Satellite imagery plays a crucial role in monitoring changes happening on Earth's surface and aiding in climate analysis, ecosystem assessment, and disaster response. In this paper, we tackle semantic change detection with satellite image time series (SITS-SCD) which encompasses both change detection and semantic segmentation tasks. We propose a new architecture that improves over the state of the art, scales better with the number of parameters, and leverages long-term temporal information. However, for practical use cases, models need to adapt to spatial and temporal shifts, which remains a challenge. We investigate the impact of temporal and spatial shifts separately on global, multi-year SITS datasets using DynamicEarthNet and MUDS. We show that the spatial domain shift represents the most complex setting and that the impact of temporal shift on performance is more pronounced on change detection than on semantic segmentation, highlighting that it is a specific issue deserving further attention.

翻译：卫星影像在监测地球表面变化、辅助气候分析、生态系统评估和灾害响应方面发挥着关键作用。本文针对卫星图像时间序列语义变化检测（SITS-SCD）问题展开研究，该任务同时涵盖变化检测与语义分割。我们提出了一种新型架构，该架构在现有最优方法基础上实现性能提升，具有更优的参数规模扩展性，并能有效利用长期时序信息。然而，在实际应用场景中，模型需要适应时空域偏移，这仍是当前面临的挑战。基于DynamicEarthNet和MUDS两个全球多年度SITS数据集，我们分别研究了时序偏移与空间域偏移的影响。研究表明：空间域偏移构成最复杂的应用场景；相较于语义分割任务，时序偏移对变化检测性能的影响更为显著，这凸显了该问题值得进一步关注的特异性。