The FLAME dataset: Aerial Imagery Pile burn detection using drones (UAVs)

Citation Author(s):
Alireza
Shamsoshoara
Northern Arizona University
Fatemeh
Afghah
Northern Arizona University
Abolfazl
Razi
Northern Arizona University
Liming
Zheng
Northern Arizona University
Peter
Fulé
Northern Arizona University
Erik
Blasch
Air Force Research Laboratory
Submitted by:
Alireza Shamsoshoara
Last updated:
Fri, 04/16/2021 - 15:41
DOI:
10.21227/qad6-r683
Data Format:
Links:
License:
4.75
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Abstract 

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 FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) dataset using drones during a prescribed pile burn 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.

The published article is available here:

https://www.sciencedirect.com/science/article/pii/S1389128621001201

The preprint article of this dataset is available here:

https://arxiv.org/pdf/2012.14036.pdf

More information about this study and the two machine learning challenges that we used is available here:

https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle

A sample video is available on YouTube:

https://www.youtube.com/watch?v=bHK6g37_KyA

To find other projects and articles in our group:

https://www.cefns.nau.edu/~fa334/

Comments

great!

Submitted by Luo Robbee on Thu, 05/27/2021 - 05:38

great

Submitted by wei gao on Mon, 06/21/2021 - 02:51

Thanks! It's really helpful.

Submitted by HYOJUN AHN on Thu, 06/24/2021 - 00:07

Please where can i find the labels and annotations of 7 and 8 for classification ?

Submitted by Aicha KHALFAOUI on Mon, 09/20/2021 - 08:06

I was gonna ask you the same question. Have you found it yet? Please let me know if you find it. Thank you

Submitted by yu bai on Fri, 12/24/2021 - 02:10

我也想要

Submitted by fan wu on Tue, 10/17/2023 - 22:52

我也想要

Submitted by fan wu on Tue, 10/17/2023 - 22:52

Please where can i find the labels and annotations of 7 and 8 for classification ?

Submitted by yu bai on Thu, 12/23/2021 - 00:29

I don't know how to thank you for this data. I was looking for it so long.

Submitted by Seyyed Mousavi on Mon, 06/20/2022 - 05:35

Why are the Masks annotations (repository 10) I downloaded for the segmentation task all full 0 matrices?

Submitted by Shipei liu on Wed, 07/27/2022 - 04:43

me too. Why ?

Submitted by jiajun li on Wed, 02/01/2023 - 03:29

Had the same problem until I realized they normalized the image so the values in the image are 0(backgorund) and 1(fire), instead of the presumed 255(white). So these binary masks look empty but the annotations are there.

Submitted by Pedro Silva on Fri, 03/24/2023 - 15:47

thank you !

Submitted by Alberto Lopez on Thu, 07/18/2024 - 16:37

Thanks! It's really helpful.

Submitted by Ruize Ge on Thu, 12/01/2022 - 00:34