发表机构
Beihang University; Shanghai Artificial Intelligence Laboratory（北京航空航天大学; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有多时相遥感变化检测方法难以表征反复变化区域的局限，提出UBDD并构建FootprintNet，结合状态转换约束与边界线索学习动态变化轨迹，引入BCDS评估，在多数据集上优于SOTA方法。
AI 中文摘要
尽管遥感多时相变化检测（MTCD）已取得显著进展，但大多数现有 MTCD 方法仍采用与最终观测结果关联的单一变化类别来表征整个观测周期内每个空间位置的动态过程。这种隐含的单一变化假设限制了它们对与人类活动密切相关的反复变化区域的表征能力。为解决这一局限，我们提出城市建筑动态检测（UBDD），该方法能从多时相影像中识别建筑变化动态 footprint（即变化发生的时间区间），并生成逐像素分类掩码。对于经历两次或更多次变化的区域，UBDD 引入独立的多变化类进行统一表征，从而实现单一和多变化过程的统一建模。此外，我们提出 FootprintNet，将建筑变化过程抽象为隐状态与动作之间的交互，并施加状态-动作转换约束以指导因果一致的变化轨迹学习；该模型还利用时间变化边界线索增强边界两侧的特征对比度，进而提升不同动态 footprint 间的区分度，实现动态 footprint 的准确检测。再者，我们引入建筑变化动态评分（BCDS），以解决传统指标无法反映预测 footprint 与标签间时间接近度的问题，该指标根据预测对变化语义的保留程度及与对应标签的时间偏移来评估预测结果。在 TSCD、MUDS 和 WUSU 上开展的大量实验表明，FootprintNet 的性能优于当前最先进方法，代码可在该 https URL 获取。
英文摘要
Despite substantial progress in remote sensing multi-temporal change detection (MTCD), most existing MTCD methods still represent the dynamic process at each spatial location over the entire observation period using a single change category associated with the final observation. This implicit single-change assumption limits their ability to characterize regions of recurrent change closely related to human activities. To address this limitation, we introduce Urban Building Dynamics Detection (UBDD), which identifies building-change dynamic footprints, i.e., the temporal intervals in which changes occur, from multi-temporal imagery and produces pixel-wise classification masks. For regions undergoing two or more changes, UBDD introduces an independent multi-change class for unified representation, thereby enabling unified modeling of single- and multi-change processes. Furthermore, we propose FootprintNet, which abstracts building-change processes as interactions between latent states and actions, and imposes state-action transition constraints to guide the learning of causally coherent change trajectories. It further exploits temporal change-boundary cues to enhance feature contrast across boundary sides, thereby improving the discrimination among different dynamic footprints and enabling accurate detection of dynamic footprints. Moreover, we introduce the Building Change Dynamics Score (BCDS) to address the inability of conventional metrics to reflect the temporal proximity between predicted footprints and labels. It evaluates predictions according to their preservation of change semantics and temporal offsets from the corresponding labels. Extensive experiments on TSCD, MUDS, and WUSU demonstrate that FootprintNet outperforms current state-of-the-art methods. The code is available at https://github.com/zmoka-zht/FootprintNet.
