38 citations · 60 across the 15 of their papers we have counts for
15 papers
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…
Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response
Thomas Manzini, Priyankari Perali, Robin R. Murphy
This paper presents the first AI/ML system for automating building damage assessment in uncrewed aerial systems (sUAS) imagery to be deployed operationally during federally declare…
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
Thomas Manzini, Priyankari Perali, Robin R. Murphy +1
This paper details four principal challenges encountered with machine learning (ML) damage assessment using small uncrewed aerial systems (sUAS) at Hurricanes Debby and Helene that…
Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery
Thomas Manzini, Priyankari Perali, Jayesh Tripathi +1
This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15,814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.02% label dis…
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…
Improving Drone Imagery For Computer Vision/Machine Learning in Wilderness Search and Rescue
Robin Murphy, Thomas Manzini
This paper describes gaps in acquisition of drone imagery that impair the use with computer vision/machine learning (CV/ML) models and makes five recommendations to maximize image…
Differentiable Boustrophedon Paths That Enable Optimization Via Gradient Descent
Thomas Manzini, Robin Murphy
This paper introduces a differentiable representation for the optimization of boustrophedon path plans in convex polygons, explores an additional parameter of these path plans that…
Open Problems in Computer Vision for Wilderness SAR and The Search for Patricia Wu-Murad
Thomas Manzini, Robin Murphy
This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of dron…
Quantitative Data Analysis: CRASAR Small Unmanned Aerial Systems at Hurricane Ian
Thomas Manzini, Robin Murphy, David Merrick
This paper provides a summary of the 281 sorties that were flown by the 10 different models of small unmanned aerial systems (sUAS) at Hurricane Ian, and the failures made in the f…
Wireless Network Demands of Data Products from Small Uncrewed Aerial Systems at Hurricane Ian
Thomas Manzini, Robin Murphy, David Merrick +1
Data collected at Hurricane Ian (2022) quantifies the demands that small uncrewed aerial systems (UAS), or drones, place on the network communication infrastructure and identifies…
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings
Thomas Manzini, Yao Chong Lim, Yulia Tsvetkov +1
Online texts -- across genres, registers, domains, and styles -- are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, per…
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities
Hai Pham, Paul Pu Liang, Thomas Manzini +2
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimod…
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
Hai Pham, Thomas Manzini, Paul Pu Liang +1
Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representa…