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Applied Sciences
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Machine Learning in Geographical Information Systems (GISs)
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Machine Learning in Geographical Information Systems (GISs)

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

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 1024

Special Issue Editors

Dr. Chih-Yu Liu

E-Mail Website
Guest Editor
Department of Civil Engineering, National Central University, Taoyuan, Taiwan
Interests: hydrogeology; groundwater; landslides modeling; artificial intelligence in geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheng-Yu Ku

E-Mail Website
Guest Editor
Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung, Taiwan
Interests: numerical methods; geotechnical engineering; groundwater modeling; groundwater; landslides modeling; slope stability; engineering geology
Special Issues, Collections and Topics in MDPI journals
Dr. Yu-Jia Chiu

E-Mail Website
Guest Editor
Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan
Interests: disaster management and practices; hydraulic engineering; watershed management; water resource conservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue "Machine Learning in Geographical Information Systems (GISs)" focuses on the integration of machine learning techniques within GIS to address various challenges related to interdisciplinary fields. Machine Learning algorithms are applied to analyze big data sets within GIS frameworks, enabling enhanced prediction and assessment capabilities. Various Machine Learning models, including supervised and unsupervised learning, reinforcement learning, and deep learning, illustrate adaptability to diverse GIS applications such as land cover classification, remote sensing, spatial clustering, natural hazards, and predictive modeling. The synergy between Machine Learning and GIS offers novel approaches to handling complex spatial datasets, enhancing the efficiency and accuracy of geospatial analyses.

This Special Issue aims to encompass innovative methodologies and applications utilizing Machine Learning to explore case studies and real-world applications of Machine Learning in GIS.

Dr. Chih-Yu Liu
Prof. Dr. Cheng-Yu Ku
Dr. Yu-Jia Chiu
Guest Editors

Manuscript Submission Information

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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

  • machine learning
  • geographical information systems
  • predictive modeling
  • big data
  • geospatial analysis

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Published Papers (1 paper)

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Research

18 pages, 2269 KiB  
Article
The Use of Language Models to Support the Development of Cartographic Descriptions of a Building’s Interior
by Krzysztof Lipka, Dariusz Gotlib and Kamil Choromański
Appl. Sci. 2024, 14(20), 9343; https://doi.org/10.3390/app14209343 - 14 Oct 2024
Viewed by 659
Abstract
The development and popularization of navigation applications are increasing expectations for their quality and functionality. Users need continuous navigation not only outdoors, but also indoors. In this case, however, the perception of space and movement is somewhat different than it is outside. One [...] Read more.
The development and popularization of navigation applications are increasing expectations for their quality and functionality. Users need continuous navigation not only outdoors, but also indoors. In this case, however, the perception of space and movement is somewhat different than it is outside. One potential method of meeting this need may be the use of so-called geo-descriptions—multi-level textual descriptions relating to a point, line or area in a building. Currently, geo-descriptions are created manually. However, this is a rather time-consuming and complex process. Therefore, this study undertook to automate this process as much as possible. The study uses classical methods of spatial analysis from GIS systems and text generation methods based on artificial intelligence (AI) techniques, i.e., large language models (LLM). In this article, special attention will be paid to the second group of methods. As part of the first stage of the research, which was aimed at testing the proposed concept, the possibility of LLMs creating a natural description of space based on a list of features of a given place obtained by other methods (input parameters for AI), such as coordinates and categories of rooms around a given point, etc., was tested. The focus is on interior spaces and a few selected features of a particular place. In the next stages, it is planned to extend the research to spaces outside buildings. In addition, artificial intelligence can be used to provide the input parameters mentioned above. Full article
(This article belongs to the Special Issue Machine Learning in Geographical Information Systems (GISs))
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