In areas like the Caribbean that face considerable risk from natural hazards like earthquakes, hurricanes, and floods, these forces of nature can have a devastating effect. This is especially true where houses and buildings are not up to modern construction standards, often in poor and informal settlements. While buildings can be retrofit to better prepare them for disaster, the traditional method for identifying high-risk buildings involves going door to door by foot, taking weeks if not months and costing millions of dollars.
This is where AI can help. WeRobotics and the World Bank Global Program for Resilient Housing have teamed up to prepare aerial drone imagery of buildings across the Caribbean annotated with characteristics that matter to building inspectors. One particularly relevant characteristic is roof construction material. Roof material is one of the main risk factors for earthquakes and hurricanes and a predictor of other risk factors, like building material, that are as not readily seen from the air.
In this challenge, your goal is to use provided aerial imagery to classify the roof material of identified buildings in St. Lucia, Guatemala, and Colombia. Machine learning models that are able to most accurately map disaster risk from drone imagery will help drive faster, cheaper prioritization of building inspections and target resources for disaster preparation where they will have the most impact.
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