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Small unmanned aircraft systems (sUAS) are becoming prominent components ofmany humanitarian assistance and disaster response (HADR) operations. PairingsUAS with onboard artificial intelligence (AI) substantially extends theirutility in covering larger areas with fewer support personnel. A variety ofmissions, such as search and rescue, assessing structural damage, andmonitoring forest fires, floods, and chemical spills, can be supported simplyby deploying the appropriate AI models. However, adoption byresource-constrained groups, such as local municipalities, regulatory agencies,and researchers, has been hampered by the lack of a cost-effective,readily-accessible baseline platform that can be adapted to their uniquemissions. To fill this gap, we have developed the free and open-source ADAPTmulti-mission payload for deploying real-time AI and computer vision onboard asUAS during local and beyond-line-of-site missions. We have emphasized amodular design with low-cost, readily-available components, open-sourcesoftware, and thorough documentation (https://kitware.github.io/adapt/). Thesystem integrates an inertial navigation system, high-resolution color camera,computer, and wireless downlink to process imagery and broadcast georegisteredanalytics back to a ground station. Our goal is to make it easy for the HADRcommunity to build their own copies of the ADAPT payload and leverage thethousands of hours of engineering we have devoted to developing and testing. Inthis paper, we detail the development and testing of the ADAPT payload. Wedemonstrate the example mission of real-time, in-flight ice segmentation tomonitor river ice state and provide timely predictions of catastrophic floodingevents. We deploy a novel active learning workflow to annotate river iceimagery, train a real-time deep neural network for ice segmentation, anddemonstrate operation in the field.

ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI