Patrick, HelberHelberPatrickBenjamin, BischkeBischkeBenjaminAndreas, DengelDengelAndreasDamian, BorthBorthDamian2023-04-132023-04-132019-07https://www.alexandria.unisg.ch/handle/20.500.14171/9848910.1109/JSTARS.2019.2918242In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images that covers 13 spectral bands and is comprised of ten classes with a total of 27000 labeled and geo-referenced images. Benchmarks are provided for this novel dataset with its spectral bands using state-of-the-art deep convolutional neural networks. An overall classification accuracy of 98.57% was achieved with the proposed novel dataset. The resulting classification system opens a gate toward a number of earth observation applications. We demon- strate how this classification system can be used for detecting land use and land cover changes, and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat.enjournal article