Publicação
Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh
| Resumo: | Water 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. |
|---|---|
| Autores principais: | Enan, Muhammad Esmat |
| Assunto: | Artificial Neural Network Convolution Neural Network Deep Learning Landsat data Machine Learning Urban Water bodies |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868415413755314176 |
|---|---|
| author | Enan, Muhammad Esmat |
| author_facet | Enan, Muhammad Esmat |
| author_role | author |
| contributor_name_str_mv | Pla Bañón, Filiberto Fernández Beltrán, Rubén Caetano, Mário Sílvio Rochinha de Andrade RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Enan, Muhammad Esmat\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Pla Bañón, Filiberto Fernández Beltrán, Rubén Caetano, Mário Sílvio Rochinha de Andrade RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Enan, Muhammad Esmat |
| datacite.date.Accepted.fl_str_mv | 2021-03-05T00:00:00Z |
| datacite.date.available.fl_str_mv | 2021-03-11T11:55:51Z |
| datacite.date.embargoed.fl_str_mv | 2021-03-11T11:55:51Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Artificial Neural Network Convolution Neural Network Deep Learning Landsat data Machine Learning Urban Water bodies |
| datacite.titles.title.fl_str_mv | Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh |
| dc.contributor.none.fl_str_mv | Pla Bañón, Filiberto Fernández Beltrán, Rubén Caetano, Mário Sílvio Rochinha de Andrade RUN |
| dc.creator.none.fl_str_mv | Enan, Muhammad Esmat |
| dc.date.Accepted.fl_str_mv | 2021-03-05T00:00:00Z |
| dc.date.available.fl_str_mv | 2021-03-11T11:55:51Z |
| dc.date.embargoed.fl_str_mv | 2021-03-11T11:55:51Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/113704 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Artificial Neural Network Convolution Neural Network Deep Learning Landsat data Machine Learning Urban Water bodies |
| dc.title.fl_str_mv | Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | Water 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/7235bc8f-a476-4711-a7a4-9b04c191094e/download |
| id | run_d99f20c3ecdaed81918a0fe462dfd22e |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/113704 |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/113704 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Enan, Muhammad Esmat |
| publishDate | 2021 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTWater 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.application/pdfpt_PTDeep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, BangladeshEnan, Muhammad EsmatPla Bañón, FilibertoFernández Beltrán, RubénCaetano, Mário Sílvio Rochinha de AndradeHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2026708212021-03-11T11:55:51Z2021-03-052021-03-05T00:00:00ZHandlehttp://hdl.handle.net/10362/113704http://purl.org/coar/access_right/c_abf2open accessArtificial Neural NetworkConvolution Neural NetworkDeep LearningLandsat dataMachine LearningUrban Water bodies3100202 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2021-03-05http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/7235bc8f-a476-4711-a7a4-9b04c191094e/download |
| 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 |
| status | SINGLETON |
| subject.fl_str_mv | Artificial Neural Network Convolution Neural Network Deep Learning Landsat data Machine Learning Urban Water bodies |
| title | 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_short | 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 |
| topic | Artificial Neural Network Convolution Neural Network Deep Learning Landsat data Machine Learning Urban Water bodies |
| topic_facet | Artificial Neural Network Convolution Neural Network Deep Learning Landsat data Machine Learning Urban Water bodies |
| url | http://hdl.handle.net/10362/113704 |
| visible | 1 |