  In this paper, we address the challenge of land use and land cover
classification using Sentinel-2 satellite images. The Sentinel-2 satellite
images are openly and freely accessible provided in the Earth observation
program Copernicus. We present a novel dataset based on Sentinel-2 satellite
images covering 13 spectral bands and consisting out of 10 classes with in
total 27,000 labeled and geo-referenced images. We provide benchmarks for this
novel dataset with its spectral bands using state-of-the-art deep Convolutional
Neural Network (CNNs). With the proposed novel dataset, we achieved an overall
classification accuracy of 98.57%. The resulting classification system opens a
gate towards a number of Earth observation applications. We demonstrate 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.
