Re-mapping after natural disasters is critical for assessing damage, optimizing aid distribution, and more. Creating new maps is thus is a central element of any disaster response effort. At present, re-mapping involves time-intensive, manual approaches where mappers spend significant time labeling buildings and roads in imagery. Current efforts are too slow to aid immediate response efforts: for example, re-mapping of Puerto Rico by the Humanitarian Open Street Maps team and 5,300 volunteers took approximately two months. Automation or partial automation of mapping using artificial intelligence presents an exciting opportunity to accelerate the process.