During a disaster, the most vulnerable communities are often disconnected from the decisions and processes that impact their ability to recover and strengthen their resiliency. Our tools consider how planners, community organizers, and nonprofit leaders can work together to accurately assess community needs and coordinate equitable emergency response.

Aerial imagery can provide insight on disaster events by collecting perishable data with temporal and geospatial qualities. These photos can then be evaluated for structural damage. Applying deep learning techniques such as image classification, object detection, or change detection can automate this analysis and improve rapid damage assessments. 

Machine Learning has emerged as a promising tool to increase the efficiency and accuracy of damage assessment through street view imagery. Our tools provide a revised machine learning framework which technicians can use to meet the equity and accuracy needs of current damage assessment and disaster management systems for faster recovery.