WFP is piloting an Artificial Intelligence prototypes to speed up the analysis of information gathered through Unmanned Aerial Vehicles (UAVs/drones) in the aftermath of crisis. 

Humanitarians are racing to uncover use cases that when merged with AI could spell significant efficiency gains and make a tangible impact on the lives of millions of people worldwide. From enhanced communication interfaces that give us a better understanding of the people we help to automated transport and logistics systems, organisations like WFP and its Innovation Accelerator are primed to take advantage of the technology to help us reach Zero Hunger faster. 

For the benefit of society, the use and development of AI must be balanced with a rights based approach; one that puts people’s needs first. The human-centred focus highlights the importance of matching high-tech solutions with real humanitarian challenges to rapidly advance the achievement of the SDGs by 2030. At the WFP Innovation Accelerator, we are looking for use cases, data sets or concepts that can be tested quickly ranging from areas of work such as climate change resilience, supply chain as well as geo-spatial analysis. To date we've only scratched the surface of what AI can achieve, but we think it has the power to transform the world we live and help provide a better, hunger free future.  

Rapid UAVs Data Analysis in Emergencies (RUDA)

With the support of the Accelerator, WFP’s IT Emergency Preparedness and Response Team is working on a project that aims to use AI to speed up the analysis of information gathered through Unmanned Aerial Vehicles (UAVs/drones) in a crisis, to enable better and faster decision making.Applying the best practices of human-centred design and lean start-up methodology, the project will employ machine learning to automate and accelerate analysis of UAV imagery during humanitarian response, efficiently collecting remotely sensed image data, processing and analysing it in order to extract meaningful information. This approach allows for the reduction of time-extensive manual analysis, allowing emergency response coordinators to access key data in a timely manner.