Multimodal Retrieval-Augmented Data Generation Engine

Intelligence, Surveillance, and Reconnaissance (ISR) operations are integral to modern Army missions. In recent years, the use of Artificial Intelligence and Machine Learning (AI/ML) models has become integral for ISR operations. The effectiveness of these models hinges on access to large-scale, high-quality sensor data with detailed ground-truth annotations. However, collecting real-world sensor data is often impractical due to time constraints, high costs, and restricted access to sensitive domains such as SIPR and JWICS networks. Existing synthetic data generation platforms are insufficient, producing low-fidelity, unrealistic imagery and requiring cumbersome, multi-step processes for context specification and data annotation. We introduce the Retrieval-Augmented Data Generation Engine (RADGE), a novel platform that automates the creation of high-fidelity multimodal synthetic sensor data—specifically infrared (IR) and RGB imagery—alongside precise object annotations. RADGE streamlines data generation through a zero-learning-curve, one-step workflow: users simply specify the desired context, and the platform autonomously generates both the synthetic sensor data and the associated annotations. This automation eliminates the need for manual intervention, significantly accelerating data preparation and enhancing operational efficiency. Our proposal aims to demonstrate the fidelity of the synthetic data produced by RADGE and validate the accuracy of its automatic object annotations. This project directly addresses two key areas from the A244-P037 topic solicitation: Synthetic Data Generation in a Format Applicable to a Given Situation and Retrieval-Augmented Generation (RAG) Techniques.  Through rigorous testing and validation, we will showcase RADGE's capability to produce hyper-realistic multimodal sensor data that meets the stringent requirements of AI/ML models used in ISR operations. By facilitating rapid, efficient, and cost-effective data generation, RADGE has the potential to revolutionize data preparation processes, enhance model training, and ultimately strengthen ISR capabilities in contested environments.