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

University of Kaiserslautern · University of St.Gallen

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Abstract

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…

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1,683
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Authors

4

Topics & keywords

Keywords
  • Land cover
  • Benchmark (surveying)
  • Computer science
  • Convolutional neural network
  • Remote sensing
  • Earth observation
  • Cover (algebra)
  • Satellite
UN Sustainable Development Goals
  • Life in Land
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