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Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))
| Date | Stars |
|---|---|
| 2026-07-24 | 252 |
| 2026-07-25 | 252 |
| 2026-07-28 | 252 |
| 2026-07-30 | 252 |
| 2026-08-06 | 252 |
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# Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV)
## Title
FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) Dataset<br/>
## Paper
You can find the **article** related to this code [here at Elsevier](https://www.sciencedirect.com/science/article/pii/S1389128621001201) or <br/>
You can find the **preprint** from the [Arxiv website](https://arxiv.org/pdf/2012.14036.pdf).
### Dataset
* The dataset is uploaded on IEEE dataport. You can find the dataset here at [IEEE Dataport](https://ieee-dataport.org/open-access/flame-dataset-aerial-imagery-pile-burn-detection-using-drones-uavs) or [DOI](https://dx.doi.org/10.21227/qad6-r683). 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](https://ieee-dataport.org/open-access/aerial-images-pile-fire-detection-using-drones-uavs) 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:
```bash
Repository/frames/Training
├── Fire/*.jpg
├── No_Fire/*.jpg
```
* For testing your trained model, please use item [8](https://ieee-dataport.org/open-access/aerial-images-pile-fire-detection-using-drones-uavs) and unzip it in direcotry /frame/Test/... . The direcotry looks like this:
```bash
Repository/frames/Test
├── Fire/*.jpg
├── No_Fire/*.jpg
```
* Items [9](https://ieee-dataport.org/open-access/aerial-images-pile-fire-detection-using-drones-uavs) and [10](https://ieee-dataport.org/open-access/aerial-images-pile-fire-detection-using-drones-uavs) should be unzipped in these directories frames/Segmentation/Data/Image/... and frames/Segmentation/Data/Masks/... accordingly. The direcotry looks like this:
```bash
Repository/frames/Segmentation/Data
├── Images/*.jpg
├── Masks/*.png
```
* Please remove other README files from those directories and make sure that only images are there.
<!---  --->
<img src=/Output/table.PNG width="860" height="600"/>
### Model
* The binary fire classifcation model of this project is based on the Xception Network:
<br/>
<br/>
* The fire segmentation model of this project is based on the U-NET:
### Sample
* A short sample video of the dataset is available on YouTube:
[](https://youtu.be/bHK6g37_KyA "Sample video")
## Requirements
* os
* re
* cv2
* copy
* tqdm
* scipy
* pickle
* numpy
* random
* itertools
* Keras 2.4.0
* scikit-image
* Tensorflow 2.3.0
* matplotlib.pyplot
## Code
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.
* To run the training phase for the "Fire_vs_NoFire" image classification, change the **mode** value to 'Training' in the config.py file.
[Like This](https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle/blob/7f2999a33d7d6655dccfdd8a65c33373b53067Excerpt of 7,357 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:27b756a94df7c75c, topic:dataset, desc:dataset, readme:dataset
matched fp:27b756a94df7c75c, topic:tensorflow
matched fp:27b756a94df7c75c, name:image classification, readme:image classification