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

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