GTC Silicon Valley-2019: End-to-End Analysis of Large 3D Geospatial Datasets in RAPIDS
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GTC Silicon Valley-2019 ID:S9791:End-to-End Analysis of Large 3D Geospatial Datasets in RAPIDS
John Murray(Fusion Data Science)
Location intelligence is key to understanding areas such as property insights, environmental monitoring, disaster management and prevention, traffic flows, and customer behavior. We'll discuss our work involving Europe's property insurance sector, which has been disrupted by the growing use of comparison websites that require real-time quotations. To build deep learning models, large volumes of data from satellite images, 3D sensors, GPS-enabled devices, social media, and other sources must be merged using computationally intensive coordinate conversion and matching. We'll outline our solution, which uses 3D CNNs to estimate risk factors from color 3D virtual models of individual properties. We'll describe how we used RAPIDS and cover our entire process, from processing raw data, merging sources, generating and labeling colorized voxel cubes for training, to model building, inference, and final application.