# Next day fire prediction via semantic segmentation

Source: [Next day fire prediction via semantic segmentation](<https://www.sota2.com/research/paper/68fa72921e209131cea5734e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:28:52.799Z

Authors: Konstantinos Alexis, Stella Girtsou, Alexis Apostolakis, Giorgos Giannopoulos, Charalampos Kontoes

Published: 2024-03-20T12:31:13.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.13545>)

[PDF](<https://arxiv.org/pdf/2403.13545.pdf>)