Feasibility study of urban flood mapping using traffic signs for route optimization
This addresses flood emergency management by providing timely flood depth information for rescue teams and evacuees, though it is incremental as it builds on existing methods like crowdsourcing and A* search.
The paper tackled urban flood mapping by using crowdsourced photos of submerged stop signs to estimate floodwater depth via deep neural networks and image processing, then converted the data into flood inundation maps for A* search algorithm-based route optimization, enabling effective wayfinding during floods.
Water events are the most frequent and costliest climate disasters around the world. In the U.S., an estimated 127 million people who live in coastal areas are at risk of substantial home damage from hurricanes or flooding. In flood emergency management, timely and effective spatial decision-making and intelligent routing depend on flood depth information at a fine spatiotemporal scale. In this paper, crowdsourcing is utilized to collect photos of submerged stop signs, and pair each photo with a pre-flood photo taken at the same location. Each photo pair is then analyzed using deep neural network and image processing to estimate the depth of floodwater in the location of the photo. Generated point-by-point depth data is converted to a flood inundation map and used by an A* search algorithm to determine an optimal flood-free path connecting points of interest. Results provide crucial information to rescue teams and evacuees by enabling effective wayfinding during flooding events.