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

• URL: http://arxiv.org/abs/2608.21041v1
• Date: Fri, 21 Aug 2026 12:36:13 GMT
• Title: CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment
• Abstract summary: CoST aims to align spatial context with multi-temporal semantics to extract universal geographic regularities across regions.<n>Experiments demonstrate that CoST consistently achieves superior performance across various downstream tasks and in unseen scenario.
• Score: 33.040248910330824
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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{https://github.com/Arandinglv/CoST}{this repo}.

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