2 citations · 3 across the 4 of their papers we have counts for
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Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment
Thomas Manzini, Priyankari Perali, Raisa Karnik +2
This paper presents the first known empirical investigation of annotator and reviewer performance across multi-source remotely sensed imagery, evaluating human labeling across dron…
A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery
Thomas Manzini, Priyankari Perali, Raisa Karnik +1
This paper presents the largest known benchmark dataset for road damage assessment and road alignment, and provides 18 baseline models trained on the CRASAR-U-DRIODs dataset's post…
CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery
Thomas Manzini, Priyankari Perali, Raisa Karnik +1
This document presents the Center for Robot Assisted Search And Rescue - Uncrewed Aerial Systems - Disaster Response Overhead Inspection Dataset (CRASAR-U-DROIDs) for building dama…
Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters
Thomas Manzini, Priyankari Perali, Raisa Karnik +3
This work presents the first quantitative study of alignment errors between small uncrewed aerial systems (sUAS) georectified imagery and a priori building polygons and finds that…