The creation of high fidelity scenarios for disaster simulation is a major challenge for a number of reasons. First, the maps supplied by existing map providers tend to provide only road or building shapes and do not accurately model open spaces which people use to evacuate buildings, homes, or industrial facilities. Secondly, even if some of the data about evacuation routes is available, the real-world connection points between these spaces and roads and buildings is usually not well defined unless data from buildings’ owners can be obtained. Finally, in order to augment current maps with accurate spatial data, it would require either a good set of training data for a computer vision algorithm to define evacuation routes using pictures or a significant amount of manpower to directly survey a vast area. Against this background, we develop a novel model of geospatial data creation, called CollabMap, that relies on human computation. CollabMap is a crowdsourcing tool to get users contracted via Amazon Mechanical Turk or a similar service to perform micro-tasks that involve augmenting existing maps by drawing evacuation routes, using satellite imagery from Google Maps and panoramic views from Google Street-View. We use human computation to complete tasks that are hard for a computer vision algorithm to perform or to generate training data that could be used by a computer vision algorithm to automatically define evacuation routes.