Gathering infrastructure data in emergency situations is challenging. The affected by a disaster areas are often large and the needed observations numerous. Spaceborne remote sensing techniques cover large areas but they are of limited use as their field of view may be blocked by clouds, smoke, buildings, highways, etc. Remote sensing products furt hermore require specialists to collect and analyze the data. This contrasts the nature of the damage detection problem: almost everyone is capable of observing whether a street is usable or not. The crowd is fit for solving these challenges as its members are numerous, they are willing to help and are often in the vicinity of the disaster thereby forming a highly dispersed sensor network. This paper proposes and implements a small WebGIS application for performing shortest path calculations based on crowds ourced information about the infrastructure health. The application is built on top of Google M aps and uses its routing service to calculate the shortest distance between two locations. Impassable areas are indicated on a map by people performing in-situ observations on a mobile device, and by users on a desktop machine who consult a multitude of information sources.
Nedkov and Zlatanova performed a shortest-path calculation using crowdsourced data about infrastructure health REF .
GOOGLE MAPS FOR CROWDSOURCED EMERGENCY ROUTING
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Engineering