Publicação

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

Ver documento

Detalhes bibliográficos
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

Registos relacionados

Registos relacionados