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README.md

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+---
+license: gpl-3.0
+library_name: pytorch
+pipeline_tag: image-segmentation
+tags:
+- semantic-segmentation
+- remote-sensing
+- aerial-imagery
+- trees-outside-forest
+- pytorch-lightning
+---
+
+# TOFMapper
+
+Trained checkpoints for mapping and classifying **Trees outside Forest (TOF)** in high-resolution aerial RGB orthophotos, from the paper
+[Mapping and classification of trees outside forests using deep learning](https://doi.org/10.1016/j.srs.2026.100510)
+(Science of Remote Sensing, 2026; preprint: [arXiv:2510.25239](https://arxiv.org/abs/2510.25239)).
+
+- **Code:** https://github.com/Moerizzy/TOFMapper
+- **Training data:** https://zenodo.org/records/17468859
+
+## Models
+
+| Model | File | Config | Size |
+|---|---|---|---|
+| FT-UNetFormer | `ftunetformer.ckpt` | [`config/tof/ftunetformer.py`](https://github.com/Moerizzy/TOFMapper/blob/main/config/tof/ftunetformer.py) | 1.5 GB |
+| DC-Swin | `dcswin.ckpt` | [`config/tof/dcswin_small.py`](https://github.com/Moerizzy/TOFMapper/blob/main/config/tof/dcswin_small.py) | 1.1 GB |
+| ABCNet | `abcnet.ckpt` | [`config/tof/abcnet.py`](https://github.com/Moerizzy/TOFMapper/blob/main/config/tof/abcnet.py) | 216 MB |
+| LSKNet | `unetformer_lsk_s.ckpt` | [`config/tof/unetformer_lsk_s.py`](https://github.com/Moerizzy/TOFMapper/blob/main/config/tof/unetformer_lsk_s.py) | 231 MB |
+| BANet | `banet.ckpt` | [`config/tof/banet.py`](https://github.com/Moerizzy/TOFMapper/blob/main/config/tof/banet.py) | 204 MB |
+| U-Net | `unet.ckpt` | [`config/tof/unet.py`](https://github.com/Moerizzy/TOFMapper/blob/main/config/tof/unet.py) | 496 MB |
+
+## Classes
+
+| Index | Class |
+|---|---|
+| 0 | Background |
+| 1 | Forest |
+| 2 | Patch |
+| 3 | Linear |
+| 4 | Tree |
+
+## Results
+
+Test-set results as reported in the TOFMapper repository. Best value per column in bold.
+
+| Model | mIoU | mF1 | Forest IoU | Forest F1 | Patch IoU | Patch F1 | Linear IoU | Linear F1 | Tree IoU | Tree F1 |
+|---|---|---|---|---|---|---|---|---|---|---|
+| ABCNet | 0.709 | 0.821 | 0.944 | 0.971 | 0.532 | 0.695 | 0.739 | 0.850 | 0.622 | 0.767 |
+| BANet | 0.656 | 0.777 | 0.937 | 0.967 | 0.422 | 0.593 | 0.689 | 0.816 | 0.575 | 0.730 |
+| DC-Swin | 0.714 | 0.824 | 0.949 | 0.974 | 0.570 | 0.726 | 0.751 | 0.858 | 0.586 | 0.727 |
+| FT-UNetFormer | **0.739** | **0.843** | **0.952** | **0.975** | **0.606** | **0.754** | **0.774** | **0.872** | **0.626** | **0.770** |
+| LSKNet | 0.697 | 0.812 | 0.943 | 0.971 | 0.545 | 0.705 | 0.718 | 0.836 | 0.582 | 0.736 |
+| U-Net | 0.484 | 0.618 | 0.804 | 0.891 | 0.151 | 0.263 | 0.527 | 0.690 | 0.456 | 0.626 |
+
+## Usage
+
+The files are PyTorch Lightning checkpoints and are loaded through the TOFMapper code, not through `transformers`.
+Set up the repository as described in its README, then download a checkpoint to the location its config expects
+(run from the `Project` folder that contains `TOFMapper/`). Example for FT-UNetFormer:
+
+```python
+from huggingface_hub import hf_hub_download
+
+name = "ftunetformer"  # or: dcswin, abcnet, unetformer_lsk_s, banet, unet
+hf_hub_download(
+    repo_id="Moerizzi/TOFMapper",
+    filename=f"{name}.ckpt",
+    local_dir=f"model_weights/tof/{name}",
+)
+```
+
+Run inference on a folder of georeferenced RGB orthophotos with the matching config:
+
+```bash
+python TOFMapper/inference_with_overlap.py \
+  -i path/to/images \
+  -c TOFMapper/config/tof/ftunetformer.py \
+  -o path/to/output \
+  -st 256 -ps 1024 -b 2 -m 500 -kr 0.7
+```
+
+Predictions are written as GeoTIFF and converted to a shapefile.
+
+> **Use an up-to-date checkout of the code.** Versions of `inference_with_overlap.py` before 9 October 2026
+> swapped the red and blue channels at inference, which does not match how the models were trained.
+
+## Input
+
+3-band RGB orthophotos (8 bit), normalised with ImageNet mean and standard deviation. The models were trained on 1024 x 1024 px patches.
+
+## License
+
+GPL-3.0, the same as the TOFMapper code.
+
+## Citation
+
+```bibtex
+@article{LUCAS2026100510,
+title = {Mapping and classification of trees outside forests using deep learning},
+journal = {Science of Remote Sensing},
+volume = {14},
+pages = {100510},
+year = {2026},
+issn = {2666-0172},
+doi = {https://doi.org/10.1016/j.srs.2026.100510},
+url = {https://www.sciencedirect.com/science/article/pii/S2666017226001483},
+author = {Moritz Lucas and Hamid Ebrahimy and Viacheslav Barkov and Ralf Pecenka and Kai-Uwe Kühnberger and Björn Waske}
+}
+```
+
+## Acknowledgement
+
+TOFMapper builds on [GeoSeg](https://github.com/WangLibo1995/GeoSeg) by Libo Wang.