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UniChange: Unifying Change Detection with Multimodal Large Language Model

arXiv — cs.CL•Wednesday, November 5, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

UniChange: Unifying Change Detection with Multimodal Large Language Model

UniChange is a novel approach designed to enhance change detection by unifying multiple data modalities, addressing limitations found in existing models that typically rely on single-type datasets (F1, F3). The primary goal of UniChange is to improve the monitoring of land cover dynamics by combining diverse datasets, which is expected to increase both the accuracy and reliability of detecting changes (F2, F6). Unlike previous methods, UniChange integrates multimodal large language models to process and analyze varied data sources simultaneously (F4). This integration allows for a more comprehensive understanding of environmental changes, overcoming the constraints posed by single-modality approaches (F7). The approach has been proposed positively for its potential effectiveness in advancing change detection capabilities (A1). By leveraging multiple data types, UniChange represents a significant step forward in the field, promising improved performance in monitoring land cover changes (F5, F8).

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