Abstract

Wildfire perimeter mapping currently relies on deferred processing of data from manned and orbital platforms using hand-tuned physics-based models. We demonstrate real-time on-board multispectral data processing on cost-efficient unmanned aerial platforms using ML-based semantic segmentation.

Metadata

publication

Applied Industrial Spectroscopy, FD1. 7, 2024

year

2024

publication date

2024/7/15

authors

Andrew Rittenbach, Connor Imes, John Paul Walters

link

https://opg.optica.org/abstract.cfm?uri=AIS-2024-FD1.7

resource_link

https://preprints.opticaopen.org/articles/preprint/Timely_Wildfire_Perimeter_Mapping_for_Unmanned_Aerial_Platforms/24198333/1/files/42465246.pdf

conference

Applied Industrial Spectroscopy

pages

FD1. 7

publisher

Optica Publishing Group