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Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies. Among different methods and technologies for aerial images, Unmanned Aerial Vehicles (UAVs) and drones are beneficial to collect information regarding the fire. This study provides an aerial imagery dataset using drones during a prescribed pile fire in Northern Arizona, USA. This dataset consists of different repositories including raw aerial videos recorded by drones' cameras and also raw heatmap footage recorded by an infrared thermal camera. To help researchers, two well-known studies; fire classification and fire segmentation are defined based on the dataset. For approaches such as Neural Networks (NNs) and fire classification, 39,375 frames are labeled ("Fire" vs "Non-Fire") for the training phase. Also, another 8,617 frames are labeled for the test data. 2,003 frames are considered for the fire segmentation and regarding that, 2,003 masks are generated for the purpose of Ground Truth data with pixel-wise annotation.
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OSL results
{"Abstract":["Dosimetry data, equivalent doses, and single grain post-infrared infrared stimulated luminescence (p-IR IRSL) ages from "Microcontinent Breakup and Links to Possible Plate Boundary Reorganization in the Northern Gulf of California, México". Also shown in Table S2 of publication's supplementary file."]}
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- Award ID(s):
- 1728145
- PAR ID:
- 10356811
- Publisher / Repository:
- UCLA Dataverse
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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