Integration of Sentinel-1 and Sentinel-2 for Classification of Small Urban Areas in Rural Landscape aided by Google Earth Engine

Abstract

Rapid economic development and population growth lead to fast urban expansion in both urban and rural landscapes. Accurate and updated mapping of urban expansions is essential in urban and territorial planning for sustainable and strategic urban development. Using Earth Observation (EO) technologies, the classification of urban areas in a rural landscape is more challenging than big cities. In this regard, in this paper, we aim at assessing the integration of Sentinel-1 and Sentinel-2 satellite data for classifying small urban areas in rural landscape in Google Earth Engine (GEE). Images of close dates from Sentinel-1 and Sentinel-2 were selected, preprocessed, and integrated to develop a machine learning classification through a Support Vector Classification (SVM) classifier. We have also added vegetation indices to the investigated dataset. As a study area, the Strumica-Radovish Valley in the Republic of North Macedonia has been selected. The results showed that the integration of Sentinel-1 and Sentinel-2 performed better than Sentinel-2 alone, with accuracy higher than 90%. For future studies, we recommend testing the dataset to different study areas and adding different EO data for obtaining even higher accuracy.