SBIR-STTR Award

Fraym proposes to combine our 1x1km human geography data with publicly available event data in our machine learning models to inform Air Force threat prediction approaches. For this effort we seek to prove out the benefit of using population characteristic data and publicly available event data to model for emerging crises. We will employ our standard best practices in machine learning around data and threat prediction in geographies of high interest to the Air Force and subordinate organizations. The final output will be a scalable algorithm and data model that could translate to other risk-based scenarios with a phase II goal of replication in a new country without current data coverage. Fraym provides geospatially enabled localized data on populations across 69 data poor countries and growing. We leverage professionally enumerated survey data combined with over 40 remotely sensed geospatial covariates in our machine learning models creating predicted surfaces of more than 60 human indicators. By rapidly processing publicly available data combined with our standard data layers, we have successfully created risked based models for issues like risk for terrorism, social unrest, and political violence all of which are essential to monitoring points of interest, mission planning, and overall situational awareness.

We are interested in attending the pitch day. Fraym will create geospatially-enabled information on local population characteristics to inform Air Force (AF) missions in critical AORs. In addition to providing rich human data, Fraym will work with AF customers to build predictive models that anticipate threats or significant events in these AORs. Specifically, Fraym will produce and deliver 60+ categories of high-resolution population information in at least two geographies of interest for AFWIC and other AF/DoD stakeholders. We will combine survey data with satellite imagery via advanced machine learning models to create local population data for each AOR. We will also develop models that use this human data plus open-source or government-furnished data to anticipate threats/significant events. In commercial work, we have successfully used this approach to assess risk of terrorism, unrest, and political violence. We anticipate that our approach, when tailored for AFWIC, will provide critical insight for training, mission planning, strategy development, situational awareness, and information campaigns. In particular, we will inform training curriculum and exercises for the newly developed information operations career field within AF, supporting use case development, data integration, and methodologies. We will also explore applications for other AF/DoD stakeholders.