Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context

Bibliographic Details
Main Author: Salam, Roquia
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/165521
Summary: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse contextRainfall-induced shallow landslidesPlanetScope imagerySentinel-2 imageryALOS PALSAR digital elevation modelDeep learningU-netRepeated stratified hold-out validationData-sparse contextDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesDetecting rainfall-induced shallow landslides in data-sparse contexts has become an environmental concern in recent decades and is crucial for a comprehensive landslide disaster management plan (CLDMP). Most of the previous works have contributed to the development of automated methods for detecting earthquake-triggered landslides. Despite the substantial contributions of researchers in this field, gaps and uncertainties still exist in developing a method for automatically detecting rainfall-induced shallow landslides. To address this gap, the present study has utilized the deep learning (DL) based U-net model for automatically detecting rainfall-induced shallow landslides from multi-temporal, very high-resolution (VHR) PlanetScope, medium resolution (MR) Sentinel-2 imagery, and ALOS PALSAR-provided digital elevation model (DEM), collected from the years 2018, 2019, 2022, and 2023. Four different data sets have been prepared for this study: Dataset A, comprising red, green, blue (RGB), and near-infrared (NIR) bands of PlanetScope imagery; Dataset B, comprising RGB and NIR bands of PlanetScope imagery with the inclusion of the normalized difference vegetation index (NDVI) calculated from the red and NIR bands, elevation, and slope derived from DEM; Dataset C, comprising RGB and NIR bands of Sentinel-2 imagery; and Dataset D, comprising RGB and NIR bands of Sentinel-2 imagery with the inclusion of NDVI, elevation, and slope. As a case study, the Chittagong Hill Tracts (CHT) of Bangladesh have been selected. For training the U-net model with ground truth data, 181 landslide polygons have been created from Google Earth Pro, which is a small set of ground truth data. So, the horizontal flip technique has been applied to augment the dataset, effectively doubling the entire dataset. Each dataset (A, B, C, and D) has been experimented with in 4 different trials utilizing the repeated stratified hold-out validation method so that all data is used as test data, to avoid biased results. Comparatively, Trials 1 and 2 contain a larger set of landslide training samples than Trials 3 and 4. Thus, 16 different experiments have been conducted in the present study. The performance of the U-net model is evaluated by precision, recall, F1 score, loss, and accuracy metrics. It is explored from the experiment that Datasets A and B perform the best; however, the integration of the DEM data does not enhance the accuracy of the model. The datasets comprised of Sentinel-2 imagery (Datasets C and D) exhibited very poor performance in all trials (4) in detecting rainfall-induced shallow landslides. Among the four Trials, utilizing Dataset A and B, Trials 1 and 2 outperformed, indicating the necessity of using larger training samples for DL model implementation. The mean precision, recall, F1 score, loss, and accuracy based on Trials 1 and 2 are 1, 0.625, 0.625, 0.380, and 0.999, respectively (same results found in both Datasets A and B). Overall, the performance of the model indicates that the U-net model can be used to detect rainfall-induced shallow landslides across similar geographic regions and temporal contexts around the worldBañón, Filiberto PlaAhmed, BayesPainho, Marco Octávio TrindadeRUNSalam, Roquia2024-02-262027-02-26T00:00:00Z2024-02-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/165521TID:203564871enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:20:00Zoai:run.unl.pt:10362/165521Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:50:46.548487Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
title Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
spellingShingle Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
Salam, Roquia
Rainfall-induced shallow landslides
PlanetScope imagery
Sentinel-2 imagery
ALOS PALSAR digital elevation model
Deep learning
U-net
Repeated stratified hold-out validation
Data-sparse context
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_full Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_fullStr Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_full_unstemmed Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_sort Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
author Salam, Roquia
author_facet Salam, Roquia
author_role author
dc.contributor.none.fl_str_mv Bañón, Filiberto Pla
Ahmed, Bayes
Painho, Marco Octávio Trindade
RUN
dc.contributor.author.fl_str_mv Salam, Roquia
dc.subject.por.fl_str_mv Rainfall-induced shallow landslides
PlanetScope imagery
Sentinel-2 imagery
ALOS PALSAR digital elevation model
Deep learning
U-net
Repeated stratified hold-out validation
Data-sparse context
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Rainfall-induced shallow landslides
PlanetScope imagery
Sentinel-2 imagery
ALOS PALSAR digital elevation model
Deep learning
U-net
Repeated stratified hold-out validation
Data-sparse context
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2024
dc.date.none.fl_str_mv 2024-02-26
2024-02-26T00:00:00Z
2027-02-26T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/165521
TID:203564871
url http://hdl.handle.net/10362/165521
identifier_str_mv TID:203564871
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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