Predicting Urban trends of growth with Google Earth Engine
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
| Автори: | , , , , |
|---|---|
| Інші автори: | |
| Формат: | Дисертація |
| Мова: | English |
| Опубліковано: |
Brac University
2023
|
| Предмети: | |
| Онлайн доступ: | http://hdl.handle.net/10361/17928 |
| id |
10361-17928 |
|---|---|
| record_format |
dspace |
| spelling |
10361-179282023-03-01T21:01:38Z Predicting Urban trends of growth with Google Earth Engine Islam, Rifah Tasmiah Mim, Md. Shahriar Tahiat Seeum, Nur Fathiha Ahmed, Tahmina Nawar, Tabassum Tanzim Rahman, Dr. Md. Khalilur Department of Computer Science and Engineering, Brac University Urbanization Prediction Google Earth Engine Land use/land cover (LULC) change Remote-sensed data Time series Urban growth Urban sustainability Google Earth. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 38-40). Land Usage is one of the most pressing concerns confronting the landscape of Bangladesh due its heavily dense population and limited area. Rapid urbanization has been seen in different parts of Bangladesh, so factors like change in infrastructure, decrease in agricultural land, decrease in greens and water body, as well as a steep increase in built ups are being observed all over the country. Hence, it is critical to have an overall concept of the urbanization trends in order to plan infras tructures, make policies and to conduct large-scale comparison studies. This paper presents a general framework to detect urbanization patterns and transformation of forested areas to residential or commercial developments, specifically in Dhaka division of Bangladesh using Machine Learning Algorithms (MLA). Moreover, for monitoring land coverage change we will be using Google Earth Engine (GEE) data which has a high accuracy record, with accuracy evaluations of 91.21 percent in 2013, 90.46 percent in 2015, and 91.01 percent in 2017. With the help of Landsat archive within GEE, two separate MLA is compared to find the most accurate classification Model. Along with GEE, softwares like QGIS version 3.26, ArcGIS, Terrsat has been used for data cleaning, processing and analysis. Therefore, In this study, the time span of 2015 to 2020 has been considered to create the prediction model and the prediction map of 2025 and 2030 has been obtained using the framework proposed in this work. It is of utmost necessity for the authorities to have optimal data on hand while planning the infrastructure. Rifah Tasmiah Islam Md. Shahriar Mim Nur Fathiha Tahiat Seeum Tahmina Ahmed Tabassum Tanzim Nawar B. Computer Science 2023-03-01T08:14:37Z 2023-03-01T08:14:37Z 2022 2022-09 Thesis ID: 19101459 ID: 17101463 ID: 19101460 ID: 19101479 ID: 19101134 http://hdl.handle.net/10361/17928 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 46 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Urbanization Prediction Google Earth Engine Land use/land cover (LULC) change Remote-sensed data Time series Urban growth Urban sustainability Google Earth. |
| spellingShingle |
Urbanization Prediction Google Earth Engine Land use/land cover (LULC) change Remote-sensed data Time series Urban growth Urban sustainability Google Earth. Islam, Rifah Tasmiah Mim, Md. Shahriar Tahiat Seeum, Nur Fathiha Ahmed, Tahmina Nawar, Tabassum Tanzim Predicting Urban trends of growth with Google Earth Engine |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Rahman, Dr. Md. Khalilur |
| author_facet |
Rahman, Dr. Md. Khalilur Islam, Rifah Tasmiah Mim, Md. Shahriar Tahiat Seeum, Nur Fathiha Ahmed, Tahmina Nawar, Tabassum Tanzim |
| format |
Thesis |
| author |
Islam, Rifah Tasmiah Mim, Md. Shahriar Tahiat Seeum, Nur Fathiha Ahmed, Tahmina Nawar, Tabassum Tanzim |
| author_sort |
Islam, Rifah Tasmiah |
| title |
Predicting Urban trends of growth with Google Earth Engine |
| title_short |
Predicting Urban trends of growth with Google Earth Engine |
| title_full |
Predicting Urban trends of growth with Google Earth Engine |
| title_fullStr |
Predicting Urban trends of growth with Google Earth Engine |
| title_full_unstemmed |
Predicting Urban trends of growth with Google Earth Engine |
| title_sort |
predicting urban trends of growth with google earth engine |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17928 |
| work_keys_str_mv |
AT islamrifahtasmiah predictingurbantrendsofgrowthwithgoogleearthengine AT mimmdshahriar predictingurbantrendsofgrowthwithgoogleearthengine AT tahiatseeumnurfathiha predictingurbantrendsofgrowthwithgoogleearthengine AT ahmedtahmina predictingurbantrendsofgrowthwithgoogleearthengine AT nawartabassumtanzim predictingurbantrendsofgrowthwithgoogleearthengine |
| _version_ |
1814309134902755328 |