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We present a novel dataset based on Sentinel-2 satellite\nimages covering 13 spectral bands and consisting out of 10 classes with in\ntotal 27,000 labeled and geo-referenced images. We provide benchmarks for this\nnovel dataset with its spectral bands using state-of-the-art deep Convolutional\nNeural Network (CNNs). With the proposed novel dataset, we achieved an overall\nclassification accuracy of 98.57%. The resulting classification system opens a\ngate towards a number of Earth observation applications. We demonstrate how\nthis classification system can be used for detecting land use and land cover\nchanges and how it can assist in improving geographical maps. 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