Explainable Machine Learning for Forest Fire Detection with Remote Sensing for Effective Rescue Planning

Abstract

In the present decade, forest fires have become more common than ever. Efficient strategies to cope with fire situations, and/damage assessments need efficient automatic forest fire detection model. In this research, we propose an unsupervised eXplainable machine learning model to assess the severity of forest fire with remote sensing data. The model, namely, Latent Dirichlet Allocation is a Bayesian Generative model, is capable of generating interpretable visualizations. LDA uncertainty quantifiable and explainable. We do not need labelled data to train the model. Other usefulness of the model is that it is simple to combine any kind of input data (for example, UAV images, wind speed information). In the scope of this contribution, we use Sentinel-2 spectral bands to extract information to compute indices indicating severity of fire. Uncertainty of each prediction of the model is computed to ascertain robustness of the model.