Post-event structural damage assessment from images

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Rapidly classifying images immediately after a natural disaster such as a hurricane or an earthquake is key to**emergency management and forensic assessment efforts. Such images from disaster zones, which often include**thousands of images, are valuable sources of information that could inform decision-making regarding the**allotment of resources and prioritization of relief efforts. While crowdsourcing-supported schemes, like that**applied in Mexico following the 2017 Central Mexico earthquake, expedite the overall assessment process,**crowd-sourced data can be unreliable or incomplete, and can lead to inconsistencies between assessments**results. Additionally, the typical processes for reviewing images are manual and time-consuming. Methods for**accelerating and improving the consistency of the damage assessment process can yield invaluable returns such**as rapid response and prioritization of insurance claims. This proposal aims to develop methods and a software**tool to streamline the post-event structural damage assessment process using computer vision and machine**learning techniques to enable rapid and automated processing of large volumes of images. Outcomes of this**project have both commercial value to the industry partner in terms of enhanced and new offerings of**consulting services and significant societal value by being able to rapidly respond to large-scale disasters such**as floods, snowstorms or earthquakes.