While UAVs provide a platform to efficiently collect remotely sensed image data, the data must be processed and analysed in order to extract meaningful information. This image analysis is normally achieved through visual interpretation by an experienced remote sensing analyst; a manual approach requires a very high workload to be carried out. It means that data often remains unused, because of time constraints. Automating or partially automating some of the interpretation with machine learning techniques would greatly reduce the time required to carry out the analysis and help get better information into the hands of emergency coordinators faster.
As part of the current pilot project Rapid UAVs for Data Analysis in Emergencies (RUDA), the machine learning software will be integrated with the data collection and analysis workflows, and be implemented at first instance in five countries prone to natural disasters, in which the UAV coordination model is being developed.
Since enhanced emergency response can be useful for many humanitarian actors, WFP invites interested organisations to reach out and explore opportunities for collaboration.