Context-Aware Change Detection With Semi-Supervised Learning
Ritu Yadav Andrea Nascetti Yifang Ban
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.