---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': arbor woodland
          '1': artificial grassland
          '2': dry cropland
          '3': garden plot
          '4': industrial land
          '5': irrigated land
          '6': lake
          '7': natural grassland
          '8': paddy field
          '9': pond
          '10': river
          '11': rural residential
          '12': shrub land
          '13': traffic land
          '14': urban residential
  splits:
  - name: train
    num_bytes: 1777210275
    num_examples: 30000
  download_size: 1263253291
  dataset_size: 1777210275
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "GID"

## Dataset Description

- **Paper** [Land-cover classification with high-resolution remote sensing images using transferable deep models](https://www.sciencedirect.com/science/article/pii/S0034425719303414)

### Licensing Information

Public domain.

## Citation Information

[Land-cover classification with high-resolution remote sensing images using transferable deep models](https://www.sciencedirect.com/science/article/pii/S0034425719303414)

```
@article{GID2020,
  title        = {Land-cover classification with high-resolution remote sensing images using transferable deep models},
  author       = {Tong, Xin-Yi and Xia, Gui-Song and Lu, Qikai and Shen, Huanfeng and Li, Shengyang and You, Shucheng and Zhang, Liangpei},
  year         = 2020,
  journal      = {Remote Sensing of Environment},
  volume       = 237,
  pages        = 111322
}
```