Key Findings for Small UAS for Post-Disaster Damage Assessment from the CURSE'26 Exercise

Key points

• 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.1
• 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.1
• 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.1

Sources (1)

• [1]Key Findings for Small UAS for Post-Disaster Damage Assessment from the CURSE'26 Exercise
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:06 PM

  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.

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