Integration of contextual sensors into vehicle-borne mobile radiation detection systems delivers a rich description of the environment to inform estimates of the complex and variable gamma-ray signals observed in urban areas. Models based on these data streams could provide realistic inputs to urban radiological search algorithms and potentially improve the system's sensitivity to detect illicit radiological and nuclear materials. In this work, LiDAR and inertial data are combined using simultaneous localization and mapping techniques to create a three-dimensional (3D) representation of the surrounding scenery. Semantic segmentation of concurrently collected video imagery enables the division of the 3D model into distinct material categories. The radioactive flux of surfaces associated with these categories are inferred through maximum likelihood estimation maximization and the activity of the three most common isotopes (K-40, U-238 series, and Th-232 series) in the respective materials is predicted. The results, found to be in agreement with ground truth measurements performed at the facility, suggest that it is possible to quickly infer the composition of naturally occurring materials in structures that comprise a radiological scene. Such a capability could be used to inform radiological search algorithms and enable data-driven modeling of radiological search problems, which could facilitate system testing and operator training activities.

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.