# A CNN-Based Super-Resolution Technique for Active Fire Detection on Sentinel-2 Data

## TL;DR

This paper introduces a CNN-based super-resolution method to enhance Sentinel-2 multispectral data, especially SWIR bands, for improved active fire detection at higher spatial resolution, validated on a fire-affected area.

## Contribution

A novel CNN-based super-resolution technique for Sentinel-2 data that improves fire detection accuracy by increasing spatial resolution of key spectral bands.

## Key findings

- Outperforms alternative super-resolution methods in accuracy metrics
- Enhances active fire detection capabilities using super-resolved bands
- Validated on Vesuvius fire-affected region

## Abstract

Remote Sensing applications can benefit from a relatively fine spatial resolution multispectral (MS) images and a high revisit frequency ensured by the twin satellites Sentinel-2. Unfortunately, only four out of thirteen bands are provided at the highest resolution of 10 meters, and the others at 20 or 60 meters. For instance the Short-Wave Infrared (SWIR) bands, provided at 20 meters, are very useful to detect active fires. Aiming to a more detailed Active Fire Detection (AFD) maps, we propose a super-resolution data fusion method based on Convolutional Neural Network (CNN) to move towards the 10-m spatial resolution the SWIR bands. The proposed CNN-based solution achieves better results than alternative methods in terms of some accuracy metrics. Moreover we test the super-resolved bands from an application point of view by monitoring active fire through classic indices. Advantages and limits of our proposed approach are validated on specific geographical area (the mount Vesuvius, close to Naples) that was damaged by widespread fires during the summer of 2017.