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-sample 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.The source code is available at https://github.com/CityMind-Lab/SatCLE.
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