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Intelligent GIS Application for Spatial Data Analysis
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Intelligent GIS Application for Spatial Data Analysis

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 4336

Special Issue Editors

Prof. Dr. Zhanlong Chen

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: geocomputing and spatial analysis; GIS applications; intelligent mining of spatiotemporal big data; land use data analysis
Dr. Yongyang Xu

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: land use; GIS applications; artificial intelligent applications in GIS and RS
Dr. Dezhi Wang

E-Mail Website
Guest Editor
Wuhan Botanical Garden, Chinese Academy of Sciences, No. 201 Jiufeng 1st Road, Wuhan, China
Interests: remote sensing; ecology; mangrove; watershed; land use
Special Issues, Collections and Topics in MDPI journals
Dr. Yihui Xiong

E-Mail Website
Guest Editor
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
Interests: GIS-based mineral prospectivity mapping

Special Issue Information

Dear Colleagues,

Geographic Information System systems are important tools for managing natural and other resources at all scales ranging from local to global. GIS has different applications, and technological advancements have significantly enhanced GIS data, specifically how it can be used and what can be achieved as a result. Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial Intelligence GIS (AI GIS) technology is currently an important research direction.

Artificial Intelligence made a major footstep that has been arising independently in GIS. During the past ten years, there is significant convergence in GIS and AI. Geo-intelligence refers to the general term for geospatial visualization, analysis, decision-making, design, and control based on GIS, remote sensing, and satellite positioning technologies. Combining AI and GIS to process the big special data for prediction or solving complex problems is important for research.

The main themes of this Special Issue include status analysis using AI and GIS technology in order to develop methods of spatial analysis or geographical linkages to applied interdisciplinary research, innovative methods of research and data integration, as well as the processing and visualization of research results using the tools of spatial analysis and cartography. The aim of this Special Issue is to present original research articles and review work related to AI and GIS applications. In this Special Issue, we seek original work focused on using innovative AI and GIS methods to address urban climate issues. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Land use changes
  • Landscape
  • Land use-related environmental sustainability
  • Spatiotemporal dynamic of land use/land cover
  • Land cover
  • Data matching
  • Special pattern recognition
  • Intelligent information mining
  • GIS-based mineral prospectivity mapping
  • Mineral prospectivity mapping by machine (deep) learning
  • Geochemical anomaly mapping by machine (deep) learning
  • GIS modelling
  • Remote sensing and GIS
  • Spatial analysis
  • Cartography
  • Map generalization
  • GIS based landslide susceptibility mapping
  • Terrain analysis
  • DEM application
  • Super-resolution mapping
  • Assessment of spatial data
  • Space-time trajectory analysis

We look forward to receiving your contributions.

Prof. Dr. zhanlong Chen
Dr. Yongyang Xu  
Dr. Dezhi Wang
Dr. Yihui Xiong
Guest Editors

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. Sustainability 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 application
  • Artificial Intelligence
  • GIS modelling
  • land use

Published Papers (3 papers)

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Research

22 pages, 12766 KiB  
Article
Semi-Supervised Building Detection from High-Resolution Remote Sensing Imagery
by Daoyuan Zheng, Jianing Kang, Kaishun Wu, Yuting Feng, Han Guo, Xiaoyun Zheng, Shengwen Li and Fang Fang
Sustainability 2023, 15(15), 11789; https://doi.org/10.3390/su151511789 - 1 Aug 2023
Viewed by 975
Abstract
Urban building information reflects the status and trends of a region’s development and is essential for urban sustainability. Detection of buildings from high-resolution (HR) remote sensing images (RSIs) provides a practical approach for quickly acquiring building information. Mainstream building detection methods are based [...] Read more.
Urban building information reflects the status and trends of a region’s development and is essential for urban sustainability. Detection of buildings from high-resolution (HR) remote sensing images (RSIs) provides a practical approach for quickly acquiring building information. Mainstream building detection methods are based on fully supervised deep learning networks, which require a large number of labeled RSIs. In practice, manually labeling building instances in RSIs is labor-intensive and time-consuming. This study introduces semi-supervised deep learning techniques for building detection and proposes a semi-supervised building detection framework to alleviate this problem. Specifically, the framework is based on teacher–student mutual learning and consists of two key modules: the color and Gaussian augmentation (CGA) module and the consistency learning (CL) module. The CGA module is designed to enrich the diversity of building features and the quantity of labeled images for better training of an object detector. The CL module derives a novel consistency loss by imposing consistency of predictions from augmented unlabeled images to enhance the detection ability on the unlabeled RSIs. The experimental results on three challenging datasets show that the proposed framework outperforms state-of-the-art building detection methods and semi-supervised object detection methods. This study develops a new approach for optimizing the building detection task and a methodological reference for the various object detection tasks on RSIs. Full article
(This article belongs to the Special Issue Intelligent GIS Application for Spatial Data Analysis)
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15 pages, 4468 KiB  
Article
Spatial Differentiation and Driving Factors of Traditional Villages in Jiangsu Province
by Qinghai Zhang and Jiabei Wang
Sustainability 2023, 15(14), 11448; https://doi.org/10.3390/su151411448 - 24 Jul 2023
Cited by 2 | Viewed by 1184
Abstract
Jiangsu Province, situated in the Yangtze River basin, has rich traditional village resources and a prominent position in economic development and cultural integration. This study focuses on the analysis of the variation distribution pattern of traditional villages in Jiangsu Province using six batches [...] Read more.
Jiangsu Province, situated in the Yangtze River basin, has rich traditional village resources and a prominent position in economic development and cultural integration. This study focuses on the analysis of the variation distribution pattern of traditional villages in Jiangsu Province using six batches of traditional village directories with data until 2023 as research samples. By employing ANN, Voronoi graph analysis, and Moran’s I index, the researchers determined the spatial distribution characteristics of rural settlements. Additionally, kernel density and spatial autocorrelation techniques were used to further examine the spatial distribution patterns, and geographic detector detection was introduced. The results showed the following: (1) The spatial distribution of traditional village settlements in Jiangsu Province showed a significant clustering distribution that is mainly concentrated in central Jiangsu Province. (2) The driving factors reflected a strong symbiotic relationship of “air–water–soil–man”. The spatial distribution of traditional villages was mainly driven by the annual mean temperature and soil type. The interaction between factors was dominated by the enhancement relationship between the two factors. (3) According to the detection results of risk areas in the region, the average annual temperature was 17~17.6 °C, the annual precipitation was 133.0~145.7 billion m3, the average annual wind speed was 0.549~0.565 m/s, the GDP was 85,100~204,000 CNY/km−2, and the population density was 2.32~3.91 thousand/km−2. Arable land was the main type of area and was conducive to the gathering of traditional villages. The preservation of rural settlements should take into account the complex and diverse factors that affect their distribution. Additionally, it is crucial to tailor protection strategies to specific local conditions and conduct flexible research. Full article
(This article belongs to the Special Issue Intelligent GIS Application for Spatial Data Analysis)
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15 pages, 3869 KiB  
Article
CA-BASNet: A Building Extraction Network in High Spatial Resolution Remote Sensing Images
by Liang Huang, Juanjuan Zhu, Mulan Qiu, Xiaoxiang Li and Shasha Zhu
Sustainability 2022, 14(18), 11633; https://doi.org/10.3390/su141811633 - 16 Sep 2022
Cited by 4 | Viewed by 1337
Abstract
Aiming at the problems of holes, misclassification, and rough edge segmentation in building extraction results from high spatial remote sensing images, a coordinate attention mechanism fusion network based on the BASNet network (CA-BASNet) is designed for building extraction in high spatial remote sensing [...] Read more.
Aiming at the problems of holes, misclassification, and rough edge segmentation in building extraction results from high spatial remote sensing images, a coordinate attention mechanism fusion network based on the BASNet network (CA-BASNet) is designed for building extraction in high spatial remote sensing images. Firstly, the deeply supervised encoder–decoder network was used to create a rough extract of buildings; secondly, to make the network pay more attention to learning building edge features, the mixed loss function composed of binary cross entropy, structural similarity and intersection-over-union was introduced into the network training process; finally, the residual optimization module of fusion coordinate attention mechanism was used for post-processing to realize the fine extraction of buildings from high spatial resolution remote sensing images. Experiments on the WHU building dataset show that the proposed network can achieve mIoU of 93.43%, mPA of 95.86%, recall of 98.79%, precision of 90.13% and F1 of 91.35%. Compared with the existing semantic segmentation networks, such as PSPNet, SegNet, DeepLapV3, SE-UNet, and UNet++, the accuracy of the proposed network and the integrity of object edge segmentation are significantly improved, which proves the effectiveness of the proposed network. Full article
(This article belongs to the Special Issue Intelligent GIS Application for Spatial Data Analysis)
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