Observing mega cities to grow from space and predicting its growth using machine learning techniques
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| التنسيق: | أطروحة |
| اللغة: | English |
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BRAC Univeristy
2018
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/10133 |
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10361-10133 |
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10361-101332022-01-26T10:21:50Z Observing mega cities to grow from space and predicting its growth using machine learning techniques Chakma, Mitesh Zannat, Sadia Rhaman, Dr. Md. Khalilur Department of Computer Science and Engineering, BRAC University Megacity Machine learning This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 73-74). In recent years, developing countries are undergoing a massive change in growth in the urbanization. Urbanization is yielding many positive effects for these developing countries in infrastructure, economy, social status and so on. However, this extensive urbanization mostly resulting some adverse effects. This study aims to evaluate and observe the various changes like vegetation, built-up and water in the urban area of the greater Dhaka area of Bangladesh which has one of the highest growth rate among developing countries using Landsat 7 ETM+ and Landsat 8 OLI images between 2012 to 2018 using different GIS and remote sensing techniques. The analysis shows the superabundant growth of the buildup areas as well as degradation in the vegetated and water areas of greater Dhaka, Bangladesh. Based on such data, different machine learning techniques have been applied to show the performance in terms of accuracy and forecast a predicting rate for the future growth of Dhaka city. The experimental results provides an idea of the subsequent changes in terms of water body, vegetation and built-up areas for Dhaka city, with an aim to provide these useful information for the policy makers and urban planners. Mitesh Chakma Sadia Zannat B. Computer Science and Engineering 2018-05-13T06:17:00Z 2018-05-13T06:17:00Z 2018 2018 Thesis ID 13101082 ID 12201045 http://hdl.handle.net/10361/10133 en 74 pages application/pdf BRAC Univeristy |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Megacity Machine learning |
| spellingShingle |
Megacity Machine learning Chakma, Mitesh Zannat, Sadia Observing mega cities to grow from space and predicting its growth using machine learning techniques |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Rhaman, Dr. Md. Khalilur |
| author_facet |
Rhaman, Dr. Md. Khalilur Chakma, Mitesh Zannat, Sadia |
| format |
Thesis |
| author |
Chakma, Mitesh Zannat, Sadia |
| author_sort |
Chakma, Mitesh |
| title |
Observing mega cities to grow from space and predicting its growth using machine learning techniques |
| title_short |
Observing mega cities to grow from space and predicting its growth using machine learning techniques |
| title_full |
Observing mega cities to grow from space and predicting its growth using machine learning techniques |
| title_fullStr |
Observing mega cities to grow from space and predicting its growth using machine learning techniques |
| title_full_unstemmed |
Observing mega cities to grow from space and predicting its growth using machine learning techniques |
| title_sort |
observing mega cities to grow from space and predicting its growth using machine learning techniques |
| publisher |
BRAC Univeristy |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10133 |
| work_keys_str_mv |
AT chakmamitesh observingmegacitiestogrowfromspaceandpredictingitsgrowthusingmachinelearningtechniques AT zannatsadia observingmegacitiestogrowfromspaceandpredictingitsgrowthusingmachinelearningtechniques |
| _version_ |
1814309553684086784 |