Summary of Flame 3 Dataset: Unleashing the Power Of Radiometric Thermal Uav Imagery For Wildfire Management, by Bryce Hopkins et al.
FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management
by Bryce Hopkins, Leo ONeill, Michael Marinaccio, Eric Rowell, Russell Parsons, Sarah Flanary, Irtija Nazim, Carl Seielstad, Fatemeh Afghah
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study introduces methods for collecting and processing synchronized visual spectrum and radiometric thermal imagery using unmanned aerial vehicles (UAVs) at prescribed fires. The authors present a pipeline that simplifies and automates each step from data collection to neural network input. They also introduce the FLAME 3 dataset, which includes radiometric thermal Tag Image File Format (TIFFs) and nadir thermal plots, providing a new data type and collection method. This dataset aims to spur machine learning models utilizing radiometric thermal imagery for tasks such as aerial wildfire detection, segmentation, and assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps improve artificial intelligence for managing wildfires from the air. It develops a way to collect and process images taken by small planes or drones during controlled burns. This will make it easier to develop computer models that can detect and track wildfires. The study also introduces a new dataset of images, which can be used to train these computer models. |
Keywords
» Artificial intelligence » Machine learning » Neural network
