| --- |
| 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} |
| } |
| ``` |
|
|
