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

Abstract

Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response (HADR) operations. Pairing sUAS with onboard artificial intelligence (AI) substantially extends their utility in covering larger areas with fewer support personnel. A variety of missions, such as search and rescue, assessing structural damage, and monitoring forest fires, floods, and chemical spills, can be supported simply by deploying the appropriate AI models. However, adoption by resourceconstrained groups, such as local municipalities, regulatory agencies, researchers, and indigenous persons, has been hampered by the lack of a cost-effective, readily-accessible baseline platform that can be easily adapted to their unique missions. To fill this gap, we have developed the fully free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS during local and beyond-line-of-site missions. We have emphasized a modular design with low-cost, readily-available components, open-source software, and thorough documentation (https: //kitware.github.io/adapt/). The system integrates an inertial navigation system, high-resolution color camera, computer, and wireless downlink to process imagery and broadcast georegistered analytics back to a ground station. Our goal is to make it easy for the HADR community to build their own copies of the ADAPT payload and leverage the thousands of hours of non-recurring engineering we have devoted to developing and testing this general-purpose capability. In this paper, we detail the development and testing of the ADAPT payload. We also demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide more-timely predictions of catastrophic flooding events. We deploy a novel active learning workflow to annotate river ice imagery, train a real-time deep neural network for ice segmentation, and demonstrate operation during field testing.