Bee2Fire: A Deep Learning Powered Forest Fire Detection System

Rui Valente de Almeida, Fernando Crivellaro, Maria Narciso, Ana Sousa, Pedro Vieira

Abstract

Bee2Fire is a commercial system for forest fire detection, inheriting from the Forest Fire Finder System. Designed in Portugal, it aims to address one of Southern Europe’s main concern, forest fires. It is a well known fact that the sooner a wildfire is detected, the quicker it can be put out, which highlights the importance of early detection. By scanning the landscape using regular cameras and Deep Artificial Neural Networks, Bee2Fire searches for smoke columns above the horizon with a image classification approach. After these networks were trained, the system was deployed in the field, obtaining a sensitivity score between 74% and 93%, a specificity of more than 99% and a precision of around 82%.

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Paper Citation


in Harvard Style

Valente de Almeida R., Crivellaro F., Narciso M., Sousa A. and Vieira P. (2020). Bee2Fire: A Deep Learning Powered Forest Fire Detection System.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 603-609. DOI: 10.5220/0008966106030609


in Bibtex Style

@conference{icaart20,
author={Rui Valente de Almeida and Fernando Crivellaro and Maria Narciso and Ana Sousa and Pedro Vieira},
title={Bee2Fire: A Deep Learning Powered Forest Fire Detection System},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={603-609},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008966106030609},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Bee2Fire: A Deep Learning Powered Forest Fire Detection System
SN - 978-989-758-395-7
AU - Valente de Almeida R.
AU - Crivellaro F.
AU - Narciso M.
AU - Sousa A.
AU - Vieira P.
PY - 2020
SP - 603
EP - 609
DO - 10.5220/0008966106030609