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dc.contributor.advisorRahman, Dr. Md. Khalilur
dc.contributor.authorIslam, Rifah Tasmiah
dc.contributor.authorMim, Md. Shahriar
dc.contributor.authorTahiat Seeum, Nur Fathiha
dc.contributor.authorAhmed, Tahmina
dc.contributor.authorNawar, Tabassum Tanzim
dc.date.accessioned2023-03-01T08:14:37Z
dc.date.available2023-03-01T08:14:37Z
dc.date.copyright2022
dc.date.issued2022-09
dc.identifier.otherID: 19101459
dc.identifier.otherID: 17101463
dc.identifier.otherID: 19101460
dc.identifier.otherID: 19101479
dc.identifier.otherID: 19101134
dc.identifier.urihttp://hdl.handle.net/10361/17928
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 38-40).
dc.description.abstractLand 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.en_US
dc.description.statementofresponsibilityRifah Tasmiah Islam
dc.description.statementofresponsibilityMd. Shahriar Mim
dc.description.statementofresponsibilityNur Fathiha Tahiat Seeum
dc.description.statementofresponsibilityTahmina Ahmed
dc.description.statementofresponsibilityTabassum Tanzim Nawar
dc.format.extent46 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac 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.
dc.subjectUrbanization Predictionen_US
dc.subjectGoogle Earth Engineen_US
dc.subjectLand use/land cover (LULC) changeen_US
dc.subjectRemote-sensed dataen_US
dc.subjectTime seriesen_US
dc.subjectUrban growthen_US
dc.subjectUrban sustainabilityen_US
dc.subject.lcshGoogle Earth.
dc.titlePredicting Urban trends of growth with Google Earth Engineen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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