical factors significantly influence the layout of landscapes. First, the values of projected evapotranspiration and rainfall were extracted from the multi-model ensemble to investigate the eight GCMs under two climate change scenarios (RCP4.5 and RCP8.5). The AWBM hydrological model was calibrated with daily streamflow and applied to generate historical runoff for 1990–2010. Runoff was projected under two scenarios for eight GCMs and by incorporating the percentage of each land cover into the hydrological model for two horizons (2020–2065 and 2066–2085). Following that, the ProNEVA model was used to calculate the frequency and magnitude of runoff extremes across the parameter space. The maximum peak flood differences under the RCP4.5 and RCP8.5 scenarios were 16.90% and 15.18%, respectively. The outcomes of this study suggested that neglecting the non-stationary assumption in flood frequency can lead to underestimating the amounts that can lead to more risks for the related hydraulic structures. This framework is adaptable to various geographical regions to estimate extreme conditions, offering valuable insights for infrastructure design, planning, risk assessment, and the sustainable management of future water resources in the context of long-term water management plans.
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Open AccessArticle
Balancing Flood Control and Economic Development in Flood Detention Areas of the Yangtze River Basin
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Siyuan Liao, Chao Wang, Renke Ji, Xiang Zhang, Zhifei Wang, Wei Wang and Nengcheng Chen
ISPRS Int. J. Geo-Inf. 2024, 13(4), 122; https://doi.org/10.3390/ijgi13040122 - 08 Apr 2024
Abstract
Serving as a crucial part of the Yangtze River Basin (YRB)’s flood control system, Flood Detention Areas (FDAs) are vital in mitigating large-scale floods. Urbanization has led to the development of urban FDAs, but significant losses could ensue if these FDAs are activated.
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Serving as a crucial part of the Yangtze River Basin (YRB)’s flood control system, Flood Detention Areas (FDAs) are vital in mitigating large-scale floods. Urbanization has led to the development of urban FDAs, but significant losses could ensue if these FDAs are activated. With improved reservoirs and embankments, flood pressure in the middle reaches has lessened, posing challenges in balancing flood control and economic benefits. This paper presents a comparative analysis of land use, GDP, and population in FDAs and adjacent cities, enhancing our understanding of their disparities and interrelations. Using the Analytic Hierarchy Process (AHP)–Entropy Weight Method (EW)–Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method, we assess changes in flood control and economic values in FDAs. The results show a conflict between flood control and economic policies in FDAs, highlighting their underestimated economic potential, especially in urban areas. This study identifies differences in economic development across FDAs and a strong correlation between flood control value and inundation rates. Based on evaluations and simulations of the 1954 flood, we provide recommendations for the FDAs’ construction plan, which serves the development and flood management of the YRB and offer insights for similar assessments elsewhere.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Mapping the CityGML Energy ADE to CityGML 3.0 Using a Model-Driven Approach
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Carolin Bachert, Camilo León-Sánchez, Tatjana Kutzner and Giorgio Agugiaro
ISPRS Int. J. Geo-Inf. 2024, 13(4), 121; https://doi.org/10.3390/ijgi13040121 - 04 Apr 2024
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With the increasing adoption of semantic 3D city models, the relevance of applications in the field of Urban Building Energy Modelling (UBEM) has rapidly grown, as the building sector accounts for a large part of the total energy consumption. UBEM allows us to
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With the increasing adoption of semantic 3D city models, the relevance of applications in the field of Urban Building Energy Modelling (UBEM) has rapidly grown, as the building sector accounts for a large part of the total energy consumption. UBEM allows us to better understand the energy performance of the building stock and can contribute to defining refurbishment strategies. However, UBEM applications require lots of heterogeneous data, eventually advocating for standards for data interoperability. The Energy Application Domain Extension has been created to cope with UBEM data requirements and offers a standardised data model that builds upon the CityGML standard. The Energy ADE 1.0, released in 2018, creates new classes and adds new properties to existing classes of the CityGML 2.0 Core and Building modules. CityGML 3.0, released in 2021, has introduced several changes to the data model and its ADE mechanism. These changes render the Energy ADE incompatible with CityGML 3.0. This article presents how the Energy ADE has been ported to CityGML 3.0 to allow, on the one hand, for a lossless data conversion and, on the other hand, to exploit the new characteristics of CityGML 3.0 while keeping a logical symmetry between the ADE classes of both CityGML versions. The article describes the chosen methodology, the mapping strategies, the implementation steps, as well as the data conversion tests to check the validity of the “new” Energy ADE for CityGML 3.0.
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Open AccessArticle
How Does the 2D/3D Urban Morphology Affect the Urban Heat Island across Urban Functional Zones? A Case Study of Beijing, China
by
Shouhang Du, Yuhui Wu, Liyuan Guo, Deqin Fan and Wenbin Sun
ISPRS Int. J. Geo-Inf. 2024, 13(4), 120; https://doi.org/10.3390/ijgi13040120 - 04 Apr 2024
Abstract
Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research
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Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research on land use and landscape patterns, understanding the effects of 2D/3D urban morphology in different UFZs is lacking. This study employs Landsat-8 remote sensing data to retrieve the land surface temperature (LST). A method combining supervised and unsupervised classification is proposed for UFZ mapping, utilizing multi-source geospatial data. Subsequently, parameters defining the 2D/3D urban morphology of UFZs are established. Finally, the Pearson correlation analysis and GeoDetector are used to analyze the driving factors. The results indicate the following: (1) In the Fifth Ring Road area of Beijing, the residential zones exhibit the highest LST, followed by the industrial zones. (2) In 2D urban morphology, the percentage of built-up landscape (built-PLAND) and Shannon’s diversity index (SHDI) are the main factors influencing LST. In 3D urban morphology, building density, the sky view factor (SVF), and the area-weighted mean shape index (shape index) are the main factors influencing LST. Therefore, low-density buildings with simple and dispersed shapes contribute to mitigating LST, while fragmented distributions of trees, grasslands, and water bodies also play important roles in alleviating LST. (3) In the interactive detection results, all UFZs show the highest interaction detection results with the built-PLAND. (4) Spatial variations are observed in the impact of different UFZs on LST. For instance, in the residential zones, industrial zones, green space zones, and public service zones, the SVF is negatively correlated with LST, while in the commercial zones, the SVF exhibits a positive correlation with LST.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Open AccessArticle
Low-Cost Data, High-Quality Models: A Semi-Automated Approach to LOD3 Creation
by
Harshit, Pallavi Chaurasia, Sisi Zlatanova and Kamal Jain
ISPRS Int. J. Geo-Inf. 2024, 13(4), 119; https://doi.org/10.3390/ijgi13040119 - 03 Apr 2024
Abstract
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In the dynamic realm of digital twin modeling, where advancements are swiftly unfolding, users now possess the unprecedented ability to capture and generate geospatial data in real time. This article delves into a critical exploration of this landscape by presenting a meticulously devised
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In the dynamic realm of digital twin modeling, where advancements are swiftly unfolding, users now possess the unprecedented ability to capture and generate geospatial data in real time. This article delves into a critical exploration of this landscape by presenting a meticulously devised workflow tailored for the creation of Level of Detail 3 (LOD3) models. Our research methodology capitalizes on the integration of Apple LiDAR technology alongside photogrammetric point clouds acquired from Unmanned Aerial Vehicles (UAVs). The proposed process unfolds with the transformation of point cloud data into Industry Foundation Classes (IFC) models, which are subsequently refined into LOD3 Geographic Information System (GIS) models leveraging the Feature Manipulation Engine (FME) workbench 2022.1.2. This orchestrated synergy among Apple LiDAR, UAV-derived photogrammetric point clouds, and the transformative capabilities of the FME culminates in the development of precise LOD3 GIS models. Our proposed workflow revolutionizes this landscape by integrating multi-source point clouds, imbuing them with accurate semantics derived from IFC models, and culminating in the creation of valid CityGML LOD3 buildings through sophisticated 3D geometric operations. The implications of this technical innovation are profound. Firstly, it elevates the capacity to produce intricate infrastructure models, unlocking new vistas for modeling digital twins. Secondly, it extends the horizons of GIS applications by seamlessly integrating enriched Building Information Modeling (BIM) components, thereby enhancing decision-making processes and facilitating more comprehensive spatial analyses.
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Open AccessArticle
Animating Cartographic Meaning: Unveiling the Impact of Pictorial Symbol Motion Speed in Preattentive Processing
by
Paweł Cybulski
ISPRS Int. J. Geo-Inf. 2024, 13(4), 118; https://doi.org/10.3390/ijgi13040118 - 03 Apr 2024
Abstract
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The primary objective of this study is to assess how the motion of dynamic point symbols impacts preattentive processing on a map. Specifically, it involves identifying the motion velocity parameters for cartographic animated pictorial symbols that contribute to the preattentive perception of the
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The primary objective of this study is to assess how the motion of dynamic point symbols impacts preattentive processing on a map. Specifically, it involves identifying the motion velocity parameters for cartographic animated pictorial symbols that contribute to the preattentive perception of the target symbols. We created five pictorial symbols, each accompanied by a unique animation tailored to convey the meaning associated with each symbol. The animation dynamics of symbols on the administrative map were distributed across arithmetic, logarithmic, and exponential scales. Eye-tracking technology was utilized to explain the user’s visual attention. The key findings reveal that, although movement does not uniformly hold the same designation in cartographic communication, it could guide user attention to identify the value peaks in quantitative map visualization. Motion velocity enhances the salience of animated symbols, making them stand out, not only against static elements but also against other animated distractors. Additionally, motion distributions between symbol classes based on exponential or arithmetic scales were identified as the most successful. Nevertheless, certain types of motion, such as rotational, do not perform well with pictorial symbols, even on the most effective motion distribution scale.
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