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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 12; no. 7; pp. 2217 - 2226
• Main Authors: Helber, Patrick, Bischke, Benjamin, Dengel, Andreas, Borth, Damian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Band spectra; Benchmark testing; Benchmarks; Classification; Dataset; Datasets; deep convolutional neural network; Deep learning; Earth; earth observation; Feature extraction; Image classification; Land cover; land cover classification; Land use; land use classification; Machine learning; Neural networks; Remote sensing; satellite image classification; Satellite imagery; satellite images; Satellites; Spaceborne remote sensing; Spatial resolution; Spectral bands
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2019.2918242

In 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 27 000 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 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.