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Al-Based Remoted Sensing Model for Sustainable Landcover Mapping and Monitoring in Smart City Context

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Research and Innovation Forum 2022 (RIIFORUM 2022)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

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.

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Authors and Affiliations

  1. Department of Geography, Faculty of Social Science, Srinakharinwirot University, 114 Sukhumvit 23, Wattana District, Bangkok, 10110, Thailand

    Asamaporn Sitthi

  2. Department of Computing and Mathematics, Management Manchester Metropolitan University, Manchester, UK

    Saeed-Ul Hassan

Authors
  1. Asamaporn Sitthi
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  2. Saeed-Ul Hassan
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Correspondence to Asamaporn Sitthi .

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Editors and Affiliations

  1. SGH Warsaw School of Economics, Warsaw, Poland

    Anna Visvizi

  2. University of Salerno, Fisciano, Italy

    Orlando Troisi

  3. University of Salerno, Fisciano, Italy

    Mara Grimaldi

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Sitthi, A., Hassan, SU. (2023). Al-Based Remoted Sensing Model for Sustainable Landcover Mapping and Monitoring in Smart City Context. In: Visvizi, A., Troisi, O., Grimaldi, M. (eds) Research and Innovation Forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-19560-0_27

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  • DOI: https://doi.org/10.1007/978-3-031-19560-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19559-4

  • Online ISBN: 978-3-031-19560-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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