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A Lightweight, Robust Exploitation System for Temporal Stacks of UAS Data: Use Case for Forward-Deployed Military or Emergency Responders

Spatial Sciences Institute, University of Southern California Dana and David Dornsife College of Letters, Arts and Sciences, Los Angeles, CA 90089, USA
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Drones 2019, 3(1), 29; https://doi.org/10.3390/drones3010029
Received: 21 February 2019 / Revised: 16 March 2019 / Accepted: 20 March 2019 / Published: 22 March 2019
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Abstract

The availability and precision of unmanned aerial systems (UAS) permit the repeated collection of very-high quality three-dimensional (3D) data to monitor high-interest areas, such as dams, urban areas, or erosion-prone coastlines. However, challenges exist in the temporal analysis of this data, specifically in conducting change-detection analysis on the high-quality point cloud data. These files are very large in size and contain points in varying locations that do not align between scenes. These large file sizes also limit the use of this data for individuals with low computational resources, such as first responders or forward-deployed soldiers. In response, this manuscript presents an approach that aggregates data spatially into voxels to provide the user with a lightweight, web-based exploitation system coupled with a flexible backend database. The system creates a robust set of tools to analyze large temporal stacks of 3D data and reduces data size by 78%, all while being able to query the original point cloud data. This approach offers a solution for organizations analyzing high-resolution, temporal point-clouds, as well as a possible solution for operations in areas with poor computational and connectivity resources requiring high-quality, 3D data for decision support and planning. View Full-Text
Keywords: UAS; drone; 3D; voxel; point cloud UAS; drone; 3D; voxel; point cloud
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Marx, A.; Chou, Y.-H.; Mercy, K.; Windisch, R. A Lightweight, Robust Exploitation System for Temporal Stacks of UAS Data: Use Case for Forward-Deployed Military or Emergency Responders. Drones 2019, 3, 29.

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