Spatio-temporal Change Detection of Built-Up Areas with Sentinel-1 SAR Data Using Random Forest Classification Arnavutköy Istanbul

Abstract

As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BA of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BA footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. The changes experienced with the spatial-temporal data were determined, and causes and effects were investigated. In order to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.