Modern forms of undersea, terrestrial, and aerial warfare largely depend on autonomous systems. Autonomous systems operate in a 3D world via sensors that The commercial marketplace (and DoD agencies) are rapidly transitioning away from 2D imagery and maps and embracing 3D/4D Geospatial Data as the new wave of the future. 3D datasets are being leveraged in many different areas, from long range reconnaissance to boots on the ground mission execution. From tactical warfare to strategic warfare. Whether at the tactical level or at the in Because of the tremendous growth in the usage of 3D data and the broad spectrum of different end users with differing levels of technical aptitude, it is increasingly important that these hybrid 3D datasets from different acquisition platforms (e.g. DigitalGlobe, Predator UAV, handheld drone) be analyzed for quality and fidelity in an agnostic, but repeatable way. Currently there are no existing commercial solutions that are capable of adequately characterizing, quantifying, and visualizing individual errors sources associated with the creation of different 3D geospatial data products (e.g. a 3D globe). PixElement aims to change that. We propose to build on our existing technology which currently serves the commercial geospatial mapping market and enhance it's ability to work with 3D data generated from government platforms. By creating a repeatable process and a robust visualization engine for interpreting error states, we can reduce the risk associated with efforts that depend on 3D data for decision making and build a better, and more trusted 3D environment. This topic is specifically geared towards two AFWERX Mission Focus Areas, namely ⢠Geospatial Intelligence/3D Imaging ⢠Solutions in multi-sensor data fusion to provide accurate, robust, and continuous situational awareness of the battlesp
