LoCA: Enabling Multi-Modality in Geospatial Location Encoders
The paper studies location encoders that map coordinates to embeddings via contrastive alignment with satellite imagery. It proves that any Lipschitz location encoder has intrinsic output dimension at most 2, and that joint training collapses embedding covariance to low rank. It proposes LoCA, a two-stage pipeline: first co-aligning Sentinel-1 and Sentinel-2 encoders, then training location and metadata adapters on the frozen encoders. Across 17 tasks against five encoders, LoCA reports the most task wins and a 68.8% pairwise win rate.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
- Linked in sourcehttps://github.com/eleannavali/LoCA
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The paper studies location encoders that map coordinates to embeddings via contrastive alignment with satellite imagery. It proves that any Lipschitz location encoder has intrinsic output dimension at most 2, and that joint training collapses embedding covariance to low rank. It proposes LoCA, a two-stage pipeline: first co-aligning Sentinel-1 and Sentinel-2 encoders, then training location and metadata adapters on the frozen encoders. Across 17 tasks against five encoders, LoCA reports the most task wins and a 68.8% pairwise win rate.
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Key findings from the stored brief
- Any Lipschitz location encoder produces output of intrinsic dimension at most 2, regardless of architecture or bandwidth.
- Joint training of image and location encoders from scratch collapses embedding covariance to very low rank.
- LoCA first co-aligns Sentinel-1 and Sentinel-2 encoders, then trains location and metadata adapters on them.
- LoCA wins 68.8% of pairwise per-task comparisons against SatCLIP, GeoCLIP, GT-Loc, AlphaEarth and Copernicus-FM.
Source claims not independently checked; code and pretrained weights are listed as coming soon.
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Why this may matter
Location encoders are general-purpose geospatial embeddings, so understanding their structural limits and a way to extend them to multiple sensors could inform how such embeddings are built for climate, wildfire and biodiversity applications.
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