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FreekH/
multiview_spatial_embedding

2511.17954

10.57967/hf/7009

fair-noncommercial-research-license

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A multi-view contrastive learning framework for spatial embeddings in risk modeling

In this repository, we provide the pretrained models as described in our paper:

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.

Paper is available as pre-print via Arxiv: arXiv preprint arXiv:2511.17954

This repository accompanies our GitHub repository, where you can find the code to train the models and an example of usage.

Using the pretrained models

The pretrained models described in Section 3.4 of the paper are provided in this repository.

There are five different models available:

EU16_GS32_OSM16.ckpt
EU16_OSM16.ckpt
EU32_GS96_OSM32.ckpt
EU64_GS64.ckpt
EU8_GS32_OSM32.ckpt

Example on how to download the models and calculate embeddings for a list of latitude and longitude coordinates:

python

from huggingface_hub import hf_hub_download
from load_lightweight import get_mvloc_encoder
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Example coordinates (latitude, longitude) of various European cities
c = torch.tensor([
    (50.8503, 4.3517),   # Brussels
    (48.8566, 2.3522),   # Paris
    (51.5074, -0.1278),  # London
    (52.5200, 13.4050),  # Berlin
    (41.9028, 12.4964),  # Rome
    (40.4168, -3.7038),  # Madrid
    (59.3293, 18.0686),  # Stockholm
    (60.1699, 24.9384),  # Helsinki
    (47.4979, 19.0402),  # Budapest
    (48.2082, 16.3738),  # Vienna
], dtype=torch.float32) 

model = get_mvloc_encoder(
    hf_hub_download("FreekH/multiview_spatial_embedding", "MODEL_NAME.ckpt"),
    device=device
)
model.to(device)

with torch.no_grad():
    emb = model(c.to(device).double()).detach().cpu().numpy()

Replace MODEL_NAME.ckpt with the desired model filename from the list above. The GitHub repository 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.

Citation

Citing the paper:

bibtex

@article{holvoet2025multiview,
  title={A multi-view contrastive learning framework for spatial embeddings in risk modeling},
  author={Holvoet, Freek and Blier-Wong, Christopher and Antonio, Katrien},
  journal={arXiv preprint arXiv:2511.17954},
  year={2025}
}

Citing the models:

bibtex

@misc{holvoet_pretrainedmodels,
	author       = { Freek Holvoet },
	title        = { Spatial embeddings via multiview contrastive learning},
	year         = 2025,
	note         = {[Pretrained spatial embedding models]},
	url          = { https://huggingface.co/FreekH/multiview_spatial_embedding },
	doi          = { 10.57967/hf/7009 },
	publisher    = { Hugging Face }
}