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--- |
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license: fair-noncommercial-research-license |
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language: |
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- en |
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--- |
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# A multi-view contrastive learning framework for spatial embeddings in risk modeling |
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In this repository, we provide the pretrained models as described in our paper: |
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> Holvoet, F., Blier-Wong, C., & Antonio, K. (2025). A multi-view contrastive learning framework for spatial embeddings in risk modeling. *arXiv preprint arXiv:2511.17954*. |
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Paper is available as pre-print via Arxiv: **[arXiv preprint arXiv:2511.17954](https://arxiv.org/abs/2511.17954)** |
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This repository accompanies our [GitHub repository](https://github.com/freekholvoet/MultiviewSpatialEmbeddings), where you can find the code to train the models and an example of usage. |
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## Using the pretrained models |
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The pretrained models described in Section 3.4 of the paper are provided in this repository. |
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There are five different models available: |
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- `EU16_GS32_OSM16.ckpt` |
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- `EU16_OSM16.ckpt` |
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- `EU32_GS96_OSM32.ckpt` |
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- `EU64_GS64.ckpt` |
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- `EU8_GS32_OSM32.ckpt` |
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Example on how to download the models and calculate embeddings for a list of latitude and longitude coordinates: |
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```python |
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from huggingface_hub import hf_hub_download |
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from load_lightweight import get_mvloc_encoder |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Example coordinates (latitude, longitude) of various European cities |
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c = torch.tensor([ |
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(50.8503, 4.3517), # Brussels |
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(48.8566, 2.3522), # Paris |
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(51.5074, -0.1278), # London |
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(52.5200, 13.4050), # Berlin |
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(41.9028, 12.4964), # Rome |
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(40.4168, -3.7038), # Madrid |
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(59.3293, 18.0686), # Stockholm |
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(60.1699, 24.9384), # Helsinki |
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(47.4979, 19.0402), # Budapest |
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(48.2082, 16.3738), # Vienna |
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], dtype=torch.float32) |
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model = get_mvloc_encoder( |
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hf_hub_download("FreekH/multiview_spatial_embedding", "MODEL_NAME.ckpt"), |
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device=device |
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) |
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model.to(device) |
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with torch.no_grad(): |
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emb = model(c.to(device).double()).detach().cpu().numpy() |
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``` |
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Replace `MODEL_NAME.ckpt` with the desired model filename from the list above. The [GitHub repository](https://github.com/freekholvoet/MultiviewSpatialEmbeddings) contains a Jupyter Notebook, called Add_embeddings_to_data.ipynb, that includes a function to systematically add embeddings to a data set containing a latitude and a longitude feature. |
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## Citation |
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Citing the paper: |
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```bibtex |
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@article{holvoet2025multiview, |
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title={A multi-view contrastive learning framework for spatial embeddings in risk modeling}, |
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author={Holvoet, Freek and Blier-Wong, Christopher and Antonio, Katrien}, |
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journal={arXiv preprint arXiv:2511.17954}, |
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year={2025} |
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} |
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``` |
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Citing the models: |
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```bibtex |
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@misc{holvoet_pretrainedmodels, |
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author = { Freek Holvoet }, |
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title = { Spatial embeddings via multiview contrastive learning}, |
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year = 2025, |
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note = {[Pretrained spatial embedding models]}, |
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url = { https://huggingface.co/FreekH/multiview_spatial_embedding }, |
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doi = { 10.57967/hf/7009 }, |
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publisher = { Hugging Face } |
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} |
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``` |
