In-Sat: A Novel Land Cover Classification Dataset for Indian Subcontinent

Remote sensing through satellite imagery is applied widely for environmental control, urban planning and land cover classification. To this end, supervised deep learning models can fully exploit the potential of satellite images. However, such models require large annotated datasets. Therefore, through this study we present two novel labelled satellite image datasets, namely In-Sat1, based on Sentinel-2 Satellite Imagery and Google Earth Imagery covering Indian subcontinent regions. We provide benchmarks and detailed analysis for these datasets using state-of-the-art and our improved remote sensing deep learning models which achieve 91% and 81% overall accuracy in region-wise split setting and 98% and 94% overall accuracy in class-wise split setting for Sentinel-2 data and Google Earth data respectively. We also demonstrate the application of our system for change detection.