SBIR-STTR Award

Enview's 3D Geospatial Analytics Engine's new Wildfire Risk module encapsulates core capabilities of exploiting Air Force space data to identify Wildfire threats, predict their spread, and enable first-responders and civilians to respond to fires. The capability will fuse space-based infrared, electro-optical (RGB) and multi-spectral imagery and weather sensing data, with ground and aerial collected LiDAR to build a wide-area 3D model of wildfire risk. Enview's 3D Computer Vision algorithms will detect vegetation density and health, electric powerlines & powerpoles, commercial and residential structures, and other infrastructure. It will use deep learning algorithms to predict the spread of wildfire, optimize plans to respond to current and future wildfires.machine learning,sensor fusion,artificial intelligence,wildfire,energy infrastructure,installation energy,space and missile defense,Computer Vision

The 821 Contingency Response Squadron (CRS) is highly-specialized at rapidly deploying personnel to quickly open airfields and establish, expand, sustain, and coordinate air mobility operations. The 821 CRS has a national defense-related mission need in the area of natural disaster relief. The specific challenge is that it is difficult to maintain access to current and actionable geographic information in a rapidly changing, wide-area, post-disaster environment. Further, these data are generated independently with different footprints and frequencies. It is a challenge to seamlessly fuse these disparate data sources for human consumption, and they represent an enormous volume of data that must be manually reviewed to extract operational insights. Enview's Geospatial AI platform will be adapted for use by 821 CRS to support their national defense-related mission need. The solution fuses recent, high-frequency space-based data onto pre-existing high-resolution 3D terrain baselines and leverages artificial intelligence and machine learning algorithms. This hybrid approach leverages the strengths of multiple, large scale heterogeneous datasets and fuses them, analyses them, and presents insights in an accessible manner for rapid and intuitive human consumption to enhance post-disaster decision making.