Deep learning for studying urban water bodies' spatio-temporal transformation: a study of Chittagong City, Bangladesh
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71345
comunitat-uji-handle3:10234/141145
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TFG-TFMMetadatos
Título
Deep learning for studying urban water bodies' spatio-temporal transformation: a study of Chittagong City, BangladeshAutoría
Tutor/Supervisor; Universidad.Departamento
Pla, Filiberto; Fernández Beltrán, Rubén; Departament de Llenguatges i Sistemes InformàticsFecha de publicación
2021-03-05Editor
Universitat Jaume IResumen
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 ... [+]
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. [-]
Palabras clave / Materias
Màster Universitari Erasmus Mundus en Tecnologia Geoespacial | Erasmus Mundus University Master's Degree in Geospatial Technologies | Máster Universitario Erasmus Mundus en Tecnología Geoespacial | artificial neural network | convolution neural network | deep learning | Landsat data | machine learning | urban water bodies
Descripción
Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2020-2021
Tipo de documento
info:eu-repo/semantics/masterThesisDerechos de acceso
info:eu-repo/semantics/openAccess