PyroNear: early wildfire detection

The increasing adoption of mobile phones have significantly shortened the time required for firefighting agents to be alerted of a starting wildfire. In less dense areas, limiting and minimizing this duration remains critical to preserve forest areas.

PyroNear aims at offering an wildfire early detection system with state-of-the-art performances at minimal deployment costs.

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
• References
• Documentation
• Contributing
• Credits
• License

Getting started

Prerequisites

• Python 3.6 (or more recent)
• pip

Installation

Use pip to install the package from git

pip install git+https://github.com/frgfm/PyroNear@master

Usage

datasets

Access all PyroNear datasets just like any torchvision.datasets.VisionDataset:

from pyronear.datasets import OpenFire
dataset = OpenFire('./data', download=True)

References

You are free to use any training script, but some are already provided for reference. In order to use them, install the specific requirements and check script options as follows:

pip install -r references/classification/fastai/requirements.txt
python references/classification/fastai/train.py --help

You can then run the script with your own arguments:

python references/classification/fastai/train.py --data-path ./data --lr 3e-3 --epochs 4 --pretrained --deterministic

Please note that most tasks are provided with two training scripts (and their requirements.txt): one using fastai and the other without it.

Documentation

The full package documentation is available here for detailed specifications. The documentation was built with Sphinx using a theme provided by Read the Docs.

Contributing

Please refer to CONTRIBUTING if you wish to contribute to this project.

Credits

This project is developed and maintained by the repo owner and volunteers from Data for Good.

License

Distributed under the MIT License. See LICENSE for more information.