EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
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 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.
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The authors of Helber et al. (2019) achieve 98.56% accuracy on EUROSAT using solely RGB channels as input to ResNet50.
“the authors of Helber et al. (2019) achieve very high accuracy on EUROSAT, i.e. 98.56% using solely the RGB channels as input to a ResNet50 model.”
From Benchmarking and scaling of deep learning models for land cover image classification (2023) · cites this paper as Helber et al. (2019)
Helber et al. (2019) show that one can achieve >90% accuracy on EUROSAT using only one band, making multispectral information redundant.
“Moreover, their experiments show that one can achieve very high accuracy >90% while using only one band making the multispectral information redundant.”
From Benchmarking and scaling of deep learning models for land cover image classification (2023) · cites this paper as Helber et al. (2019)
The overall coarse-to-fine-grain classifier reaches performances on par with Helber et al. [35], where the authors reached a 98.57% overall accuracy using a ResNet-50-based model.
“reaching performances on par with Helber et al. [35], where the authors reached a 98.57% of overall accuracy, by using a model based on the ResNet-50.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [35]
The experimental dataset is EuroSAT, a dataset of Sentinel-2 satellite images covering 13 spectral bands and consisting of ten classes with in total 27000 labeled and georeferenced images.
“The experimental dataset under consideration is the”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [35]
The architecture proposed in this article is extremely less complex than the one proposed in [35], since ResNet-50 is composed of 50 layers while the proposed one is composed of six layers only: five classical and one quantum.
“the architecture proposed in this article is extremely less complex than the one proposed in [35], since the ResNet-50 is composed of 50 layers, while the proposed one is composed of six layers only: five classical and one quantum.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [35]
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- Authors
- Helber, P., Bischke, B., Dengel, A., & Borth, D.
- Affiliations
- University of Kaiserslautern (DE)
- University of St.Gallen (CH)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2019-07
- Volume
- 12
- Issue
- 7
- Pages
- 2217-2226
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2964194231
- Cited by
- 1,951
- References
- 39
- Citations by year
- 2026: 430 · 2025: 505 · 2024: 352 · 2023: 257 · 2022: 167 · 2021: 140 · 2020: 77 · 2019: 19 · 2018: 4
- FWCI
- 67.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- Funders
- BMBF Project DeFuseNN (01IW17002)
- Nvidia (xxx)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- land use and land cover classification, EuroSAT dataset, Sentinel-2, satellite imagery, deep learning benchmark, convolutional neural networks, patch-based classification, geo-referenced images, Copernicus
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