ISPRS International Journal of Geo-Information

15 pages, 2090 KiB

Open AccessArticle

Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning

by Linghan Yao, Chao Gao, Yanqing Xu, Xinyue Zhang, Xiaoyi Wang and Yequan Hu

ISPRS Int. J. Geo-Inf. 2024, 13(10), 371; https://doi.org/10.3390/ijgi13100371 - 21 Oct 2024

Viewed by 908

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big [...] Read more.

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas. Full article

► Show Figures

Figure 1

20 pages, 31052 KiB

Open AccessArticle

Spatiotemporal Information, Near-Field Perception, and Service for Tourists by Distributed Camera and BeiDou Positioning System in Mountainous Scenic Areas

by Kuntao Shi, Changming Zhu, Junli Li, Xin Zhang, Fan Yang, Kun Zhang and Qian Shen

ISPRS Int. J. Geo-Inf. 2024, 13(10), 370; https://doi.org/10.3390/ijgi13100370 - 20 Oct 2024

Viewed by 727

Abstract

The collaborative use of camera near-field sensors for monitoring the number and status of tourists is a crucial aspect of smart scenic spot management. This paper proposes a near-field perception technical system that achieves dynamic and accurate detection of tourist targets in mountainous [...] Read more.

The collaborative use of camera near-field sensors for monitoring the number and status of tourists is a crucial aspect of smart scenic spot management. This paper proposes a near-field perception technical system that achieves dynamic and accurate detection of tourist targets in mountainous scenic areas, addressing the challenges of real-time passive perception and safety management of tourists. The technical framework involves the following steps: Firstly, real-time video stream signals are collected from multiple cameras to create a distributed perception network. Then, the YOLOX network model is enhanced with the CBAM module and ASFF method to improve the dynamic recognition of preliminary tourist targets in complex scenes. Additionally, the BYTE target dynamic tracking algorithm is employed to address the issue of target occlusion in mountainous scenic areas, thereby enhancing the accuracy of model detection. Finally, the video target monocular spatial positioning algorithm is utilized to determine the actual geographic location of tourists based on the image coordinates. The algorithm was deployed in the Tianmeng Scenic Area of Yimeng Mountain in Shandong Province, and the results demonstrate that this technical system effectively assists in accurately perceiving and spatially positioning tourists in mountainous scenic spots. The system demonstrates an overall accuracy in tourist perception of over 90%, with spatial positioning errors less than 1.0 m and a root mean square error (RMSE) of less than 1.14. This provides auxiliary technical support and effective data support for passive real-time dynamic precise perception and safety management of regional tourist targets in mountainous scenic areas with no/weak satellite navigation signals. Full article

► Show Figures

Figure 1

18 pages, 8484 KiB

Open AccessArticle

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

by Hong Zhu, Jian Meng, Jiaqi Yao and Nan Xu

ISPRS Int. J. Geo-Inf. 2024, 13(10), 369; https://doi.org/10.3390/ijgi13100369 - 18 Oct 2024

Viewed by 728

Abstract

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and [...] Read more.

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems. Full article

► Show Figures

Figure 1

19 pages, 4338 KiB

Open AccessArticle

Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets

by Elmer Magsino, Francis Miguel M. Espiritu and Kerwin D. Go

ISPRS Int. J. Geo-Inf. 2024, 13(10), 368; https://doi.org/10.3390/ijgi13100368 - 18 Oct 2024

Viewed by 633

Abstract

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as [...] Read more.

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

► Show Figures

Figure 1

16 pages, 9966 KiB

Open AccessArticle

Assessment of Fine-Scale Urban Heat Health Risk and Its Potential Driving Factors Based on Local Climate Zones in Shenzhen, China

by Riguga Su, Chaobin Yang, Zhibo Xu, Tingwen Luo and Lilong Yang

ISPRS Int. J. Geo-Inf. 2024, 13(10), 367; https://doi.org/10.3390/ijgi13100367 - 18 Oct 2024

Viewed by 690

Abstract

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore [...] Read more.

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore the potential driving factors. In this study, we estimated the potential HHRs based on the “hazard–exposure–vulnerability” framework by using multisource data, including the modified thermal–humidity index (MTHI), population density, and land cover. Then, the variations in the HHRs among different local climate zones (LCZs) at the fine spatial scale were analyzed in detail. Finally, we compared the different contributions of the LCZs and types of land cover to the HHRs and their three components by using multiple linear regression models. The results indicate that the spatial pattern of the HHRs was different from those of the individual components, and high-hazard regions do not mean high HHRs. There were huge variations in the HHRs among the different LCZs. The built-up LCZs typically had much higher HHRs than the natural ones, with compact LCZs facing the most severe risk. LCZ 6 (open low-rise buildings) had a relatively low HHR and should be paid more attention in future urban planning. Compared to the LCZs, the land covers better explained the variations in the HHR. In contrast, the LCZs better predicted the land surface temperatures. However, both the LCZs and land covers made only slight contributions to the heat exposure and vulnerability. Furthermore, the manmade buildings and impervious surface areas contributed much more to the HHR than the natural land covers. Therefore, the arrangement of the warming LCZs and land cover types is worthy of further investigation from the perspective of HHR mitigation. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

► Show Figures

Figure 1

8 pages, 261 KiB

Open AccessReply

Reply to Bektaş, S. Comment on “Ioannidou, S.; Pantazis, G. Helmert Transformation Problem. From Euler Angles Method to Quaternion Algebra. ISPRS Int. J. Geo-Inf. 2020, 9, 494”

by George Pantazis and Stefania Ioannidou

ISPRS Int. J. Geo-Inf. 2024, 13(10), 366; https://doi.org/10.3390/ijgi13100366 - 18 Oct 2024

Viewed by 398

Abstract

The comment disputes some of the inferences in the paper “Helmert Transformation Problem. From Euler Angles Method to Quaternion Algebra”, published in this journal. The key points in the dissent are the following: (1) The number of unknown parameters in the reverse transformation [...] Read more.

The comment disputes some of the inferences in the paper “Helmert Transformation Problem. From Euler Angles Method to Quaternion Algebra”, published in this journal. The key points in the dissent are the following: (1) The number of unknown parameters in the reverse transformation problem using dual quaternions. (2) The reliability of both data and the results. (3) There should be no differences between Euler angles and quaternion methods. Our response is summarized as follows: (1) The problem can be solved using either eight or nine unknown parameters. (2) All the data and results are real. (3) There should be differences between methods because of different calculations. Full article

17 pages, 5389 KiB

Open AccessArticle

Nonlinear and Threshold Effects on Station-Level Ridership: Insights from Disproportionate Weekday-to-Weekend Impacts

by Yanyan Gu and Mingxuan Dou

ISPRS Int. J. Geo-Inf. 2024, 13(10), 365; https://doi.org/10.3390/ijgi13100365 - 17 Oct 2024

Viewed by 595

Abstract

Station-level ridership is an important indicator for understanding the relationship between land use and rail transit, which is crucial for building more sustainable urban mobility systems. However, the nonlinear effects of the built environment on metro ridership, particularly concerning temporal heterogeneity, have not [...] Read more.

Station-level ridership is an important indicator for understanding the relationship between land use and rail transit, which is crucial for building more sustainable urban mobility systems. However, the nonlinear effects of the built environment on metro ridership, particularly concerning temporal heterogeneity, have not been adequately explained. To address this gap, this study proposes a versatile methodology that employs the eXtreme gradient boosting (XGBoost) tree to analyze the effects of factors on station-level ridership variations and compares these results with those of a multiple regression model. In contrast to conventional feature interpretation methods, this study utilized Shapley additive explanations (SHAP) to detail the nonlinear effects of each factor on station-level ridership across temporal dimensions (weekdays and weekends). Using Shanghai as a case study, the findings confirmed the presence of complex nonlinear and threshold effects of land-use, transportation, and station-type factors on station-level ridership in the association. The factor “Commercial POI” represents the most significant influence on ridership changes in both the weekday and weekend models; “Public Facility Station” plays a role in increasing passenger flow in the weekend model, but it shows the opposite effect on the change in ridership in the weekday model. This study highlights the importance of explainable machine learning methods for comprehending the nonlinear influences of various factors on station-level ridership. Full article

► Show Figures

Figure 1

15 pages, 7443 KiB

Open AccessArticle

A Semantically Enhanced Label Prediction Method for Imbalanced POI Data Category Distribution

by Hongwei Zhang, Qingyun Du, Shuai Zhang and Renfei Yang

ISPRS Int. J. Geo-Inf. 2024, 13(10), 364; https://doi.org/10.3390/ijgi13100364 - 17 Oct 2024

Viewed by 574

Abstract

POI data play an important role in various location-based services, including navigation, positioning, and local search applications. However, as cities rapidly develop, a substantial amount of new POI data are generated daily, often accompanied by issues with the quality of their labels. Therefore, [...] Read more.

POI data play an important role in various location-based services, including navigation, positioning, and local search applications. However, as cities rapidly develop, a substantial amount of new POI data are generated daily, often accompanied by issues with the quality of their labels. Therefore, there is an urgent need to implement intelligent inference and enhancement processing for POI data labels. Conventional neural network models primarily target balanced data distribution, but they fail to address the issue of imbalanced distribution of POI data labels in terms of quantity. Furthermore, most neural network classification models implicitly learn the semantic knowledge of different categories from training datasets, neglecting the explicit semantic information offered by natural language labels. Considering the above problems, several negative samples are introduced for each input to a positive class, thereby transforming the multi-classification task into a binary classification problem. Simultaneously, POI data labels are introduced to provide explicit semantic information, and the semantic relationship between POI data labels and their names is determined using cross-coding. Experiments demonstrate that the macro − F1 score for the test dataset, which contains 75 different categories of POI data, reaches 0.84. This result surpasses the performance of traditional methods, highlighting the effectiveness of the proposed method. Full article

► Show Figures

Figure 1

39 pages, 15881 KiB

Open AccessReview

Applications for Semantic 3D Streetspace Models and Their Requirements—A Review and Look at the Road Ahead

by Christof Beil and Thomas H. Kolbe

ISPRS Int. J. Geo-Inf. 2024, 13(10), 363; https://doi.org/10.3390/ijgi13100363 - 16 Oct 2024

Viewed by 1082

Abstract

In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models [...] Read more.

In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models of buildings or the terrain so far, the increasing availability of data on roads and other transportation infrastructure opened up a range of emerging use cases in the field of semantic 3D streetspace models. While there are already a number of implemented examples, there is also a potential for new use cases not yet established in the field of 3D city modeling, which benefit from detailed representations of roads and their environment. To ensure clarity in our discussions, we introduce an unambiguous distinction between the terms ‘application domain’, ‘use case’, ‘functionality’ and ‘software application’. Based on these definitions, use cases are categorized according to their primary application domain and discussed with respect to relevant literature and required functionalities. Furthermore, requirements of functionalities towards semantic 3D streetspace models are determined and evaluated in detail with regard to geometric, semantic, topological, temporal and visual aspects. This article aims to give an overview on use cases in the context of semantic 3D streetspace models and to present requirements of respective functionalities, in order to provide insight for researchers, municipalities, companies, data providers, mapping agencies and other stakeholders interested in creating and using a digital twin of the streetspace. Full article

► Show Figures

Figure 1

22 pages, 17993 KiB

Open AccessArticle

Research on Global Off-Road Path Planning Based on Improved A* Algorithm

by Zhihong Lv, Li Ni, Hongchun Peng, Kefa Zhou, Dequan Zhao, Guangjun Qu, Weiting Yuan, Yue Gao and Qing Zhang

ISPRS Int. J. Geo-Inf. 2024, 13(10), 362; https://doi.org/10.3390/ijgi13100362 - 16 Oct 2024

Viewed by 738

Abstract

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations [...] Read more.

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments. Full article

► Show Figures

Figure 1

18 pages, 6219 KiB

Open AccessArticle

A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems

by Peng Zhang, Wenzhou Wu, Cunjin Xue, Shaochen Shi and Fenzhen Su

ISPRS Int. J. Geo-Inf. 2024, 13(10), 361; https://doi.org/10.3390/ijgi13100361 - 14 Oct 2024

Viewed by 735

Abstract

As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling [...] Read more.

As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling the analysis of complex geographical phenomena and the resolution of comprehensive spatial problems. With the rapid advancement of artificial intelligence, deep neural network-based geographic simulation models (DNN-GSMs) have increasingly replaced traditional models, offering significant advantages in simulation accuracy and inference speed, and have become indispensable components in GISystems. However, existing integration methods do not adequately account for the specific characteristics of DNN-GSMs, such as their formats and input/output data types. To address this gap, we propose a novel tight integration framework for DNN-GSMs, comprising four key interfaces: the data representation interface, the model representation interface, the data conversion interface, and the model application interface. These interfaces are designed to describe spatial data, the simulation model, the adaptation between spatial data and the model, and the model’s application process within the GISystem, respectively. To validate the proposed method, we construct a spatial morphology simulation model based on CNN-LSTM, integrate it into a GISystem using the proposed interfaces, and conduct a series of predictive experiments on island morphology evolution. The results demonstrate the effectiveness of the proposed integration framework for DNN-GSMs. Full article

► Show Figures

Figure 1

24 pages, 12557 KiB

Open AccessArticle

A Study on a Spatiotemporal Entity-Based Event Data Model

by Mingming Wang, Jiangshui Zhang, Yibing Cao, Shenghui Li and Minjie Chen

ISPRS Int. J. Geo-Inf. 2024, 13(10), 360; https://doi.org/10.3390/ijgi13100360 - 14 Oct 2024

Viewed by 630

Abstract

An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly important for describing the real world. [...] Read more.

An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly important for describing the real world. By analyzing the representational categories of the existing event data models, the representation of existing event models was found to have different emphases and not be sufficiently balanced, and the universality and comprehensiveness need to be improved. Therefore, based on the advantages of the ontological event model in expressing semantic information and the advantages of the object-event-based spatiotemporal data model in expressing entity multidimensional characteristics and dynamic processes, a spatiotemporal entity-based event data model and the modeling method were designed to provide model support for event organization and processing. Additionally, the Long March and its important battles were selected as case studies to validate the proposed model. The validation shows that the proposed model performs well in terms of event dynamics, hierarchical structure, and complex interrelationships. Full article

(This article belongs to the Special Issue Unlocking the Power of Geospatial Data: Semantic Information Extraction, Ontology Engineering, and Deep Learning for Knowledge Discovery)

► Show Figures

Figure 1

2 pages, 184 KiB

Open AccessComment

Comment on Ioannidou, S.; Pantazis, G. Helmert Transformation Problem. From Euler Angles Method to Quaternion Algebra. ISPRS Int. J. Geo-Inf. 2020, 9, 494

by Sebahattin Bektaş

ISPRS Int. J. Geo-Inf. 2024, 13(10), 359; https://doi.org/10.3390/ijgi13100359 - 12 Oct 2024

Cited by 1 | Viewed by 448

Abstract

I have read the article by Ioannidou and Pantazis [...] Full article

20 pages, 14310 KiB

Open AccessArticle

Deep Learning Application for Biodiversity Conservation and Educational Tourism in Natural Reserves

by Marco Flórez, Oscar Becerra, Eduardo Carrillo, Manny Villa, Yuli Álvarez, Javier Suárez and Francisco Mendes

ISPRS Int. J. Geo-Inf. 2024, 13(10), 358; https://doi.org/10.3390/ijgi13100358 - 11 Oct 2024

Viewed by 928

Abstract

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these [...] Read more.

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these reserves accurately is challenging due to environmental variability and species similarities, complicating conservation efforts and educational tourism promotion. This study aims to create and assess a mobile application based on deep learning, called FloraBan, to autonomously identify plant species in natural reserves, enhancing biodiversity conservation and encouraging sustainable and educational tourism practices. The application employs the EfficientNet Lite4 model, trained on a comprehensive dataset of plant images taken in various field conditions. Designed to work offline, the application is particularly useful in remote areas. The model evaluation revealed an accuracy exceeding 90% in classifying plant images. FloraBan was effective under various lighting conditions and complex backgrounds, offering detailed information about each species, including scientific name, family, and conservation status. The ability to function without internet connectivity is a significant benefit, especially in isolated regions like natural reserves. FloraBan represents a notable improvement in the field of automated plant identification, supporting botanical research and efforts to preserve biodiversity in the Santurbán Moor. Additionally, it encourages educational and responsible tourism practices, which align with sustainability goals, providing a useful tool for both tourists and conservationists. Full article

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

► Show Figures

Figure 1

21 pages, 8247 KiB

Open AccessArticle

Comprehensive Assessment of Large-Scale Regional Fluvial Flood Exposure Using Public Datasets: A Case Study from China

by Xuanchi Chen, Bingjie Liang, Junhua Li, Yingchun Cai and Qiuhua Liang

ISPRS Int. J. Geo-Inf. 2024, 13(10), 357; https://doi.org/10.3390/ijgi13100357 - 8 Oct 2024

Viewed by 784

Abstract

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets [...] Read more.

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets offers a potential solution to these challenges. In this study, we obtained four key exposure indicators—population, built-up area (BA), road length (RL), and average gross domestic product (GDP)—and conducted an innovative analysis of their correlations both overall and locally. Utilising these indicators, we developed a comprehensive exposure index employing entropy-weighting and k-means clustering methods and assessed fluvial flood exposure across multiple return periods using fluvial flood maps. The datasets used for these indicators, as well as the flood maps, are primarily derived from remote sensing products. Our findings indicate a weak correlation between the various indicators at both global and local scales, underscoring the limitations of using singular indicators for a thorough exposure assessment. Notably, we observed a significant concentration of exposure and river flooding east of the Hu Line, particularly within the eastern coastal region. As flood return periods extended from 10 to 500 years, the extent of areas with flood depths exceeding 1 m expanded markedly, encompassing 2.24% of China’s territory. This expansion heightened flood risks across 15 administrative regions with varying exposure levels, particularly in Jiangsu (JS) and Shanghai (SH). This research provides a robust framework for understanding flood risk dynamics, advocating for resource allocation towards prevention and control in high-exposure, high-flood areas. Our findings establish a solid scientific foundation for effectively mitigating river flood risks in China and promoting sustainable development. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

► Show Figures

Figure 1

19 pages, 13819 KiB

Open AccessArticle

An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure

by Zhenglin Li, Zhanjie Zhao, Wujun Gao and Li Jiao

ISPRS Int. J. Geo-Inf. 2024, 13(10), 356; https://doi.org/10.3390/ijgi13100356 - 8 Oct 2024

Viewed by 734

Abstract

To tackle problems such as the destruction of topological structures and the loss of detailed features in the simplification of 3D building models, we propose a 3D building model simplification algorithm that considers detailed features and topological structures. Based on the edge collapse [...] Read more.

To tackle problems such as the destruction of topological structures and the loss of detailed features in the simplification of 3D building models, we propose a 3D building model simplification algorithm that considers detailed features and topological structures. Based on the edge collapse algorithm, the method defines the region formed by the first-order neighboring triangles of the endpoints of the edge to be collapsed as the simplification unit. It incorporates the centroid displacement of the simplification unit, significance level, and approximate curvature of the edge as influencing factors for the collapse cost to control the edge collapse sequence and preserve model details. Additionally, considering the unique properties of 3D building models, boundary edge detection and face overlay are added as constraints to maintain the model’s topological structure. The experimental results show that the algorithm is superior to the classic QEM algorithm in terms of preserving the topological structure and detailed features of the model. Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. Moreover, the advantages of this algorithm become more pronounced as the simplification rate increases. The research findings can be applied to the simplification of 3D building models. Full article

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

► Show Figures

Figure 1

21 pages, 4358 KiB

Open AccessArticle

Where and Why Travelers Visit? Classifying Coastal Tourism Activities Using Geotagged Image Content from Social Media Data

by Gang Sun Kim, Choong-Ki Kim and Woo-Kyun Lee

ISPRS Int. J. Geo-Inf. 2024, 13(10), 355; https://doi.org/10.3390/ijgi13100355 - 7 Oct 2024

Viewed by 1298

Abstract

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked [...] Read more.

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space. Full article

► Show Figures

Figure 1

19 pages, 12108 KiB

Open AccessArticle

WC-CP: A Bluetooth Low Energy Indoor Positioning Method Based on the Weighted Centroid of the Convex Polygon

by Jinjin Yan, Manyu Zhang, Jinquan Yang, Lyudmila Mihaylova, Weijie Yuan and You Li

ISPRS Int. J. Geo-Inf. 2024, 13(10), 354; https://doi.org/10.3390/ijgi13100354 - 6 Oct 2024

Viewed by 2970

Abstract

Indoor navigation has attracted significant attention from both academic and industrial perspectives. Indoor positioning is a critical component of indoor navigation. Several solutions or technologies have been proposed, such as Wi-Fi, UWB, and Bluetooth. Among them, Bluetooth Low Energy (BLE) is cost-effective, easily [...] Read more.

Indoor navigation has attracted significant attention from both academic and industrial perspectives. Indoor positioning is a critical component of indoor navigation. Several solutions or technologies have been proposed, such as Wi-Fi, UWB, and Bluetooth. Among them, Bluetooth Low Energy (BLE) is cost-effective, easily deployable, flexible, and efficient. This paper focuses on indoor positioning solely based on BLE. Motivated by two observations, namely, that (i) involving more anchor nodes can enhance positioning accuracy, and that (ii) narrowing the area for unknown location determination can also lead to improved accuracy, a new distance-based method, the Weighted Centroid of the Convex Polygon (WC-CP), is proposed. While it is generally acknowledged that incorporating more anchor nodes can enhance indoor positioning performance, the current state of the art lacks a robust methodology for selecting and utilizing these nodes. The WC-CP approach addresses this gap by introducing a systematic and efficient method for identifying and employing the most suitable anchor nodes. By avoiding nodes that could potentially introduce significant errors or lead to incorrect localization, our method ensures more accurate and reliable indoor positioning. The efficacy of WC-CP is demonstrated in an indoor environment, achieving an RMSE of 1.35 m. This result shows significant improvements over three state-of-the-art approaches, about 34.15% better than LSBM, 32.50% better than TWCBM, and 30.05% better than ITWCBM. These findings underscore the potential of WC-CP for enhanced accuracy and reliability in indoor positioning based on BLE. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

► Show Figures

Figure 1

25 pages, 11498 KiB

Open AccessArticle

Spatially Varying Effect Mechanism of Intermodal Connection on Metro Ridership: Evidence from a Polycentric Megacity with Multilevel Ring Roads

by Bozhezi Peng, Tao Wang, Yi Zhang, Chaoyang Li and Chunxia Lu

ISPRS Int. J. Geo-Inf. 2024, 13(10), 353; https://doi.org/10.3390/ijgi13100353 - 4 Oct 2024

Viewed by 649

Abstract

Understanding the spatially varying effect mechanism of intermodal connection on metro ridership helps policymakers develop differentiated interventions to promote metro usage, especially for megacities with multiple city sub-centers and ring roads. Using multiple datasets in Shanghai, this study combines Light Gradient Boosting Machine [...] Read more.

Understanding the spatially varying effect mechanism of intermodal connection on metro ridership helps policymakers develop differentiated interventions to promote metro usage, especially for megacities with multiple city sub-centers and ring roads. Using multiple datasets in Shanghai, this study combines Light Gradient Boosting Machine (LightGBM) with Shapley additive explanations (SHAP) to explore these effects with the consideration of the built environment and metro network topology. Results show that the collective impacts of intermodal connection are positive, not only within the main city but also alongside the main commuting corridors, while negative effects occur in the peripheral area. Specifically, bike sharing trips increase metro ridership within the inner ring of the city, while bus services lower metro usage at stations alongside the elevated ring roads. Parking facilities enable metro usage at city sub-centers, and the small pedestrian catchment area increases metro riders alongside the main commuting corridors. Empirical findings help policymakers understand the effect mechanism of intermodal connection for stations in different regions and prioritize customized planning strategies. Full article

► Show Figures

Figure 1

26 pages, 12142 KiB

Open AccessArticle

A Study of the Evolution of Haze Microblog Concerns Based on a Co-Word Network Analysis

by Haiyue Lu, Xiaoping Rui, Runkui Li, Guangyuan Zhang, Ziqian Zhang and Mingguang Wu

ISPRS Int. J. Geo-Inf. 2024, 13(10), 352; https://doi.org/10.3390/ijgi13100352 - 4 Oct 2024

Viewed by 668

Abstract

Haze is a phenomenon caused by excessive PM2.5 (air-borne particulate matter having a diameter of fewer than 2.5 μm) and other pollutants and results from the interaction between specific climatic conditions and human activities. It significantly impacts human health, transportation, and the natural [...] Read more.

Haze is a phenomenon caused by excessive PM2.5 (air-borne particulate matter having a diameter of fewer than 2.5 μm) and other pollutants and results from the interaction between specific climatic conditions and human activities. It significantly impacts human health, transportation, and the natural environment and has aroused widespread concern. However, the influence of haze on human mental health, being hidden and indirect, is often overlooked. When haze pollution occurs, people express their feelings and concerns about haze events on media such as Weibo. At present, few studies focus on haze public opinion, as well as the changing trends in people’s discussion of haze since its emergence, which is of great significance for haze response and resource management. Based on the perspective of topic analysis, this study explores the psychological impact of haze on people by exploring the feelings of netizens in haze public opinion and investigates the evolution of people’s concerns based on long-term public opinion data. In this study, seven typical provinces and cities in China with severe haze pollution were selected as the research area. Based on data on the “haze” theme from Weibo from 2013 to 2019, first, the microblog posts were preprocessed, and the keyword co-word network was constructed. Second, the Louvain algorithm was used to detect the topic community. Based on this, the cosine similarity was calculated to realize the temporal evolution analysis of topics. The results show that with the development and change in haze pollution, the content and intensity of the topics netizens pay attention to have changed, including five types: merger, split, survival, transformation, and rebirth/extinction. People’s attention to haze shows obvious spatial differences, and it is related to the degree of haze pollution, which is bipolar. Areas with severe haze tend to pay more attention to haze itself and its influence, while areas with light haze pay more attention to haze control. The research results can provide valuable insights for governments and relevant departments in guiding public opinion and resource allocation. Full article

► Show Figures

Figure 1

14 pages, 8341 KiB

Open AccessArticle

Detecting Urban Traffic Anomalies Using Traffic-Monitoring Data

by Yunkun Mao, Yilin Shi and Binbin Lu

ISPRS Int. J. Geo-Inf. 2024, 13(10), 351; https://doi.org/10.3390/ijgi13100351 - 4 Oct 2024

Viewed by 1624

Abstract

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks [...] Read more.

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to effectively achieve long-term anomaly detection at the city level. First, we converted traffic track points into an image representation, which enables spatial correlation between traffic flow and roads and correlations between traffic flow and roads, as well as the surrounding environment, to be captured. Second, the model utilizes convolution kernels of different sizes to extract spatial features at road-, regional-, and city-level scales while incorporating the temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic-control strategies. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)

► Show Figures

Figure 1

16 pages, 15468 KiB

Open AccessArticle

Contextual Enrichment of Crowds from Mobile Phone Data through Multimodal Geo-Social Media Analysis

by Klára Honzák, Sebastian Schmidt, Bernd Resch and Philipp Ruthensteiner

ISPRS Int. J. Geo-Inf. 2024, 13(10), 350; https://doi.org/10.3390/ijgi13100350 - 3 Oct 2024

Viewed by 899

Abstract

The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse [...] Read more.

The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary nature and potential for emergency management, this paper introduces a novel methodology for improving situational awareness with the focus on urban events. For crowd detection, a spatial hot spot analysis of mobile phone data is used. The analysis of geo-social media data involves building spatio-temporal topic-sentiment clusters of posts. The results of the spatio-temporal contextual enrichment include unusual crowds associated with topics and sentiments derived from the analyzed geo-social media data. This methodology is demonstrated using the case study of the Vienna Pride. The results show how crowds change over time in terms of their location, size, topics discussed, and sentiments. Full article

► Show Figures

Figure 1

24 pages, 12316 KiB

Open AccessArticle

A Method for Constructing an Urban Waterlogging Emergency Knowledge Graph Based on Spatiotemporal Processes

by Wei Mao, Jie Shen, Qian Su, Sihu Liu, Saied Pirasteh and Kunihiro Ishii

ISPRS Int. J. Geo-Inf. 2024, 13(10), 349; https://doi.org/10.3390/ijgi13100349 - 3 Oct 2024

Viewed by 704

Abstract

Urban waterlogging is one of the major “diseases” faced by cities, posing a great challenge to the healthy and sustainable development of cities. The traditional geographic knowledge graph struggles to capture dynamic changes in urban waterlogging over time. Therefore, the objective of this [...] Read more.

Urban waterlogging is one of the major “diseases” faced by cities, posing a great challenge to the healthy and sustainable development of cities. The traditional geographic knowledge graph struggles to capture dynamic changes in urban waterlogging over time. Therefore, the objective of this study is to analyze the time, events, properties, geographic objects, and activities associated with urban waterlogging emergency responses from the geographic spatial and temporal processes perspective and to construct an urban waterlogging emergency knowledge graph by combining top-down and bottom-up approaches. We propose a conceptual model of urban waterlogging emergency response ontology based on spatiotemporal processes by analyzing the basic laws and influencing factors of urban waterlogging occurrence and development. Secondly, we describe the construction process of the urban waterlogging emergency response knowledge graph from knowledge extraction, knowledge fusion, and knowledge storage. Finally, the knowledge graph was visualized using 159 urban waterlogging events in China from 2020–2022, with a quality assessment indicating 81% correctness, 65.5% completeness, and 95% data conciseness. The results show that this method can effectively express the spatiotemporal process of an urban waterlogging emergency response and can provide a reference for the spatiotemporal modeling of the knowledge graph. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

► Show Figures

Figure 1

36 pages, 13506 KiB

Open AccessArticle

ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models

by Ali Mansourian and Rachid Oucheikh

ISPRS Int. J. Geo-Inf. 2024, 13(10), 348; https://doi.org/10.3390/ijgi13100348 - 1 Oct 2024

Viewed by 3503

Abstract

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to [...] Read more.

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. Full article

► Show Figures

Figure 1

27 pages, 6999 KiB

Open AccessArticle

Improved Road Extraction Models through Semi-Supervised Learning with ACCT

by Hao Yu, Shihong Du, Zhenshan Tan, Xiuyuan Zhang and Zhijiang Li

ISPRS Int. J. Geo-Inf. 2024, 13(10), 347; https://doi.org/10.3390/ijgi13100347 - 29 Sep 2024

Viewed by 725

Abstract

Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new [...] Read more.

Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios where labeled samples are not available. In this paper, our focus diverges from the typical quest to pinpoint the optimal road extraction model or evaluate generalization prowess across models. Instead, we propose a method called Asymmetric Consistent Co-Training (ACCT) to train existing road extraction models faster and make them perform better in new scenarios lacking samples. ACCT uses two models with different structures and a supervision module to enhance accuracy through mutual learning. Labeled and unlabeled images are processed by both models to generate road maps from different perspectives. The supervision module ensures consistency between predictions by computing losses based on labeling status. ACCT iteratively adjusts parameters using unlabeled data, improving generalization. Empirical evaluations show that ACCT improves IoU by 2.79% to 10.26% using only 1/8 of the labeled data compared to fully supervised methods. It also reduces parameters by over 49% compared to state-of-the-art semi-supervised methods while maintaining similar accuracy. These results highlight the potential of leveraging large amounts of unlabeled data to enhance road extraction models as data acquisition technology advances. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)

► Show Figures

Figure 1

26 pages, 6402 KiB

Open AccessArticle

SGIR-Tree: Integrating R-Tree Spatial Indexing as Subgraphs in Graph Database Management Systems

by Juyoung Kim, Seoyoung Hong, Seungchan Jeong, Seula Park and Kiyun Yu

ISPRS Int. J. Geo-Inf. 2024, 13(10), 346; https://doi.org/10.3390/ijgi13100346 - 27 Sep 2024

Viewed by 687

Abstract

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index [...] Read more.

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs. Full article

► Show Figures

Figure 1

18 pages, 9353 KiB

Open AccessArticle

Sky-Scanning for Energy: Unveiling Rural Electricity Consumption Patterns through Satellite Imagery’s Convolutional Features

by Yaofu Huang, Weipan Xu, Dongsheng Chen, Qiumeng Li, Weihuan Deng and Xun Li

ISPRS Int. J. Geo-Inf. 2024, 13(10), 345; https://doi.org/10.3390/ijgi13100345 - 26 Sep 2024

Viewed by 702

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

33 pages, 24105 KiB

Open AccessArticle

Pre-Dam Vltava River Valley—A Case Study of 3D Visualization of Large-Scale GIS Datasets in Unreal Engine

by Michal Janovský

ISPRS Int. J. Geo-Inf. 2024, 13(10), 344; https://doi.org/10.3390/ijgi13100344 - 26 Sep 2024

Viewed by 761

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

14 pages, 2387 KiB

Open AccessReview

The Status of the Implementation of the Building Information Modeling Mandate in Poland: A Literature Review

by 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

Viewed by 1017

Abstract

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 [...] Read more.

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. Full article

► Show Figures

Figure 1

18 pages, 3496 KiB

Open AccessArticle

Analysis of Guidance Signage Systems from a Complex Network Theory Perspective: A Case Study in Subway Stations

by 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

Viewed by 575

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. [...] Read more.

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. Full article

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

► Show Figures

Figure 1

Journal Menu

Journal Browser

ISPRS Int. J. Geo-Inf., Volume 13, Issue 10 (October 2024) – 32 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI