Next day fire prediction via semantic segmentation
This work addresses fire prediction for environmental monitoring, but it is incremental as it builds on a previous formulation with a new method.
The authors tackled next day fire prediction by reformulating it as a semantic segmentation task on images instead of binary classification on tabular data, achieving state-of-the-art results.
In this paper we present a deep learning pipeline for next day fire prediction. The next day fire prediction task consists in learning models that receive as input the available information for an area up until a certain day, in order to predict the occurrence of fire for the next day. Starting from our previous problem formulation as a binary classification task on instances (daily snapshots of each area) represented by tabular feature vectors, we reformulate the problem as a semantic segmentation task on images; there, each pixel corresponds to a daily snapshot of an area, while its channels represent the formerly tabular training features. We demonstrate that this problem formulation, built within a thorough pipeline achieves state of the art results.