--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': annual crop '1': forest '2': herbaceous vegetation '3': highway '4': industrial '5': pasture '6': permanent crop '7': residential '8': river '9': sea or lake splits: - name: train num_bytes: 88391109 num_examples: 27000 download_size: 88591771 dataset_size: 88391109 license: mit task_categories: - image-classification - zero-shot-image-classification --- # Dataset Card for "EuroSAT" ## Dataset Description - **Paper** [Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification](https://ieeexplore.ieee.org/iel7/4609443/8789745/08736785.pdf) - **Paper** [Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://ieeexplore.ieee.org/iel7/8496405/8517275/08519248.pdf) - **GitHub** [EuroSAT](https://github.com/phelber/EuroSAT) - **Data** [Zenodo](https://zenodo.org/record/7711810#.ZCcA9uzMLJx) ### Licensing Information MIT. ## Citation Information [Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification](https://ieeexplore.ieee.org/iel7/4609443/8789745/08736785.pdf) [Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://ieeexplore.ieee.org/iel7/8496405/8517275/08519248.pdf) ``` @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}, year = 2019, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, publisher = {IEEE} } @inproceedings{helber2018introducing, title = {Introducing 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}, year = 2018, booktitle = {IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium}, pages = {204--207}, organization = {IEEE} } ```