Engineering topics

Paris Garrett

Publications and source records attributed to Paris Garrett.

2 records

NASA NTRS2021

Contextual Segmentation of Fire Spotting Regions Through Satellite-Augmented Autonomous Modular Sensor Image

Globally, forest fires remain a significant threat to human and environmental wellbeing. Towards mitigating the impacts of forest fires, it is critical that accurate and updated information regarding not only the fire line, but also nearby human settlements, vegetation, and water sources is reported quickly to emergency services. However, while existing UAS-based fire detection methods are effective, they largely do not report the contextual environmental information necessary to best serve nearby communities in disaster response. Additionally, modern advancements in deep learning offer new approaches for image segmentation which may improve classification accuracy beyond current pixel-wise indices. In this work, we benchmark the performance of these modern segmentation techniques in locating both fire lines and environmental features in historical Autonomous Modular Sensor imagery. Furthermore, we augment these outputs with satellite imagery segmentation towards developing a robust contextual mapping tool for rapid emergency fire response and decision making.

Nikhil Behari↗

NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗