Search for: All records

This paper proposes a flood risk visualization method that is (1) readily transferable (2) hyperlocal, (3) computationally inexpensive, and (4) geometrically accurate. This proposal is for risk communication, to provide high-resolution, three-dimensional flood visualization at the sub-meter level. The method couples a laser scanning point cloud with algorithms that produce textured floodwaters, achieved through compounding multiple sine functions in a graphics shader. This hyper-local approach to visualization is enhanced by the ability to portray changes in (i) watercolor, (ii) texture, and (iii) motion (including dynamic heights) for various flood prediction scenarios. Through decoupling physics-based predictions from the visualization, a dynamic, flood risk viewer was produced with modest processing resources involving only a single, quad-core processor with a frequency around 4.30 GHz and with no graphics card. The system offers several major advantages. (1) The approach enables its use on a browser or with inexpensive, virtual reality hardware and, thus, promotes local dissemination for flood risk communication, planning, and mitigation. (2) The approach can be used for any scenario where water interfaces with the built environment, including inside of pipes. (3) When tested for a coastal inundation scenario from a hurricane, 92% of the neighborhood participants found it to be more effective in communicating flood risk than traditional 2D mapping flood warnings provided by governmental authorities. 

Abstract Street view imagery databases such as Google Street View, Mapillary, and Karta View provide great spatial and temporal coverage for many cities globally. Those data, when coupled with appropriate computer vision algorithms, can provide an effective means to analyse aspects of the urban environment at scale. As an effort to enhance current practices in urban flood risk assessment, this project investigates a potential use of street view imagery data to identify building features that indicate buildings’ vulnerability to flooding (e.g., basements and semi-basements). In particular, this paper discusses (1) building features indicating the presence of basement structures, (2) available imagery data sources capturing those features, and (3) computer vision algorithms capable of automatically detecting the features of interest. The paper also reviews existing methods for reconstructing geometry representations of the extracted features from images and potential approaches to account for data quality issues. Preliminary experiments were conducted, which confirmed the usability of the freely available Mapillary images for detecting basement railings as an example type of basement features, as well as geolocating the features. 

Across coastal urban centres, underground spaces such as storage areas, transportation corridors, basement car parks, public facilities, retail & office and private spaces present a priority risk during flood events with respect to timely evacuation. However, these underground spaces are commonly not considered in urban flood prediction models, in many cases because the location and geometry of these underground spaces are often poorly known. In order to improve urban flood prediction models, various identified underground spaces have been included into the urban flood simulation presented in this paper. Here, the Software MIKE+ is adopted to simulate the coastal flood scenarios for the urban centre of the city of Belfast, Northern Ireland. In the simulation, unstructured triangular grids are used. Based on the numerical simulation, urban flood depth and flooding rates into the underground spaces can be obtained. Based on the comparison of simulated urban flood scenarios with and without underground spaces, the impact of underground spaces on street-level inundation and flood routing is evaluated. It can be observed that the inclusion of underground space has a significant impact on the flood routing process. Moreover, the underground spaces also present priority risk areas during flood events with respect to timely evacuation and to this end, underground spaces cannot be ignored in real urban flood prediction. The presented study can be used to increase communities’ emergency preparedness and flood resilience