Problem
Research questions and friction points this paper is trying to address.
geospatial representation learning
cross-region generalization
semantic interpretability
multi-temporal urban imagery
CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment
🤖 AI Summary
为解决跨区域泛化和语义可解释性问题，CoST通过时空对齐方法从卫星图像中学习地理空间表示，提升城市分析性能。
📝 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}.
Innovation
Methods, ideas, or system contributions that make the work stand out.
contrastive learning
spatial-temporal alignment
semantic-aware
geographic regularities
cross-region generalization
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