AI Framework for Multimodal Scene Construction and Data Generation

The DAF26BZ01-DV005 project seeks to develop an AI framework for multimodal scene construction and data generation, specifically focusing on geo-specific, sensor-independent scenes for RF and EO/IR synthetic data. This initiative aims to overcome limitations in existing models by leveraging geo-spatial, time-of-day, seasonal, and measured databases. The DoD requires these high-fidelity background scenes to advance autonomous systems and AI/ML capabilities, providing realistic environments for learning and adaptation. Key challenges include accurately modeling complex environmental factors, ensuring spatial and temporal consistency across multimodal data, and aligning RF and EO/IR perspectives for sensor fusion. The project emphasizes integrating geospatial data and supporting standardized scene formats compatible with existing simulation tools like FLITES and Xpatch. Phase I required demonstrated ability to build background scenes for RF or EO/IR simulation tools and generate geo-specific synthetic training data. Phase II involves developing prototype software that ingests geographic coordinates, sources representative data, and builds geo-specific background scene data compatible with both RF and EO/IR simulation tools, along with a standardized interface. Phase III focuses on incorporating classified data, refining accuracy, and commercializing the capability within the DoD and private sectors.