UNIGEOCLIP: Unified Geospatial Contrastive Learning
For geospatial AI researchers, this work provides a unified multimodal representation that enables seamless cross-modal retrieval and reasoning, though the improvements over existing methods are not quantified with specific numbers.
UNIGEOCLIP introduces a massively multimodal contrastive framework that aligns five geospatial modalities (aerial imagery, street-level views, elevation, text, coordinates) in a unified embedding space via all-to-all contrastive learning, outperforming single-modality and coordinate-only baselines across downstream tasks.
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.