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Unmanned Aerial Vehicles (UAVs) are now being increasingly used in humanitarian efforts. UAVs provide humanitarians with a "bird's eye" view of the disaster-affected areas which need immediate help. The images can be used to assess the overall level of damage inflicted on the distressed areas as well as the most effective allocation of the limited relief resources by humanitarian organizations. Analyzing large volumes of high-resolution aerial images generated after a major disaster remains a challenging task in contrast to the ease of acquiring them due to low operational costs. For this purpose, we plan to combine human computation with machine learning to build AI models that can automatically detect damaged buildings in incoming images.

We need your help to quickly identify which buildings have been damaged in order to train AI models that can accelerate recovery efforts in disaster stricken regions!

Here's a link to a detailed tutorial Step-by-Step Image Annotation Process; in order to understand the steps required to annotate images.

Many thanks for volunteering your time as a digital humanitarian.