Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))
FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) Dataset
You can find the article related to this code here at Elsevier or
You can find the preprint from the Arxiv website.
The dataset is uploaded on IEEE dataport. You can find the dataset here at IEEE Dataport or DOI. IEEE account is free, so you can create an account and access the dataset files without any payment or subscription.
This table below shows all available data for the dataset.
This project uses items 7, 8, 9, and 10 from the dataset. Items 7 and 8 are being used for the "Fire_vs_NoFire" image classification. Items 9 and 10 are for the fire segmentation.
If you clone this repository on your local drive, please download item 7 from the dataset and unzip in directory /frames/Training/... for the Training phase of the "Fire_vs_NoFire" image classification. The direcotry looks like this:
Repository/frames/Training
├── Fire/*.jpg
├── No_Fire/*.jpg
Repository/frames/Test
├── Fire/*.jpg
├── No_Fire/*.jpg
Repository/frames/Segmentation/Data
├── Images/*.jpg
├── Masks/*.png
This code is run and tested on Python 3.6 on linux (Ubuntu 18.04) machine with no issues. There is a config.py file in this directoy which shows all the configuration parameters such as Mode, image target size, Epochs, batch size, train_validation ratio, etc. All dependency files are available in the root directory of this repository.
Mode = 'Training'
Make sure that you have copied and unzipped the data in correct direcotry.
Mode = 'Classification'
Make sure that you have copied and unzipped the data in correct direcotry.
Mode = 'Segmentation'
Make sure that you have copied and unzipped the data in correct direcotry.
Then after setting your parameters, just run the main.py file.
python main.py
If you find it useful, please cite our paper as follows:
@article{shamsoshoara2021aerial,
title={Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset},
author={Shamsoshoara, Alireza and Afghah, Fatemeh and Razi, Abolfazl and Zheng, Liming and Ful{\'e}, Peter Z and Blasch, Erik},
journal={Computer Networks},
pages={108001},
year={2021},
publisher={Elsevier}
}
For academtic and non-commercial usage