Engineering topics

Joseph Coughlan

Publications and source records attributed to Joseph Coughlan.

2 records

NASA NTRS2024

In-Time Safety Management for Part 139 Airports

Today’s airports are complex multi-faceted ecosystems. Currently, of the 517 certificated airports, 270 are required to use safety management systems (SMSs) to identify and mitigate known hazards and emergent risks and to voluntarily share safety data with commercial operators and tenants. Airports manage a wide variety of hazards. These traffic hubs have direct responsibilities, such as removing foreign object debris from runways and taxiways and configuring runways to help prevent against incursions and tail strikes during takeoff. To ensure safety in the future NAS, the National Academies recommended an In-time Aviation Safety Management System (IASMS). An IASMS will employ services, functions, and capabilities (SFCs) to identify and mitigate hazards that are proactively and predictively managed based on data analytics of detected anomalies, precursors, and trends. SFCs would scale with airport complexity and environmental conditions using increasingly automated systems to respond proactively to hazards and, by using integrated data sources and predictive safety analytical methods, discover new, never before seen risks.

IASMS↗

NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗