Publications

Peer-Reviewed Publications

Challenges and Research Directions from the Operational Use of a Machine Learning Damage Assessment System via Small Uncrewed Aerial Systems at Hurricanes Debby and Helene

Priyankari Perali*, Thomas Manzini*, Robin Murphy, and David Merrick. Challenges and Research Directions from the Operational Use of a Machine Learning Damage Assessment System via Small Uncrewed Aerial Systems at Hurricanes Debby and Helene. In 2025 International Conference on Robot & Human Interactive Communication. 2025 (*equal contribution)(To Appear)
Paper

Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters

Priyankari Perali*, Thomas Manzini*, Raisa Karnik, Mihir Godbole, Hasnat Abdullah, and Robin Murphy. Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters. In 2025 International Conference on Robot & Human Interactive Communication. 2025 (*equal contribution)(To Appear)
Paper

Now you see it, Now you don’t: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery

Thomas Manzini, Priyankari Perali, Jayesh Tripathi, and Robin Murphy. Now you see it, Now you don’t: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 1998-2008).
Paper

Three challenges in utilizing machine learning to predict human behavior from observational data

Thomas Manzini, Priyankari Perali, and Robin R Murphy. Three challenges in utilizing machine learning to predict human behavior from observational data. In Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pages 737–739, 2024.
Paper | Poster

A Visibility-Based Escort Problem

Priyankari Perali*, Lance G Fletcher*, Andrew Beathard, and Jason M O’Kane. A visibility-based escort problem. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4804 4811. IEEE, 2023. (*equal contribution)
Paper | Poster

Submitted and Pre-Prints

CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery

Thomas Manzini, Priyankari Perali, Raisa Karnik, and Robin Murphy. Crasar-u-droids: A large scale benchmark dataset for building alignment and damage assessment in georectified suas imagery. arXiv preprint arXiv:2407.17673, 2024.
Paper | Dataset