Semantic change detection in remote sensing aims to identify land cover changes between bi-temporal image pairs. Progress in this area has been limited by the scarcity of annotated datasets, as pixel-level annotation is costly and time-consuming. To address this, recent methods leverage synthetic data or generate artificial change pairs, but out-of-domain generalization remains limited. In this work, we introduce a weak temporal supervision strategy that leverages additional temporal observations of existing single-temporal datasets, without requiring any new annotations. Specifically, we extend single-date remote sensing datasets with new observations acquired at different times and train a change detection model by assuming that real bi-temporal pairs mostly contain no change, while pairing images from different locations to generate change examples. To handle the inherent noise in these weak labels, we employ an object-aware change map generation and an iterative refinement process. We validate our approach on extended versions of the FLAIR and IAILD aerial datasets, achieving strong zero-shot and low-data regime performance across different benchmarks. Lastly, we showcase results over large areas in France, highlighting the scalability potential of our method.

翻译：遥感语义变化检测旨在识别双时相图像对之间的土地覆盖变化。由于像素级标注成本高昂且耗时，标注数据集的稀缺限制了该领域的发展。为解决这一问题，近期方法利用合成数据或生成人工变化对，但域外泛化能力仍有限。本文提出一种弱时序监督策略，该策略利用现有单时相数据集的额外时序观测，无需任何新标注。具体而言，我们通过在不同时间获取的新观测数据扩展单时相遥感数据集，并基于"真实双时相对大多无变化"的假设训练变化检测模型，同时通过配对不同位置的图像生成变化样本。为处理这些弱标签中的固有噪声，我们采用对象感知变化图生成与迭代优化流程。我们在扩展版FLAIR和IAILD航空数据集上验证了该方法，在不同基准测试中实现了优异的零样本与低数据区域性能。最后，我们展示了法国大范围区域的检测结果，凸显了本方法的可扩展潜力。