CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment

Abstract:Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despite recent advances, current methods struggle with cross-region generalization and semantic interpretability due to their reliance on region-specific auxiliary data and the neglect of semantic alignment within multi-temporal urban imagery. Therefore, we present CoST, a novel \underline{Co}ntrastive-based \underline{S}patial-\underline{T}emporal framework that aligns spatial context with multi-temporal semantics to extract universal geographic regularities shared across regions. Specifically, CoST explicitly models spatial correlations to capture transferable geographic structures and exploits multi-year urban change semantics to align learned representations with high-level geo-semantics. Extensive experiments demonstrate that CoST consistently achieves superior performance across various downstream tasks and in unseen scenario, yielding an average relative gain of 8.7\% over the strongest competing methods across eight city-indicator settings. The code is available in \href{this https URL}{this repo}.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2608.21041 [cs.CV]

(or arXiv:2608.21041v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.21041 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3799682.3841155 Focus to learn more DOI(s) linking to related resources

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