Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery

Population-based damage assessment is crucial for providing timely aid in case of natural hazards such as floods, tsunami, and hurricanes. One of the quickest methods to do this task is to employ remote sensing data and observe the damage. Most of the previous works have restricted themselves to damage detection and classification with respect to buildings. However, no framework exists that makes a conclusion about the overall damage done in an area affected by the hazard. In this work, we present an unsupervised density-based clustering algorithm that automatically makes spatial groups of affected regions and assigns the label based on the degree of damage to the region. The algorithm automatically selects the optimum number of clusters based on the spatial distribution of the data and works well with any shape of the hazard-affected region. The demographic estimate for the affected region is then presented based on the area of the region and the census data. The navigation information is provided with aid of Google Maps depicting the overall damage along with possibility of transportation. For evaluation of the framework, we employ xBD which is the largest annotated building damage classified data set till date. The results correctly identify the regions and perform extremely well on the silhouette score and the DB index.