Physical Sciences

Integration of contextual sensors into vehicle-borne mobile radiation detector systems delivers a rich description of the environment that could be used to estimate the complex and variable environmental gamma-ray backgrounds in urban areas. The predictions could potentially increase the sensitivity to illicit radiological and nuclear materials and could provide realistic inputs to urban radiological search simulations and algorithms. Recent work in this field has focused mainly on the predictive power of segmenting and classifying imagery from cameras and elected in its approach to aggregate the locations of gamma-ray interactions within the fielded detector array to a single point. This work builds upon the previous effort by leveraging LiDARs to create a 3D representation of the detector system and the surrounding scenery and demonstrates further improvement in the capability of attributing observed gamma-ray backgrounds to classes of surrounding materials.