| --- |
| dataset_info: |
| features: |
| - name: rgb |
| dtype: image |
| - name: bands |
| dtype: binary |
| - name: bands_shape |
| list: int64 |
| - name: bands_dtype |
| dtype: string |
| - name: band_metadata |
| dtype: string |
| - name: band_order |
| list: string |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': AnnualCrop |
| '1': Forest |
| '2': HerbaceousVegetation |
| '3': Highway |
| '4': Industrial |
| '5': Pasture |
| '6': PermanentCrop |
| '7': Residential |
| '8': River |
| '9': SeaLake |
| splits: |
| - name: train |
| num_bytes: 3001912034 |
| num_examples: 27000 |
| download_size: 2661440828 |
| dataset_size: 3001912034 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: mit |
| task_categories: |
| - image-classification |
| size_categories: |
| - 10K<n<100K |
| --- |
| # Eurosat Lulc Classification |
|
|
| This dataset provides multispectral satellite imagery capturing diverse land use and land cover patterns across European landscapes. The real-world images, collected from satellite platforms, offer a comprehensive resource for training and evaluating computer vision models in environmental monitoring and land cover analysis. The dataset contains 27,000 images across 10 classes: AnnualCrop, Forest, HerbaceousVegetation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, SeaLake. |
| Images per class: |
| - AnnualCrop: 3,000 |
| - Forest: 3,000 |
| - HerbaceousVegetation: 3,000 |
| - Highway: 2,500 |
| - Industrial: 2,500 |
| - Pasture: 2,000 |
| - PermanentCrop: 2,500 |
| - Residential: 3,000 |
| - River: 2,500 |
| - SeaLake: 3,000 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{helber2019eurosat, |
| title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification}, |
| author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian}, |
| journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, |
| volume={12}, |
| number={7}, |
| pages={2217--2226}, |
| year={2019}, |
| publisher={IEEE} |
| } |
| ``` |
|
|
| Helber, P., Bischke, B., Dengel, A., & Borth, D. (2018). EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification [Dataset]. In EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (Vol. 12, Issue 7, pp. 2217–2226). Zenodo. Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification, Valencia, Spain. https://doi.org/10.5281/zenodo.7711810 |
|
|
| Sentinel data is open source. Please refer to https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice for terms of use. |
| |
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
