Sensor Synthetic Data Generation

Currently, nearly all of the AI/ML models are developed using actual or representative data. There is not enough unique defense/intel data available to create performant models (e.g. it takes roughly 50M pieces of data to create a 60-70% performant model). Additionally, this data must be labeled; synthetically generated data has the ability to be labeled as it is generated, reducing human data labeling effort for real-world data and data generated from an external (e.g., vendor) source. Sensor Synthetic Data Generation topic encompasses the development of a synthetic data generation tool for sensors (e.g. radar, etc.) that can augment the limited, labeled, training data available to support Artificial Intelligence / Machine Learning model development. The purpose of this topic is to lead to the creation/integration of mission-focused synthetic data to include but not be limited to: Priority Needs: Commercial Satellites/Electro Optical (EO) - World View 1,2,3 (Imagery), Digital Globe, Blacksky // Synthetic Aperture Radar (SAR) - RADARSAT and Capella; Other Needs: 0903 Full Motion Video (FMV) // Electronic Intelligence (ELINT) spectrums/waveforms // Variable Message Format (VMF) and Chat; Desired synthetic data to be used in AI/ML model development: Surface to Surface Radars, Surface to Air Missile Launchers, Tanks, Etc. Please note: labeled data is a critical input to model training and model test & eval.