UNIGEOCLIP: Unified Geospatial Contrastive Learning

TL;DR

UNIGEOCLIP is a novel multimodal contrastive learning framework that aligns five geospatial data modalities in a unified space, enabling improved geospatial task performance.

Contribution

It introduces all-to-all contrastive alignment for multiple geospatial modalities and a scaled latitude-longitude encoder for better spatial representation.

Findings

Outperforms single-modality contrastive models on geospatial tasks.

Enables seamless comparison and retrieval across multiple geospatial data types.

Demonstrates the effectiveness of holistic multimodal geospatial alignment.

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.