license: other
configs:
- config_name: SAT-4
- config_name: SAT-6
- config_name: NASC-TG2
- config_name: WHU-RS19
- config_name: RSSCN7
- config_name: RS_C11
- config_name: SIRI-WHU
- config_name: EuroSAT
- config_name: NWPU-RESISC45
- config_name: PatternNet
- config_name: RSD46-WHU
- config_name: GID
- config_name: CLRS
- config_name: Optimal-31
- config_name: Airbus-Wind-Turbines-Patches
- config_name: USTC_SmokeRS
- config_name: Canadian_Cropland
- config_name: Ships-In-Satellite-Imagery
- config_name: Satellite-Images-of-Hurricane-Damage
- config_name: Brazilian_Coffee_Scenes
- config_name: Brazilian_Cerrado-Savanna_Scenes
- config_name: Million-AID
- config_name: UC_Merced_LandUse_MultiLabel
- config_name: MLRSNet
- config_name: MultiScene
- config_name: RSI-CB256
- config_name: AID_MultiLabel
task_categories:
- image-classification
- zero-shot-image-classification
pretty_name: SATellite ImageNet
size_categories:
- 100K<n<1M
language:
- en
Dataset Card for Dataset Name
Dataset Description
- Homepage: https://satinbenchmark.github.io
- Repository:
- Paper: SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models
- Leaderboard: SATIN Leaderboard
Dataset Summary
SATIN (SATellite ImageNet) is a metadataset containing 27 constituent satellite and aerial image datasets spanning 6 distinct tasks: Land Cover, Land Use, Hierarchical Land Use, Complex Scenes, Rare Scenes, and False Colour Scenes. The imagery is globally distributed, comprised of resolutions spanning 5 orders of magnitude, multiple fields of view sizes, and over 250 distinct class labels. Presented at ICCV '23 TNGCV Workshop.
Dataset Structure
The SATIN benchmark is comprised of the following datasets:
Task 1: Land Cover
- SAT-4
- SAT-6
- NASC-TG2
Task 2: Land Use
- WHU-RS19
- RSSCN7
- RS_C11
- SIRI-WHU
- EuroSAT
- NWPU-RESISC45
- PatternNet
- RSD46-WHU
- GID
- CLRS
- Optimal-31
Task 3: Hierarchical Land Use
- Million-AID
- RSI-CB256
Task 4: Complex Scenes
- UC_Merced_LandUse_MultiLabel
- MLRSNet
- MultiScene
- AID_MultiLabel
Task 5: Rare Scenes
- Airbus-Wind-Turbines-Patches
- USTC_SmokeRS
- Canadian_Cropland
- Ships-In-Satellite-Imagery
- Satellite-Images-of-Hurricane-Damage
Task 6: False Colour Scenes
- Brazilian_Coffee_Scenes
- Brazilian_Cerrado-Savanna_Scenes
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 'config'.
Example Usage
from datasets import load_dataset
hf_dataset = load_dataset('jonathan-roberts1/SATIN', DATASET_NAME, split='train') # for DATASET_NAME use one of the configs listed above (e.g., EuroSAT)
features = hf_dataset.features
class_labels = features['label'].names # Note for the Hierarchical Land Use datasets, the label field is replaced with label1, label2, ...
random_index = 5
example = hf_dataset[random_index]
image, label = example['image'], example['label']
Data Splits
For each config, there is just the single, default 'train' split.
Source Data
More information regarding the source data can be found in our paper. Additionally, each of the constituent datasets have been uploaded to HuggingFace datasets. They can be accessed at: huggingface.co/datasets/jonathan-roberts1/DATASET_NAME.
Dataset Curators
This dataset was curated by Jonathan Roberts, Kai Han, and Samuel Albanie
Licensing Information
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 for research purposes; usage information of specific constituent datasets can be found in the Appendix of our paper.
Citation Information
@article{roberts2023satin,
title = {SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models},
author = {Jonathan Roberts, Kai Han, and Samuel Albanie},
year = {2023},
eprint = {2304.11619},
archivePrefix= {arXiv},
primaryClass = {cs.CV}
}