mlsanityData to 7 Oct 2026

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

arXiv 1709.00029 ↗First posted 31 Aug 2017cs.CV cs.LGcited by 1,488

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Cited by 478 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards 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.

Key facts. Cited by 1,488 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2017.

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Citing papers per year: 2021: 55; 2022: 128; 2023: 243; 2024: 363; 2025: 406; 2026: 293.

1,488 papers.

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