EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
University of Kaiserslautern · University of St.Gallen
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…
Citation impact
- FWCI
- 76.11
- Percentile
- 100%
- References
- 49
Authors
4Topics & keywords
- Land cover
- Benchmark (surveying)
- Computer science
- Convolutional neural network
- Remote sensing
- Earth observation
- Cover (algebra)
- Satellite
- Life in Land