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Applied Sciences
Special Issues
Emerging GIS Technologies and Their Applications
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Emerging GIS Technologies and Their Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1776

Special Issue Editors

Prof. Dr. Ferdinando Di Martino

E-Mail Website
Guest Editor
Department of Architecture, University of Naples Federico II | UNINA, Napoli, Italy
Interests: fuzzy relations and fuzzy transform in image and data analysis; fuzzy intelligent systems in data and spatial data analysis; fuzzy clustering in spatial analysis and hot spot analysis fuzzy; fuzzy clustering in image segmentation; GIS; fuzzy reasoning in GIS environments
Special Issues, Collections and Topics in MDPI journals
Dr. Barbara Cardone

E-Mail Website
Guest Editor Assistant
Department of Architecture, University of Naples Federico II | UNINA, Napoli, Italy
Interests: GIS; fuzzy intelligent systems in data and spatial data analysis; fuzzy clustering in spatial analysis and hot spot analysis; fuzzy reasoning in GIS environments

Special Issue Information

Dear Colleagues,

With the technological evolution and increasing availability of spatial big data, new GIS-based approaches are becoming significant, bringing together artificial and computational intelligence to solve problems with GIS systems. Particular examples of this include environmental and climatic risk forecasting systems in urban and territorial contexts; evaluation approaches for resilient interventions; simulation models of the impacts of extreme climatic phenomena; and sentiment analysis and emotion detection methods for the evaluation of the likings and emotions of citizens and tourists connected to the landscape, infrastructures, and services in the area. Relevant areas of emerging GIS technologies include, but are not limited to, new spatial data mining algorithms applied to satellite and real-time spatial data, cluster-based approaches to hot spot analysis, deep learning models for soil classification and the detection of building typologies, methods of forecasting the impact of extreme climatic phenomena on urban settlements, and methods of image segmentation and edge detection for the recognition of spatial patterns.

This Special Issue will publish high-quality, original research papers on topics related to emerging GIS technologies in the fields of computer science and computational intelligence, such as spatial data mining for the treatment of spatial big data; machine learning and deep learning models for the classification of soils and built typologies; forecasting models for assessing climate and environmental risks and predicting disasters; sentiment analysis and emotional detection approaches for evaluating the moods of citizens and tourists; multi-criteria analysis models for evaluating the resilient efficiency of adaptation interventions in urban settlements.

Prof. Dr. Ferdinando Di Martino
Guest Editor

Dr. Barbara Cardone
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GIS
  • geospatial data
  • Geo-AI
  • GIS-based computational intelligence
  • spatial data mining

Published Papers (3 papers)

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Research

17 pages, 28897 KiB  
Article
Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
by Haiyang Xu, Huaxing Lu and Shichen Liu
Appl. Sci. 2024, 14(5), 2133; https://doi.org/10.3390/app14052133 - 04 Mar 2024
Viewed by 579
Abstract
The Sky View Factor (SVF) stands as a critical metric for quantitatively assessing urban spatial morphology and its estimation method based on Street View Imagery (SVI) has gained significant attention in recent years. However, most existing Street View-based methods prove inefficient and constrained [...] Read more.
The Sky View Factor (SVF) stands as a critical metric for quantitatively assessing urban spatial morphology and its estimation method based on Street View Imagery (SVI) has gained significant attention in recent years. However, most existing Street View-based methods prove inefficient and constrained in SVI dataset collection. These approaches often fall short in capturing detailed visual areas of the sky, and do not meet the requirements for handling large areas. Therefore, an online method for the rapid estimation of a large area SVF using SVI is presented in this study. The approach has been integrated into a WebGIS tool called BMapSVF, which refines the extent of the visible sky and allows for instant estimation of the SVF at observation points. In this paper, an empirical case study is carried out in the street canyons of the Qinhuai District of Nanjing to illustrate the effectiveness of the method. To validate the accuracy of the refined SVF extraction method, we employ both the SVI method based on BMapSVF and the simulation method founded on 3D urban building models. The results demonstrate an acceptable level of refinement accuracy in the test area. Full article
(This article belongs to the Special Issue Emerging GIS Technologies and Their Applications)
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37 pages, 20496 KiB  
Article
An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks
by Changmin Liu, Yang Wang, Weikang Li, Liufeng Tao, Sheng Hu and Mengqi Hao
Appl. Sci. 2024, 14(5), 2108; https://doi.org/10.3390/app14052108 - 03 Mar 2024
Viewed by 576
Abstract
Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often [...] Read more.
Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation. Full article
(This article belongs to the Special Issue Emerging GIS Technologies and Their Applications)
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14 pages, 3260 KiB  
Article
Multicore Parallelized Spatial Overlay Analysis Algorithm Using Vector Polygon Shape Complexity Index Optimization
by Junfu Fan, Jiwei Zuo, Guangwei Sun, Zongwen Shi, Yu Gao and Yi Zhang
Appl. Sci. 2024, 14(5), 2006; https://doi.org/10.3390/app14052006 - 28 Feb 2024
Viewed by 419
Abstract
As core algorithms of geographic computing, overlay analysis algorithms typically have computation-intensive and data-intensive characteristics. It is highly important to optimize overlay analysis algorithms by parallelizing the vector polygons after reasonable data division. To address the problem of unbalanced data partitioning in the [...] Read more.
As core algorithms of geographic computing, overlay analysis algorithms typically have computation-intensive and data-intensive characteristics. It is highly important to optimize overlay analysis algorithms by parallelizing the vector polygons after reasonable data division. To address the problem of unbalanced data partitioning in the task decomposition process for parallel polygon overlay analysis and calculation, this paper presents a data partitioning method based on shape complexity index optimization, which achieves data equalization among multicore parallel computing tasks. Taking the intersection operator and difference operator of the Vatti algorithm as examples, six polygon shape indexes are selected to construct the shape complexity model, and the vector data are divided in accordance with the calculated shape complexity results. Finally, multicore parallelism is achieved based on OpenMP. The experimental results show that when a data set with a large amount of data is used, the effect of the multicore parallel execution of the Vatti algorithm’s intersection operator and difference operator based on shape complexity division is clearly improved. With 16 threads, compared with the serial algorithm, speedups of 29 times and 32 times can be obtained. Compared with the traditional multicore parallel algorithm based on polygon number division, the speed can be improved by 33% and 29%, and the load balancing index is reduced. For a data set with a small amount of data, the acceleration effect of this method is similar to that of traditional methods involving multicore parallelism. Full article
(This article belongs to the Special Issue Emerging GIS Technologies and Their Applications)
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