# Large Scale Organization and Inference of an Imagery Dataset for Public Safety

arXiv: https://arxiv.org/abs/1908.09006 · HTML: https://mlsanity.com/paper/1908.09006/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [An overview on the evaluated video retrieval tasks at TRECVID 2022](https://mlsanity.com/paper/2306.13118/) (2023)
- [LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery](https://mlsanity.com/paper/2406.02780/) (2024)
- [Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery](https://mlsanity.com/paper/2505.08117/) (2025)
- [Damage Estimation and Localization from Sparse Aerial Imagery](https://mlsanity.com/paper/2111.03708/) (2021)

## Abstract (arXiv)

Video applications and analytics are routinely projected as a stressing and significant service of the Nationwide Public Safety Broadband Network. As part of a NIST PSCR funded effort, the New Jersey Office of Homeland Security and Preparedness and MIT Lincoln Laboratory have been developing a computer vision dataset of operational and representative public safety scenarios. The scale and scope of this dataset necessitates a hierarchical organization approach for efficient compute and storage. We overview architectural considerations using the Lincoln Laboratory Supercomputing Cluster as a test architecture. We then describe how we intelligently organized the dataset across LLSC and evaluated it with large scale imagery inference across terabytes of data.