CNN Based Approach for Post Disaster Damage Assessment

After any disaster, the Government rehabilitates the victims based on the severity of the damage caused to their properties. Since a huge number of rehabilitation claims flow in after the disaster, it takes up a lot of manual labor in inspecting and validating the claims along with deciding the amount of rehabilitation to be granted. Moreover, such manual inspection leads to a lack of transparency. In recent years, social media posts, text, and images have become a rich source of post-disaster situational information that may be useful in assessing damage at a low cost. Most of the existing research explores the use of social media text for extracting situational information useful for disaster response. The usage of social media images to assess disaster damage is limited. In this paper, we propose a convolutional neural network-based approach to locate damage in a disaster image and to quantify the degree of the damage. The proposed damage assessment system categorizes images of earthquake-affected buildings and decides the severity of the damage caused by the earthquake. Our proposed approach enables the use of social media images for post-disaster damage assessment and provides an inexpensive and feasible alternative to the more expensive GIS approach. Our approach exhibits high accuracy in classifying earthquake-affected buildings and determining the severity of damage at a negligible loss.