Thomas Manzini, Stephen Johnson, Priyankari Perali, David Merrick, Robin Murphy · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.29777 · cited by 0 works (OpenAlex) · open-access PDF
Der CLARKE-Bau- und Straßenverletzungsdatenklassifizierer wurde in die Katastrophal Unmanned Remote Sensing Exercise (CURSE) 2026 integriert, die 23 kleine unbewaffnete Luftsysteme (sUAS) -Teams von staatlichen und lokalen Behörden und Nichtregierungsorganisationen in Florida und Missouri ausgebildet hat, um Bilder für eine Unfallbefehl Remote Sensing Cell zu sammeln. Obwohl CLARKE in zwei Hurrikane (Debby und Helene) operativ eingesetzt wurde, präsentierte CURSE'26 eine Gelegenheit, das Machine Learning-Modell mit einem größeren Satz nie zuvor gesehener orthomosaics in Florida und Missouri zu bewerten. Die Ergebnisse zeigen, dass die Bewertung von
Machine translation (Meta M2M100); the English original follows.
The CLARKE building and road damage assessment AI classifier was incorporated into the Catastrophic Unmanned Remote Sensing Exercise (CURSE) 2026, which trained 23 small uncrewed aerial systems (sUAS) teams from state and local agencies and non-governmental organizations in Florida and Missouri on how to collect imagery for an incident command Remote Sensing Cell. Although CLARKE has been operationally deployed to two hurricanes (Debby and Helene), CURSE'26 presented an opportunity to evaluate the machine learning model with a larger set of never-before-seen orthomosaics. The results show that building and road damage assessment together ran in 3.5 minutes on average, and that building damage assessment achieved a Micro F1 of 0.834 with no manual adjustments to correct spatial misalignment and delivered that performance in both Florida and Missouri, demonstrating good geographic transfer. It also showed that road damage assessment performed poorly due to sensitivity to misalignment between road maps and the images, and to lighting and environmental conditions in an urban setting. The analysis of CURSE'26 presents five other findings of value to the sUAS community: that the use of sUAS and machine learning is now operationally normalized and accepted, sUAS teams in the field can improve classification accuracy by timing their flights, exercises are good operational tests but their value for evaluating learning models is less clear, edge computing is needed to overcome the wireless upload barrier, and the application of sUAS and AI to disaster response has ethical and legal ramifications for pilots and data managers.
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Topics: Upload, Computer science, Key (lock), Classifier (UML), Risk assessment, Artificial intelligence
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