A lack of on-the-ground information at the start of a humanitarian crises is a major obstacle to a quick, effective response. It is critical to know the location and best way to access affected populations. This is particularly tough in places where infrastructure and communications networks are limited even during the best of times.
Skai (formerly known as RUDA) uses Artificial Intelligence (AI) and satellites to help WFP reduce the amount of time needed to understand the impact of disasters.
By using Artificial Intelligence (AI) to analyze images provided by satellites, WFP can respond to emergencies without the delays and logistics obstacles associated with a traditional response. Skai dramatically speeds up the process of determining what has happened, what to expect on the ground, and help define the kind of support that is required.
For example, when a tropical cyclone hits a low-lying coastal region—quick assessment of everything from structural damage, to transportation networks, in addition to basic information on the lay of the land, is paramount when preparing for action.
Gathering this information by traditional means, such as sending in a human team, might take weeks. But it doesn’t have to be this way— and Skai is leading the change.
With the appropriate use of satellite imagery and image analysis powered by AI—humanitarian disaster response has the potential to be both faster and better aligned to the needs of affected populations.
Satellites are already widely used to provide broad, regional coverage of phenomena such as climate-related crop failures, post-storm housing damage, and the movement of people during political conflicts. Drones, whether remotely controlled or fully-autonomous, are increasingly being used to fill in the gaps at a more local level, tracking changes that might happen over the course of hours or days.
The big hurdle is that all this visual data needs to be interpreted. Today, for instance, imagery collected from a 20-minute drone flight takes a highly trained, remote sensing analyst half a day’s work to analyze. The monotony of the workload can introduce inconsistency, fatigue-based and other human errors, and limited opportunities for internal audits and improved accountability.
Skai has the potential to cut this down to mere seconds by moving the initial image analysis over to a machine-learning platform. Using AI, images will be scanned in near-real time, areas of particular interest will be immediately sent to human analysts for a closer look, and every event will be an opportunity to better understand the response and better train the platform for future crises.
Working with a robust training and evaluation set is the foundation of a machine learning model able to recognize such things as damaged buildings and infrastructure. Such an extensible platform may eventually integrate additional assessments, such as movement of people, crop failures, and other variables.
The ambition for Skai is to build an AI-powered platform able to function across a variety of geographic locations, lighting conditions, disasters, and damage types. Skai is unique in having been trained with 27 past-onset disasters that were previously tagged by manual analysts. Early planning for an in-field pilot is currently underway.
We are seeking additional funding in order to finalize Skai product development, and integrate the platform into the disaster assessment process of WFP. This funding will also help us integrate Skai into the broader humanitarian community.