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.
Cite
@article{arxiv.2512.06225,
title = {Curation and Dissemination of Complex Multi-modal Data Sets for Radiation Detection, Localization, and Tracking},
author = {Nicolas Abgrall and Mark S. Bandstra and Reynold J. Cooper and Marco Salathe and Brian J. Quiter and Rajesh Sankaran and Yongho Kim and Sean Shahkarami},
journal= {arXiv preprint arXiv:2512.06225},
year = {2025}
}