Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)

A Review Geospatial Artificial Intelligence (Geo-AI): Implementation of Machine Learning on Urban Planning

Authors
Cholid Fauzi1, *
1Teknik Informatika, Politeknik Negeri Banddung, Bandung Barat, Indonesia
*Corresponding author. Email: cholid.fauzi@polban.ac.id
Corresponding Author
Cholid Fauzi
Available Online 17 February 2024.
DOI
10.2991/978-94-6463-364-1_30How to use a DOI?
Keywords
SLR; GIS; Geospatial Artificial Intelligence; Geo- AI; Machine Learning
Abstract

Geospatial Artificial Intelligence (Geo-AI) is an interesting topic in its development and application in our lives. One of them is spatial planning which contributes to the economic and social development of a region or country. Spatial data is the main thing in this research, by maximizing the effectiveness of land use spatial data on the area as upstream data and developing GIS-based tourism applications to display the results of analysis and predictions of tourist objects automatically. On the other hand, to maximize tourism revenue, the government plans urban areas for both spatial and land use and makes a lot of spatial data based on geographical and environmental conditions. This study will analyze the benefits of Geo-AI in urban planning and the tourism sector. The method used in this study is the Systematic Literature Review (SLR) where the search for the required articles comes from electronic databases obtained using the NVivo software with article sources from Publish or Perish. This study discusses the main steps for the analysis of geospatial data that have been successful in the main areas, namely the development of applications and models including visualization. By treating issues in geospatial artificial intelligence, the overall aim of the research is to improve the quality of life for Indonesia's growing urban population. The results of the study with the systematic literature review of Geo-AI found a gap in the research implementation of the machine learning model used where there were 5 models that were compared and relevant to the geospatial dataset displayed in the form of a literature review matrix. Visually included in the relevant keywords in the bibliometric analysis. Interdisciplinary results that are developing and are urgently needed by both government and private stakeholders in the development of smart cities to prepare spatial planning in urban areas and strategies in optimizing technology in the field of spatial planning in implementing systems based on Geo-AI.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
Series
Advances in Engineering Research
Publication Date
17 February 2024
ISBN
10.2991/978-94-6463-364-1_30
ISSN
2352-5401
DOI
10.2991/978-94-6463-364-1_30How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Cholid Fauzi
PY  - 2024
DA  - 2024/02/17
TI  - A Review Geospatial Artificial Intelligence (Geo-AI): Implementation of Machine Learning on Urban Planning
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 311
EP  - 329
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_30
DO  - 10.2991/978-94-6463-364-1_30
ID  - Fauzi2024
ER  -