---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label_1
    dtype:
      class_label:
        names:
          '0': unutilized land
          '1': commercial land
          '2': public service land
          '3': transportation land
          '4': industrial land
          '5': water area
          '6': residential land
          '7': agriculture land
  - name: label_2
    dtype:
      class_label:
        names:
          '0': dam
          '1': religious land
          '2': rock land
          '3': sparse shrub land
          '4': arable land
          '5': factory area
          '6': detached house
          '7': desert
          '8': lake
          '9': power station
          '10': beach
          '11': ice land
          '12': bare land
          '13': island
          '14': woodland
          '15': mobile home park
          '16': railway area
          '17': river
          '18': grassland
          '19': apartment
          '20': special land
          '21': port area
          '22': commercial area
          '23': highway area
          '24': mining area
          '25': sports land
          '26': airport area
          '27': leisure land
  - name: label_3
    dtype:
      class_label:
        names:
          '0': dam
          '1': parking lot
          '2': greenhouse
          '3': pier
          '4': bridge
          '5': mine
          '6': rock land
          '7': baseball field
          '8': apron
          '9': tennis court
          '10': sparse shrub land
          '11': works
          '12': oil field
          '13': meadow
          '14': ground track field
          '15': detached house
          '16': golf course
          '17': forest
          '18': desert
          '19': lake
          '20': beach
          '21': paddy field
          '22': ice land
          '23': bare land
          '24': storage tank
          '25': basketball court
          '26': island
          '27': substation
          '28': mobile home park
          '29': cemetery
          '30': quarry
          '31': solar power plant
          '32': helipad
          '33': roundabout
          '34': runway
          '35': wastewater plant
          '36': river
          '37': apartment
          '38': dry field
          '39': intersection
          '40': swimming pool
          '41': commercial area
          '42': church
          '43': road
          '44': orchard
          '45': terraced field
          '46': stadium
          '47': train station
          '48': railway
          '49': viaduct
          '50': wind turbine
  splits:
  - name: train
    num_bytes: 871962498
    num_examples: 10000
  download_size: 871644115
  dataset_size: 871962498
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "Million-AID"

## Dataset Description

- **Paper** [On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)
- **Split** Train

## Split Information

This HuggingFace dataset repository contains just the Train split. 

### Licensing Information

[CC BY-NC-ND 4.0](https://competitions.codalab.org/competitions/35974#learn_the_details-terms-and-conditions)

## Citation Information

[On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)

```
@article{long2021creating,
  title        = {On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid},
  author       = {Long, Yang and Xia, Gui-Song and Li, Shengyang and Yang, Wen and Yang, Michael Ying and Zhu, Xiao Xiang and Zhang, Liangpei and Li, Deren},
  year         = 2021,
  journal      = {IEEE Journal of selected topics in applied earth observations and remote sensing},
  publisher    = {IEEE},
  volume       = 14,
  pages        = {4205--4230}
}
```