Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe

Using methods of deep learning, this thesis conducts an extensive study on landslide mapping in the Genting Highlands with the goal of creating an automated tool to identify land locations most suitable for development by evaluating probable landslide hazards. The research makes use of Digital Eleva...

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Main Author:	Sappe, Siti Nuha Amisyah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Landslides Rockslides
Online Access:	https://ir.uitm.edu.my/id/eprint/102215/1/102215.pdf
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id	my-uitm-ir.102215
record_format	uketd_dc
spelling	my-uitm-ir.1022152024-11-13T03:33:24Z Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe 2024 Sappe, Siti Nuha Amisyah Landslides. Rockslides Using methods of deep learning, this thesis conducts an extensive study on landslide mapping in the Genting Highlands with the goal of creating an automated tool to identify land locations most suitable for development by evaluating probable landslide hazards. The research makes use of Digital Elevation Models (DEMs) and SPOT-7 satellite data to classify and analyse different landslide formations in the training area in accordance with Varnes1 1978 categorization. In order to identify possible landslides, this study trains two models Mask R-CNN and YOLO v3 in Kundasang and then applies them to the Genting Highlands. The approach comprises data acquisition from many sources, including open source DEMs and SPOT-7 satellite images, and then processing the data, including mosaicking, georeferencing, and manual landslide delineation. Deep learning methodologies are used, which include model training, object labelling, and accuracy assessment with metrics including F1-score, precision, and recall. The outcomes show how well Mask R-CNN and YOLO v3 can detect landslide-prone regions; comprehensive landslide maps for the Genting Highlands are produced. These results emphasise the advantages and disadvantages of each model and offer important insights into the effectiveness and dependability of automated landslide detection techniques. According to the study's findings, combining deep learning models with conventional remote sensing methods greatly improves the accuracy and efficiency of landslide detection, which helps with risk management and preventive measures in mountainous areas and provides a useful tool for land-use planning and development in landslide-prone areas. 2024 Thesis https://ir.uitm.edu.my/id/eprint/102215/ https://ir.uitm.edu.my/id/eprint/102215/1/102215.pdf text en public degree Universiti Teknologi MARA (UiTM) College of Built Environment
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Landslides Rockslides
spellingShingle	Landslides Rockslides Sappe, Siti Nuha Amisyah Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe
description	Using methods of deep learning, this thesis conducts an extensive study on landslide mapping in the Genting Highlands with the goal of creating an automated tool to identify land locations most suitable for development by evaluating probable landslide hazards. The research makes use of Digital Elevation Models (DEMs) and SPOT-7 satellite data to classify and analyse different landslide formations in the training area in accordance with Varnes1 1978 categorization. In order to identify possible landslides, this study trains two models Mask R-CNN and YOLO v3 in Kundasang and then applies them to the Genting Highlands. The approach comprises data acquisition from many sources, including open source DEMs and SPOT-7 satellite images, and then processing the data, including mosaicking, georeferencing, and manual landslide delineation. Deep learning methodologies are used, which include model training, object labelling, and accuracy assessment with metrics including F1-score, precision, and recall. The outcomes show how well Mask R-CNN and YOLO v3 can detect landslide-prone regions; comprehensive landslide maps for the Genting Highlands are produced. These results emphasise the advantages and disadvantages of each model and offer important insights into the effectiveness and dependability of automated landslide detection techniques. According to the study's findings, combining deep learning models with conventional remote sensing methods greatly improves the accuracy and efficiency of landslide detection, which helps with risk management and preventive measures in mountainous areas and provides a useful tool for land-use planning and development in landslide-prone areas.
format	Thesis
qualification_level	Bachelor degree
author	Sappe, Siti Nuha Amisyah
author_facet	Sappe, Siti Nuha Amisyah
author_sort	Sappe, Siti Nuha Amisyah
title	Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe
title_short	Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe
title_full	Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe
title_fullStr	Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe
title_full_unstemmed	Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe
title_sort	genting highlands landslide mapping using deep learning / siti nuha amisyah sappe
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Built Environment
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/102215/1/102215.pdf
_version_	1818588037254217728

Genting Highlands landslide mapping using deep learning / Siti Nuha Amisyah Sappe

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