Mapping Green space, Roads & Built-up Areas with Optical Remote Sensing and Polarimetric SAR (A Novel Approach)

Recent progress in remote sensing and Geographical Information System (GIS) has revolutionized the research studies on urban space. Satellites that image on daily basis and cloud platforms that increase mathematical modelling precision and speed have given rise to extraction of high-quality data. In this study, we propose a novel approach in extracting information on green space, buildings, and roads in Ankara and Eskişehir cities in Türkiye. In this approach, optical and SAR images are utilized. Modelling is also fulfilled in google earth engine cloud platform using machine learning algorithm. We show how optical and SAR images with varying indexes may lead to a Land use/Land cover map with the highest overall accuracy (98.94 for Ankara and 93.97 for Eskişehir). Additionally, techniques offered in this study can help to extract other classes other than the present study ones. Land use/Land cover map is the basis of many studies and can benefit urban management, planning, urban policy making, protection and renovation, and environmental sustainment.