ABSTRACT
Accurately predicting local coastal flooding and its flow through the urban environment is highly complex. This is, in part, because of the density of data used to create the base model and how this data was collected. Spaces such as sub-surface car parking, basements, and pedestrian underpasses are not included in current flood models, and information regarding their location and volume is scarce and largely inaccessible. Additionally, underground spaces present a priority risk during flood events with respect to timely evacuation. Inadequate knowledge of these spaces causes significant logistical challenges in generating highly accurate flood maps of these priority risk areas. This case study, which is part of the UrbanARK project (www.urbanark-project.org), will focus on the use of relatively low-cost, handheld mobile laser scanning technology (HMLS) to map the urban environment and increase knowledge of the location and internal geometry of underground spaces. This data will then be used to refine flood prediction models. Our project partners will also use this data to develop immersive Virtual Reality applications as a communication tool to support communities and emergency planners.
