Instructions to use fabiandegen/GeoPlanAgent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fabiandegen/GeoPlanAgent with PEFT:
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- Notebooks
- Google Colab
- Kaggle
| license: other | |
| license_name: sam-license | |
| license_link: https://huggingface.co/facebook/sam3/blob/main/LICENSE | |
| base_model: facebook/sam3 | |
| tags: | |
| - lora | |
| - peft | |
| - segmentation | |
| - image-segmentation | |
| - geospatial | |
| - sam3 | |
| # GeoPlanAgent — fine-tuned weights | |
| The trained weights behind | |
| [*Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary | |
| Reconstruction from Planning Records*](https://arxiv.org/abs/2606.02747) | |
| ([code](https://github.com/fabiandegen/GeoPlanAgent) · | |
| [project page](https://odeb1.github.io/Plan2Map_Project_Page/)). | |
| Both models are trained as 5-fold cross-validation over a shared case→fold | |
| split: each fold's copy has a fifth of the benchmark cases held out of its | |
| training data, and at inference every case is served by the copy that never | |
| saw it during training. | |
| | Path | What it is | Performance | | |
| |---|---|---| | |
| | `sam3_lora/fold_{0..4}/` | PEFT LoRA adapters for [facebook/sam3](https://huggingface.co/facebook/sam3), fine-tuned to segment drawn planning boundaries on scanned UK planning maps | 0.912 mean pixel IoU | | |
| | `rotation_classifier_kfold/fold_{0..4}/best.pt` | ResNet50 (ImageNet-pretrained) fine-tuned to classify scanned-map orientation (0°/90°/180°/270°) | 0.981 accuracy (with test-time augmentation) | | |
| ## Usage | |
| These weights are consumed by the | |
| [GeoPlanAgent pipeline](https://github.com/fabiandegen/GeoPlanAgent), which | |
| handles fold routing, adapter loading, and inference. From the root of a | |
| clone of that repository, download them straight into `models/`: | |
| ```bash | |
| hf download fabiandegen/GeoPlanAgent --include "sam3_lora/*" "rotation_classifier_kfold/*" --local-dir models | |
| ``` | |
| The SAM3 base weights are not included — they download from | |
| [facebook/sam3](https://huggingface.co/facebook/sam3) (gated; accept Meta's | |
| SAM License there) on the pipeline's first run. | |
| ## Licence | |
| - `sam3_lora/` — the adapters are fine-tuned from Meta's SAM 3 and are | |
| therefore distributed under the [SAM License](https://huggingface.co/fabiandegen/GeoPlanAgent/blob/main/SAM_LICENSE). | |
| - `rotation_classifier_kfold/` — fine-tuned from torchvision's | |
| ImageNet-pretrained ResNet50 (BSD-3-Clause); no additional restrictions. | |
| ## Citation | |
| ```bibtex | |
| @misc{Plan2Map2026, | |
| title={Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary Reconstruction from Planning Records}, | |
| author={Fabian Degen and Oishi Deb and Jindong Gu and Junchi Yu and Samuele Marro and Philip Torr and Jialin Yu}, | |
| year={2026}, | |
| eprint={2606.02747}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2606.02747}, | |
| } | |
| ``` | |