Nature Makes No Leaps: Building Continuous Location Embeddings with Satellite Imagery from the Web
Track: Web mining and content analysis
Keywords: urban computing, multimodal learning, location embedding, satellite imagery, web mining, contrastive learning, geospatial learning
TL;DR: In this work, we propose SatCLE, a novel framework for continuous location embeddings from satellite imagery that enhances spatial and semantic continuity, achieving state-of-the-art results in different geospatial tasks.
Abstract: Building location embedding from web-sourced satellite imagery has emerged as an enduring research focus in web mining. However, most existing methods are inherently constrained by their reliance on discrete, sparse sampling strategies, failing to capture
the essential spatial continuity of geographic spaces. Moreover, the presence of confounding factors in satellite images can distort the perception of actual objects, leading to semantic discontinuity in the embeddings. In this work, we propose **SatCLE**, a novel framework for Continuous Location Embeddings leveraging Satellite imagery. Specifically, to address the out-of-distribution query challenge of spatial continuity, we propose a geospatial refinement strategy comprising stochastic perturbation continuity expansion and graph propagation fusion, which transforms discrete geospatial coordinates into a continuous space. To mitigate the effects of confounders on semantic continuity, we introduce causal refinement, integrating causal theory to localize and eliminate spurious correlations arising from the environmental context. Through extensive experiments, **SatCLE** shows state-of-the-art performance, exhibiting superior spatial coherence and semantic fidelity across diverse geospatial tasks.
Submission Number: 1550
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