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--- |
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license: cc-by-4.0 |
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datasets: |
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- DominikM198/PP2-M |
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base_model: |
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- openai/clip-vit-large-patch14 |
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- BAAI/bge-small-en-v1.5 |
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- torchgeo/vit_small_patch16_224_sentinel2_all_moco |
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- DominikM198/OSM-MAE |
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pipeline_tag: feature-extraction |
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tags: |
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- SpatialRepresentationLearning |
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- GeoFoundationModel |
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- GeoFM |
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- ContrastiveLearning |
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- Mutlimodal |
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--- |
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# UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations |
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This repository provides the **pretrained weights** of the **UrbanFusion** model — a framework for learning robust spatial representations through stochastic multimodal fusion. |
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UrbanFusion can generate **location encodings** from *any subset* of the following modalities: |
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- 📍 Geographic coordinates |
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- 🏙️ Street-view imagery |
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- 🛰️ Remote sensing data |
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- 🗺️ OSM basemaps |
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- 🏬 Points of interest (POIs) |
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🔗 The full **source code** is available on [GitHub](https://github.com/DominikM198/UrbanFusion), and further details are described in our paper. |
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## 📖 Citation |
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```bibtex |
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@article{muehlematter2025urbanfusion, |
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title = {UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations}, |
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author = {Dominik J. Mühlematter and Lin Che and Ye Hong and Martin Raubal and Nina Wiedemann}, |
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year = {2025}, |
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journal = {arXiv preprint arXiv:2510.13774} |
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} |
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``` |
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--- |
