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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. 

Current state-of-the-art point cloud data management (PCDM) systems rely on a variety of parallel architectures and diverse data models. The main objective of these implementations is achieving higher scalability without compromising performance. This paper reviews the scalability and performance of state-of-the-art PCDM systems with respect to both parallel architectures and data models. More specifically, in terms of parallel architectures, shared-memory architecture, shared-disk architecture, and shared-nothing architecture are considered. In terms of data models, relational models, and novel data models (such as wide-column models) are considered. New structured query language (NewSQL) models are considered. The impacts of parallel architectures and data models are discussed with respect to theoretical perspectives and in the context of existing PCDM implementations. Based on the review, a methodical approach for the selection of parallel architectures and data models for highly scalable and performance-efficient PCDM system development is proposed. Finally, notable research gaps in the PCDM literature are presented as possible directions for future research. 

State-of-the-art, scalable, indexing techniques in location-based image data retrieval are primarily focused on supporting window and range queries. However, support of these indexes is not well explored when there are multiple spatially similar images to retrieve for a given geographic location. Adoption of existing spatial indexes such as the kD-tree pose major scalability impediments. In response, this work proposes a novel scalable, key-value, database oriented, secondary-memory based, spatial index to retrieve the top k most spatially similar images to a given geographic location. The proposed index introduces a 4-dimensional Hilbert index (4DHI). This space filling curve is implemented atop HBase (a key-value database). Experiments performed on both synthetically generated and real world data demonstrate comparable accuracy with MD-HBase (a state of the art, scalable, multidimensional point data management system) and better performance. Specifically, 4DHI yielded 34% - 39% storage improvements compared to the disk consumption of the original index of MD-HBase. The compactness in 4DHI also yielded up to 3.4 and 4.7 fold gains when retrieving 6400 and 12800 neighbours, respectively; compared to the adoption of original index of MD-HBase for respective neighbour searches. An optimization technique termed “Bounding Box Displacement” (BBD) is introduced to improve the accuracy of the top k approximations in relation to the results of in-memory kD-tree. Finally, a method of reducing row key length is also discussed for the proposed 4DHI to further improve the storage efficiency and scalability in managing large numbers of remotely sensed images.