---
license: mit
pipeline_tag: object-detection
tags:
  - instance-segmentation
  - remote-sensing
  - building-footprint-extraction
  - pytorch
  - mmdetection
---

# BONAI (LOFT) — Pretrained Weights

Official pretrained weights for **LOFT**, the model introduced in *Learning to Extract Building Footprints from Off-Nadir Aerial Images* (IEEE TPAMI 2022), trained on the [BONAI dataset](https://huggingface.co/datasets/jwwangchn/BONAI).

BONAI is a dataset for building footprint extraction (BFE) in off-nadir aerial images, containing 268,958 building instances across 3,300 aerial images from six Chinese cities (Shanghai, Beijing, Harbin, Jinan, Chengdu, Xi'an), with instance-level roof and footprint annotations and the corresponding offset vector for each building.

## Files

| File | Size | SHA256 |
| --- | --- | --- |
| `BONAI-LOFT-ROA-F1Score-64.31.pth` | 651,220,024 bytes | `2003f790d9a8a6e829cfc25b245a5db106abb0ca45b455a8910d699bec521e6b` |

The checkpoint filename is kept as originally released by the authors; it indicates an F1 score of 64.31 for this checkpoint.

## Usage

The model is implemented in the official MMDetection-based codebase. For configs, inference and evaluation (including `tools/bonai/bonai_evaluation.py`), see the GitHub repository:

- Code: https://github.com/jwwangchn/BONAI
- Paper: https://arxiv.org/abs/2204.13637
- Dataset: https://huggingface.co/datasets/jwwangchn/BONAI

## License

The BONAI code is released under the MIT License (see the GitHub repository). This checkpoint is distributed by the BONAI authors for research use with the official code.

## Citation

If you use the BONAI dataset, codebase or models in your research, please consider citing:

```bibtex
@article{wang2022bonai,
  author  = {Wang, Jinwang and Meng, Lingxuan and Li, Weijia and Yang, Wen and Yu, Lei and Xia, Gui-Song},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title   = {Learning to Extract Building Footprints from Off-Nadir Aerial Images},
  year    = {2022},
  doi     = {10.1109/TPAMI.2022.3162583}
}
```