Change detection using earth observation data plays a vital role in quantifying the impact of disasters in affected areas. While data sources like Sentinel-2 provide rich optical information, they are often hindered by cloud cover, limiting their usage in disaster scenarios. However, leveraging pre-disaster optical data can offer valuable contextual information about the area such as landcover type, vegetation cover, soil types, enabling a better understanding of the disaster's impact. In this study, we develop a model to assess the contribution of pre-disaster Sentinel-2 data in change detection tasks, focusing on disaster-affected areas. The proposed Context-Aware Change Detection Network (CACDN) utilizes a combination of pre-disaster Sentinel-2 data, pre and post-disaster Sentinel-1 data and ancillary Digital Elevation Models (DEM) data. The model is validated on flood and landslide detection and evaluated using three metrics: Area Under the Precision-Recall Curve (AUPRC), Intersection over Union (IoU), and mean IoU. The preliminary results show significant improvement (4\%, AUPRC, 3-7\% IoU, 3-6\% mean IoU) in model's change detection capabilities when incorporated with pre-disaster optical data reflecting the effectiveness of using contextual information for accurate flood and landslide detection.

翻译：利用地球观测数据进行变化检测在量化灾害影响方面具有重要作用。尽管Sentinel-2等数据源能提供丰富的光学信息，但云层覆盖常限制其在灾害场景中的应用。然而，利用灾前光学数据可提供区域土地利用类型、植被覆盖、土壤类型等有价值的上下文信息，有助于更深入理解灾害影响。本研究构建了一个模型，旨在评估灾前Sentinel-2数据对变化检测任务的贡献，重点关注受灾区域。所提出的上下文感知变化检测网络（CACDN）融合了灾前Sentinel-2数据、灾前与灾后Sentinel-1数据以及辅助数字高程模型（DEM）数据。该模型在洪水和滑坡检测任务中进行了验证，并采用三个指标评估：精确率-召回率曲线下面积（AUPRC）、交并比（IoU）及平均交并比（mIoU）。初步结果表明，融合灾前光学数据后，模型的变化检测能力显著提升（AUPRC提升4%，IoU提升3-7%，mIoU提升3-6%），验证了利用上下文信息进行精准洪水和滑坡检测的有效性。