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--- |
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license: other |
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configs: |
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- config_name: SAT-4 |
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- config_name: SAT-6 |
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- config_name: NASC-TG2 |
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- config_name: WHU-RS19 |
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- config_name: RSSCN7 |
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- config_name: RS_C11 |
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- config_name: SIRI-WHU |
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- config_name: EuroSAT |
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- config_name: NWPU-RESISC45 |
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- config_name: PatternNet |
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- config_name: RSD46-WHU |
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- config_name: GID |
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- config_name: CLRS |
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- config_name: Optimal-31 |
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- config_name: Airbus-Wind-Turbines-Patches |
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- config_name: USTC_SmokeRS |
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- config_name: Canadian_Cropland |
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- config_name: Ships-In-Satellite-Imagery |
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- config_name: Satellite-Images-of-Hurricane-Damage |
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- config_name: Brazilian_Coffee_Scenes |
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- config_name: Brazilian_Cerrado-Savanna_Scenes |
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- config_name: Million-AID |
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- config_name: UC_Merced_LandUse_MultiLabel |
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- config_name: MLRSNet |
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- config_name: MultiScene |
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- config_name: RSI-CB256 |
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- config_name: AID_MultiLabel |
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task_categories: |
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- image-classification |
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- zero-shot-image-classification |
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pretty_name: SATellite ImageNet |
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size_categories: |
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- 100K<n<1M |
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language: |
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- en |
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--- |
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|
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# Dataset Card for Dataset Name |
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|
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## Dataset Description |
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|
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- **Homepage:** [https://satinbenchmark.github.io](https://satinbenchmark.github.io) |
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- **Repository:** |
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- **Paper:** [SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models](https://arxiv.org/pdf/2304.11619.pdf) |
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- **Leaderboard:** [SATIN Leaderboard](https://satinbenchmark.github.io/leaderboard.md) |
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|
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### Dataset Summary |
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|
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SATIN (SATellite ImageNet) is a metadataset containing 27 constituent satellite and aerial image datasets spanning 6 distinct tasks: Land Cover, Land Use, |
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Hierarchical Land Use, Complex Scenes, Rare Scenes, and False Colour Scenes. The imagery is globally distributed, comprised of resolutions spanning 5 orders |
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of magnitude, multiple fields of view sizes, and over 250 distinct class labels. |
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|
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## Dataset Structure |
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|
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The SATIN benchmark is comprised of the following datasets: |
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#### Task 1: Land Cover |
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- SAT-4 |
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- SAT-6 |
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- NASC-TG2 |
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#### Task 2: Land Use |
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- WHU-RS19 |
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- RSSCN7 |
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- RS_C11 |
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- SIRI-WHU |
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- EuroSAT |
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- NWPU-RESISC45 |
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- PatternNet |
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- RSD46-WHU |
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- GID |
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- CLRS |
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- Optimal-31 |
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#### Task 3: Hierarchical Land Use |
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- Million-AID |
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- RSI-CB256 |
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#### Task 4: Complex Scenes |
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- UC_Merced_LandUse_MultiLabel |
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- MLRSNet |
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- MultiScene |
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- AID_MultiLabel |
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#### Task 5: Rare Scenes |
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- Airbus-Wind-Turbines-Patches |
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- USTC_SmokeRS |
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- Canadian_Cropland |
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- Ships-In-Satellite-Imagery |
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- Satellite-Images-of-Hurricane-Damage |
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#### Task 6: False Colour Scenes |
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- Brazilian_Coffee_Scenes |
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- Brazilian_Cerrado-Savanna_Scenes |
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|
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For ease of use and to avoid having to download the entire benchmark for each use, in this dataset repository, each of the 27 datasets is included as a separate |
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'config'. |
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|
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### Example Usage |
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```python |
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from datasets import load_dataset |
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|
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hf_dataset = load_dataset('jonathan-roberts1/SATIN', DATASET_NAME, split='train') # for DATASET_NAME use one of the configs listed above (e.g., EuroSAT) |
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features = hf_dataset.features |
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class_labels = features['label'].names # Note for the Hierarchical Land Use datasets, the label field is replaced with label1, label2, ... |
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random_index = 5 |
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example = hf_dataset[random_index] |
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image, label = example['image'], example['label'] |
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``` |
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### Data Splits |
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For each config, there is just the single, default 'train' split. |
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|
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### Source Data |
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More information regarding the source data can be found in our paper. Additionally, each of the constituent datasets have been uploaded to HuggingFace datasets. |
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They can be accessed at: huggingface.co/datasets/jonathan-roberts1/DATASET_NAME. |
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|
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### Dataset Curators |
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|
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This dataset was curated by Jonathan Roberts, Kai Han, and Samuel Albanie |
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### Licensing Information |
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|
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As SATIN is comprised of existing datasets with differing licenses, there is not a single license for SATIN. All of the datasets in SATIN can be used |
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for research purposes; usage information of specific constituent datasets can be found in the Appendix of our paper. |
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|
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### Citation Information |
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``` |
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@article{roberts2023satin, |
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title = {SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models}, |
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author = {Jonathan Roberts, Kai Han, and Samuel Albanie}, |
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year = {2023}, |
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eprint = {2304.11619}, |
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archivePrefix= {arXiv}, |
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primaryClass = {cs.CV} |
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} |
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``` |