PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments
Author:
Affiliation:
1. Clemson University,Department of Electrical and Computer Engineering,Clemson,SC,USA
2. Clemson University,Department of Industrial Engineering,Clemson,SC,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10644130/10644150/10644894.pdf?arnumber=10644894
Reference14 articles.
1. 2010–2019: A landmark decade of us. billion-dollar weather and climate disasters;Smith;National Oceanic and Atmospheric Administration,2020
2. A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
3. Aerial imagery pile burn detection using deep learning: The FLAME dataset
4. Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset
5. Landscape fire simulation and fuel treatment optimization;Finney;Methods for integrating modeling of landscape change: Interior Northwest Landscape Analysis System. Gen. Tech. Rep. PNW-GTR-610. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station,2004
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