Deep learning promises to help emergency responders find their way through disaster zones.
What’s new: MIT researchers developed a tool that maps where hurricanes and other calamities have wiped out roads, helping to show aid workers the fastest ways to get to people in need.
How it works: Since 2017, following hurricanes in the Carolinas, Florida, Texas, and Puerto Rico, aircraft equipped with lidar collected three-dimensional images of the devastated landscapes. Previously, the researchers analyzed the data manually to evaluate damage to roads. Now they’re building neural networks that get the job done faster.
- The team trained a network to identify roads in the lidar imagery (the blue map in the animation above). The model identified roads scanned in a Boston suburb with 87 percent accuracy.
- The researchers trained an unsupervised model to spot sharp changes in the roads’ elevation (the graph above with black background and green points). A horizontal line of elevated lidar points that crosses a road, for instance, could be a downed tree. A sudden drop in elevation could be a washed-out bridge.
- They merged the road and obstruction output into OpenStreetMap and built an algorithm that finds the optimal route from place to place while avoiding impassable roads.
Results: In tests, the group was able to fly a lidar mission, process data, and generate route-finding analytics in under 36 hours.
Why it matters: After disaster strikes, damaged infrastructure often thwarts efforts by emergency responders and relief groups to deliver food and medical care to those in need. The new system could help save lives.
We’re thinking: Lidar is just one of many rich sources of post-disaster data. Machine learning engineers with humanitarian impulses can also dig into satellite imagery, GIS data, and social media posts.