license:cc-by-4.0 arxiv:1807.01688 region:us
---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': flooded or damaged buildings
          '1': undamaged buildings
  splits:
  - name: train
    num_bytes: 25588780
    num_examples: 10000
  download_size: 26998688
  dataset_size: 25588780
license: cc-by-4.0
---
# Dataset Card for "Satellite-Images-of-Hurricane-Damage"

## Dataset Description

- **Paper** [Deep learning based damage detection on post-hurricane satellite imagery](https://arxiv.org/pdf/1807.01688.pdf)
- **Data** [IEEE-Dataport](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
- **Split** Train_another
- **GitHub** [DamageDetection](https://github.com/qcao10/DamageDetection)

## Split Information

This HuggingFace dataset repository contains just the Train_another split. 

### Licensing Information

[CC BY 4.0](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)

## Citation Information

[Deep learning based damage detection on post-hurricane satellite imagery](https://arxiv.org/pdf/1807.01688.pdf)

[IEEE-Dataport](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
```
@misc{sdad-1e56-18,
  title        = {Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks},
  author       = {Cao, Quoc Dung and Choe, Youngjun},
  year         = 2018,
  publisher    = {IEEE Dataport},
  doi          = {10.21227/sdad-1e56},
  url          = {https://dx.doi.org/10.21227/sdad-1e56}
}
@article{cao2018deep,
  title={Deep learning based damage detection on post-hurricane satellite imagery},
  author={Cao, Quoc Dung and Choe, Youngjun},
  journal={arXiv preprint arXiv:1807.01688},
  year={2018}
}
```