UNIGEOCLIP: Unified Geospatial Contrastive Learning

• URL: http://arxiv.org/abs/2604.11668v1
• Date: Mon, 13 Apr 2026 16:14:49 GMT
• Title: UNIGEOCLIP: Unified Geospatial Contrastive Learning
• Abstract summary: Co-located geospatial data spans aerial imagery, street-level views, elevation models, text, and geographic coordinates.<n>We introduce UNIGEOCLIP, a massively multimodal contrastive framework to jointly align five complementary geospatial modalities.<n>We also propose a scaled latitude-longitude encoder that improves spatial representation by capturing multi-scale geographic structure.
• Score: 23.04186252685893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing availability of co-located geospatial data spanning aerial imagery, street-level views, elevation models, text, and geographic coordinates offers a unique opportunity for multimodal representation learning. We introduce UNIGEOCLIP, a massively multimodal contrastive framework to jointly align five complementary geospatial modalities in a single unified embedding space. Unlike prior approaches that fuse modalities or rely on a central pivot representation, our method performs all-to-all contrastive alignment, enabling seamless comparison, retrieval, and reasoning across arbitrary combinations of modalities. We further propose a scaled latitude-longitude encoder that improves spatial representation by capturing multi-scale geographic structure. Extensive experiments across downstream geospatial tasks demonstrate that UNIGEOCLIP consistently outperforms single-modality contrastive models and coordinate-only baselines, highlighting the benefits of holistic multimodal geospatial alignment. A reference implementation is available at https://gastruc.github.io/unigeoclip.

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