26-103 Label-scarce VHR Disaster Mapping in the Era of Foundation Models

Mission

Very-high-resolution (VHR) satellite imagery plays a central role in disaster response, allowing the identification of collapsed buildings, inundated roads, and fire-affected areas at the scale of individual structures. Such fine-grained detail is essential for rapid humanitarian action and recovery planning. In recent years, deep learning (DL) has emerged as the dominant approach for analyzing VHR imagery, offering state-of-the-art performance in various Earth observation (EO) tasks. However, automatic VHR disaster mapping remains constrained by label scarcity, sensor heterogeneity, and model inefficiency [1]. 

First, label scarcity is the primary bottleneck. In disaster mapping, obtaining accurate labels is nearly impossible due to the infrequent and unique nature of catastrophic events. The available labels primarily rely on expert knowledge derived from past events, while expert annotation is very time-consuming and costly. Second, domain shi...