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However, the semantic gap between high-level map categories and low-level image details hinders the extraction of homogeneous features for robust temporal association in change detection. Unlike conventional approaches that either compare pixel-level visual similarity or propagate segmentation errors, \\textcolor{black}{we propose a novel framework, \\underline{La}nguage-\\underline{VI}sion \\underline{D}iscriminator for d\\underline{E}tecting changes, LaVIDE}, which bridges the semantic gap between high-level map categories and low-level image details using language as an intermediary. 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Unlike conventional approaches that either compare pixel-level visual similarity or propagate segmentation errors, \\textcolor{black}{we propose a novel framework, \\underline{La}nguage-\\underline{VI}sion \\underline{D}iscriminator for d\\underline{E}tecting changes, LaVIDE}, which bridges the semantic gap between high-level map categories and low-level image details using language as an intermediary. 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