Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/29375
ชื่อเรื่อง: Al-Based Remoted Sensing Model for Sustainable Landcover Mapping and Monitoring in Smart City Context
ผู้แต่ง: Sitthi A.
Hassan S.-U.
Keywords: Artificial intelligence
Land use
Landcover
Machine learning
Remote sensing
Smart city
Sustainable development
วันที่เผยแพร่: 2023
สำนักพิมพ์: Springer Science and Business Media B.V.
บทคัดย่อ: In recent years, numerous attempts have been documented in the smart city context to make cities and human settlements more inclusive, safe, resilient, and sustainable by combining the power of ICT tools with AI/Machine Learning backed remote sensing technologies. Using remote sensing technologies, this study aims to enhance methodologies for mapping and monitoring changes in terrestrial Landcover resources in Thailand’s Dong Phayayen-Khao Yai National Park. The goal is to investigate and develop a remote sensing technique for classifying terrestrial Landcover by compensating for topographic effects. Changes were detected using the Landsat 5-TM and Landsat 8 OLI satellites, and deviations from solar and terrain were rectified before the satellite imagery was identified using a Random Forest classifier. It improves efficiency in identifying terrestrial forest regions by combining high-level numerical modelling data (Digital Elevation Model: DEM) with it. The results showed that in the Khao Yai National Park area, the extraction of terrestrial Landcover areas using Long-term Landsat satellite photos performed significantly, with an accuracy of 82.05 percent. The goal of this study is to leverage the power of AI to make the best use of a wide range of terrestrial forest resources. This includes the significance of conducting a comprehensive evaluation of legislation governing the management of terrestrial forest resources. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151121103&doi=10.1007%2f978-3-031-19560-0_27&partnerID=40&md5=cc470a6867e0d654afe4426802d72741
https://ir.swu.ac.th/jspui/handle/123456789/29375
Appears in Collections:Scopus 2023

Files in This Item:
There are no files associated with this item.


Items in SWU repository are protected by copyright, with all rights reserved, unless otherwise indicated.