Machine Learning and Remote Sensing Technique for Urbanization Change Detection in Tangail District

Abstract

Presently, urban growth is a typical marvel globally, and it is obvious in non-industrial countries with current rapidity. Land-use changes are inferable from the fast urbanization in recent decades in non-industrial countries. With the fast growth of the populace, urbanization in Bangladesh has occurred in the same way as other different urban communities of the world. Tangail District has likewise been confronting rapid urban growth and land-use changes for the most recent couple of years. This paper aims to examine land-use changes investigation of Tangail District by utilizing multi-transient Landsat Thematic Mapper information. Remote sensing procedures are being used for analyzing examples of urban development utilizing geographic information systems (GIS). To understand the land-use change and urban development pattern, GIS and remote sensing procedures are cost-effective. In this research, Landsat images are ordered in four land-use classes utilizing different supervised classification including random forest algorithm. The spatial and fleeting changes of Tangail District concerning urban development and land use have been portrayed from Sentinel-2A data. We made an endeavor to explore the progressions from the years 2017, through 2020 for Tangail District. This study could significantly assist the urban planners through fundamental data about the degree and pattern of Tangail District's urbanization to deal with the rapid urban development and give the essential urban drawbacks and benefits.