A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context

Bibliographic Details
Main Author: Salam, Roquia
Publication Date: 2025
Other Authors: Pla, Filiberto, Ahmed, Bayes, Painho, Marco
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/182028
Summary: Salam, R., Pla, F., Ahmed, B., & Painho, M. (2025). A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context. Natural Hazards Research, 5(1), 175-186. https://doi.org/10.1016/j.nhres.2024.09.001 --- This work is the MSc thesis of Roquia Salam and she is grateful to the European Commission for fully funding her Master’s program (Geospatial Technologies) by awarding the Erasmus Mundus Scholarship.
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spelling A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse contextRainfall-induced shallow landslidesData-sparse contextPlanetScope imagerySentinel-2 imageryU-net modeRepeated stratified hold-out validationBangladeshEarth and Planetary Sciences (miscellaneous)Environmental Science (miscellaneous)Geography, Planning and DevelopmentSDG 13 - Climate ActionSalam, R., Pla, F., Ahmed, B., & Painho, M. (2025). A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context. Natural Hazards Research, 5(1), 175-186. https://doi.org/10.1016/j.nhres.2024.09.001 --- This work is the MSc thesis of Roquia Salam and she is grateful to the European Commission for fully funding her Master’s program (Geospatial Technologies) by awarding the Erasmus Mundus Scholarship.Detecting rainfall-induced shallow landslides in data-sparse regions has become increasingly important for effective landslide disaster management. Previous studies have predominantly focused on automated methods for deep-seated, earthquake-triggered landslides. This study addresses this gap by employing a U-net Convolutional Neural Network (CNN) model to detect rainfall-induced shallow landslides using multi-temporal, high-resolution PlanetScope (3m spatial resolution), medium-resolution Sentinel-2 (10m spatial resolution) imagery, and ALOS-PALSAR-provided digital elevation model (DEM). Four datasets were created: Datasets A and B using PlanetScope, and Datasets C and D using Sentinel-2, with Datasets B and D also including DEM data. A total of 181 manually delineated landslide polygons were used as ground truth masks. Each dataset was tested using repeated stratified hold-out validation. Performance metrics included precision, recall, F1 score, loss, and accuracy. Results indicated that Datasets A and B outperformed the others; however, integrating DEM with Dataset B did not enhance model accuracy. The best mean precision, recall, F1 score, loss, and accuracy were 1, 0.625, 0.625, 0.380, and 0.999, respectively, for both Datasets A and B. This study demonstrates the U-net model's potential for detecting rainfall-induced shallow landslides in various geographic and temporal contexts globally.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNSalam, RoquiaPla, FilibertoAhmed, BayesPainho, Marco2025-04-10T15:43:44Z2025-032025-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/182028eng2666-5921PURE: 99998885https://doi.org/10.1016/j.nhres.2024.09.001info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-05-26T01:42:11Zoai:run.unl.pt:10362/182028Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T06:25:58.538176Repositórios 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 A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
title A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
spellingShingle A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
Salam, Roquia
Rainfall-induced shallow landslides
Data-sparse context
PlanetScope imagery
Sentinel-2 imagery
U-net mode
Repeated stratified hold-out validation
Bangladesh
Earth and Planetary Sciences (miscellaneous)
Environmental Science (miscellaneous)
Geography, Planning and Development
SDG 13 - Climate Action
title_short A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_full A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_fullStr A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_full_unstemmed A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
title_sort A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context
author Salam, Roquia
author_facet Salam, Roquia
Pla, Filiberto
Ahmed, Bayes
Painho, Marco
author_role author
author2 Pla, Filiberto
Ahmed, Bayes
Painho, Marco
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Salam, Roquia
Pla, Filiberto
Ahmed, Bayes
Painho, Marco
dc.subject.por.fl_str_mv Rainfall-induced shallow landslides
Data-sparse context
PlanetScope imagery
Sentinel-2 imagery
U-net mode
Repeated stratified hold-out validation
Bangladesh
Earth and Planetary Sciences (miscellaneous)
Environmental Science (miscellaneous)
Geography, Planning and Development
SDG 13 - Climate Action
topic Rainfall-induced shallow landslides
Data-sparse context
PlanetScope imagery
Sentinel-2 imagery
U-net mode
Repeated stratified hold-out validation
Bangladesh
Earth and Planetary Sciences (miscellaneous)
Environmental Science (miscellaneous)
Geography, Planning and Development
SDG 13 - Climate Action
description Salam, R., Pla, F., Ahmed, B., & Painho, M. (2025). A Convolutional Neural Network-based approach for automatically detecting rainfall-induced shallow landslides in a data-sparse context. Natural Hazards Research, 5(1), 175-186. https://doi.org/10.1016/j.nhres.2024.09.001 --- This work is the MSc thesis of Roquia Salam and she is grateful to the European Commission for fully funding her Master’s program (Geospatial Technologies) by awarding the Erasmus Mundus Scholarship.
publishDate 2025
dc.date.none.fl_str_mv 2025-04-10T15:43:44Z
2025-03
2025-03-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/182028
url http://hdl.handle.net/10362/182028
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2666-5921
PURE: 99998885
https://doi.org/10.1016/j.nhres.2024.09.001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12
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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