Deep Learning for High-Resolution Remote Sensing Change Detection: Task Taxonomy, Datasets, and Perspectives

Change detection (CD) aims to reveal the dynamic evolution of the Earth's surface by analyzing multi-temporal remote sensing imagery, exhibiting significant application value in critical domains such as emergency management. With the continuous improvement in the spatial and temporal resolution of remote sensing imagery, traditional CD methods based on handcrafted features are facing increasing limitations. These approaches have gradually become insufficient to meet the demands of increasingly complex and diverse application scenarios. In recent years, fueled by the powerful feature representation capabilities of various network architectures, deep learning-based methods have achieved remarkable breakthroughs in the field of CD. This progress has also given rise to a diverse range of task types, including binary change detection (BCD), multi-class change detection (MCD), semantic change detection (SCD), change captioning (CC), and change detection data synthesis (CDDS). However, most existing reviews primarily focus on model architectures or supervision strategies, lacking a systematic overview from the perspective of task taxonomy. This paper presents a task-oriented survey of deep learning-based CD methods for high-resolution remote sensing imagery. It comprehensively summarizes the progress, representative approaches, and application scenarios across different task categories, while also organizing widely used datasets and evaluation metrics. Furthermore, potential future directions are discussed to provide valuable insights for subsequent research.