Early Wildfire Detection Using One Class Learning

Wildfires cause major damages to forests and infrastructures every year. The cost of wildfire control and damage repair are rapidly increasing, leading to significant financial burdens for many communities, especially remote and rural communities. An effective way to mitigate the damage wildfires cause is by detecting the fire early. Recently, a combination of Unmanned Aerial Vehicles (UAVs) and deep learning has been proposed as a method for early detection. However, a major challenge is the lack of data to train learning models. This causes current models to lack the ability to generalize the task of wildfire detection. In this paper, we present One Class Classification (OCC) as an alternative solution to alleviate the lack of data. Our models achieved F1 scores of 0.94 when trained and tested on the FLAME data set and 0.97 when trained and tested on the FLAME2 data set.