Curation and Dissemination of Complex Multi-modal Data Sets for Radiation Detection, Localization, and Tracking

TL;DR

This paper presents the PANDAWN sensor network in Chicago, a multi-modal data platform for radiological detection, with automated data curation and labeling to support advanced algorithms in nuclear security.

Contribution

It introduces the PANDAWN network, details its multi-modal data acquisition and automated curation pipeline, enabling new radiological detection and tracking methods.

Findings

Automated data curation pipeline implemented

Diverse multi-modal data streams collected and labeled

Studies leveraging curated data demonstrate its utility

Abstract

The PANDAWN sensor network in Chicago, IL, is a state-of-the-art test-bed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal data sets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms, and methods relevant across the nonproliferation mission space. This paper first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes, and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. We finally provide a short overview of different studies that leveraged the curated data sets.