The DOI matches its Crossref record; 36 of the 39 references it cites were found in a registry or an index.
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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In the citing paper
“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)
In the citing paper
“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)
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Rosier, J. F., Taubenböck, H., Verburg, P. H., & van Vliet, J. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113076 · Cited by 52
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training
You, H., Zhou, L., Xiao, B., Codella, N., Cheng, Y., Xu, R., Chang, S., & Yuan, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_5 · Cited by 53
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models
Bulat, A., & Tzimiropoulos, G. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02225 · Cited by 64
Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
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A Survey on Deep Learning-Based Semi-Supervised Semantic Segmentation
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Double Targeted Universal Adversarial Perturbations
Benz, P., Zhang, C., Imtiaz, T., & Kweon, I. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_18 · Cited by 54
Searching for CNN Architectures for Remote Sensing Scene Classification
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Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataPreprint
Islam, A., Chen, C.-F., Panda, R., Karlinsky, L., Feris, R., & Radke, R. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07807 · Cited by 53
Multimodal Information Fusion for Weather Systems and Clouds Identification From Satellite Images
Bai, C., Zhao, D., Zhang, M., & Zhang, J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3202246 · Cited by 54
Chintalapati, B., Precht, A. D. F., Hanra, S., Laufer, R., Liwicki, M., & Eickhoff, J. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.03.053 · Cited by 56
Diesing, M., Mitchell, P., O’Keeffe, E., Gavazzi, G. M., & Bas, T. L. (2020) · Remote Sensing · doi:10.3390/rs12203398 · Cited by 50
A survey of efficient fine-tuning methods for Vision-Language Models — Prompt and Adapter
Xing, J., Liu, J., Wang, J., Sun, L., Chen, X., Gu, X., & Wang, Y. (2024) · Computers & Graphics · doi:10.1016/j.cag.2024.01.012 · Cited by 51
Extending global-local view alignment for self-supervised learning with remote sensing imagery
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Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images
Jain, P., Schoen-Phelan, B., & Ross, R. J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3204888 · Cited by 53
Dehghani, M., Tay, Y., Gritsenko, A. A., Zhao, Z., Houlsby, N., Diaz, F., Metzler, D., & Vinyals, O. (2021) · arXiv · doi:10.48550/arxiv.2107.07002 · Cited by 48
Explaining the Effects of Clouds on Remote Sensing Scene Classification
Gawlikowski, J., Ebel, P., Schmitt, M., & Zhu, X. X. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3221788 · Cited by 48
d’Andrimont, R., Claverie, M., Kempeneers, P., Muraro, D., Yordanov, M., Peressutti, D., Batič, M., & Waldner, F. (2023) · Earth system science data · doi:10.5194/essd-15-317-2023 · Cited by 48
Metadata
- 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
- 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 › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Social Sciences › Geography, Planning and Development › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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