Machine Learning for Automated Weapon Selection & Battle Damage Assessments

Athenium Analytics’ enterprise, decision-support software leverages deep Machine Learning (ML) and Computer Vision (CV) to measure, mitigate, and manage risk through imagery analytics. By adapting its commercially-successful facility damage assessment tools (i.e. confirming desired effects on target), Athenium Analytics’ capabilities suite will be able to provide automated Battle Damage Assessments (BDA) to Air Force ISR elements. The company’s technology can also be leveraged to accelerate and optimize weapon and/or munitions selection. Athenium Analytics R/R&D with an Air Force end-user will lead to the production of BDA reports algorithmically, using high-resolution, panchromatic, multi-spectral, and/or electro-optical aerial imagery, significantly shortening the BDA delivery cycle to less than 24 hours after imagery is received from the Air Force, with the ability to assess large volumes of target locations simultaneously. This revolutionary application and adaptation of productized ML and CV assessment technology to the Advanced Battle Management System (ABMS) program will represent a significant increase to warfighter safety, reduce latency during multiple stages of a mission, inform weapon selection automation in future operations, and translate into significant cost savings. The adaptation of the non-defense commercial solution of IRIS through R/R&D for the Air Force will require modifications to the COTS solution’s current product workflows, technical advancements in ML and CV algorithms by way of supervised and unsupervised training, and the development of new predictive structural damage insights. The required developments would also allow for the integration of Air Force-provided imagery into IRIS’ image annotation workflow.