Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh

Bibliographic Details
Main Author: Enan, Muhammad Esmat
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/113704
Summary: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, BangladeshArtificial Neural NetworkConvolution Neural NetworkDeep LearningLandsat dataMachine LearningUrban Water bodiesDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWater has been playing a key role in human life since the dawn of civilization. It is an integral part of our lives. In recent years, water bodies specially, urban water bodies are in a poor state due to climate change and rapid urban expansion. Though some cities have become aware of this poor state of water bodies, many cities around the world are not contemplating this issue. Because less research has been conducted on water bodies than other land covers in urban areas like built-up. Besides, many advanced algorithms are currently being utilized in different fields, but in terms of water body study, these advancements are still missing. That is why this study aims at investigating the spatio-temporal changes in urban water bodies in Chittagong city using deep learning and freely available Landsat data. Looking at the significance of the study, firstly, as this study has adopted two different deep learning (DL) models and evaluated the performance, the findings can help to understand the suitability of applying deep learning algorithms to extract information from mid to low resolution imagery like Landsat. Secondly, this work will help us to understand why the conservation of the existing water bodies is so important. Finally, this study will encourage further research in the field of deep learning and water bodies by opening the door for monitoring other environmental resources.Pla Bañón, FilibertoFernández Beltrán, RubénCaetano, Mário Sílvio Rochinha de AndradeRUNEnan, Muhammad Esmat2021-03-11T11:55:51Z2021-03-052021-03-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113704TID:202670821enginfo: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:RCAAP2024-05-22T17:51:09Zoai:run.unl.pt:10362/113704Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:22:23.952230Repositórios 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 Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
title Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
spellingShingle Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
Enan, Muhammad Esmat
Artificial Neural Network
Convolution Neural Network
Deep Learning
Landsat data
Machine Learning
Urban Water bodies
title_short Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
title_full Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
title_fullStr Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
title_full_unstemmed Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
title_sort Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
author Enan, Muhammad Esmat
author_facet Enan, Muhammad Esmat
author_role author
dc.contributor.none.fl_str_mv Pla Bañón, Filiberto
Fernández Beltrán, Rubén
Caetano, Mário Sílvio Rochinha de Andrade
RUN
dc.contributor.author.fl_str_mv Enan, Muhammad Esmat
dc.subject.por.fl_str_mv Artificial Neural Network
Convolution Neural Network
Deep Learning
Landsat data
Machine Learning
Urban Water bodies
topic Artificial Neural Network
Convolution Neural Network
Deep Learning
Landsat data
Machine Learning
Urban Water bodies
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2021
dc.date.none.fl_str_mv 2021-03-11T11:55:51Z
2021-03-05
2021-03-05T00:00:00Z
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/10362/113704
TID:202670821
url http://hdl.handle.net/10362/113704
identifier_str_mv TID:202670821
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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