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In this paper, we address the challenge of land use and land coverclassification using Sentinel-2 satellite images. The Sentinel-2 satelliteimages are openly and freely accessible provided in the Earth observationprogram Copernicus. We present a novel dataset based on Sentinel-2 satelliteimages covering 13 spectral bands and consisting out of 10 classes with intotal 27,000 labeled and geo-referenced images. We provide benchmarks for thisnovel dataset with its spectral bands using state-of-the-art deep ConvolutionalNeural Network (CNNs). With the proposed novel dataset, we achieved an overallclassification accuracy of 98.57%. The resulting classification system opens agate towards a number of Earth observation applications. We demonstrate howthis classification system can be used for detecting land use and land coverchanges and how it can assist in improving geographical maps. Thegeo-referenced dataset EuroSAT is made publicly available athttps://github.com/phelber/eurosat.

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification