The field of remote sensing change detection is moving towards the development of more unified and general frameworks that can adapt to multiple change detection tasks. This is driven by the need to eliminate the requirement for specialized decoders and to accommodate different output granularities. Recent work has focused on introducing novel architectural paradigms, such as state space models and frequency change prompt generators, to improve the accuracy and robustness of change detection methods. Another key area of development is the use of pre-trained models and multi-scale cross-attention mechanisms to address the challenges of damage assessment in conflict zones. Noteworthy papers in this area include UniRSCD, which proposes a unified change detection framework, and CSD, which introduces a new task of change semantic detection. Additionally, papers such as ChessMamba and TaCo have made significant contributions to the field by proposing structure-aware interleaving of state spaces and spatio-temporal semantic consistent networks, respectively. SAM Guided Semantic and Motion Changed Region Mining is also a notable work that explores the use of foundation models to extract region-level representations for remote sensing change captioning.