  Unmanned aerial vehicles (UAVs) offer a flexible and cost-effective solution
for wildfire monitoring. However, their widespread deployment during wildfires
has been hindered by a lack of operational guidelines and concerns about
potential interference with aircraft systems. Consequently, the progress in
developing deep-learning models for wildfire detection and characterization
using aerial images is constrained by the limited availability, size, and
quality of existing datasets. This paper introduces a solution aimed at
enhancing the quality of current aerial wildfire datasets to align with
advancements in camera technology. The proposed approach offers a solution to
create a comprehensive, standardized large-scale image dataset. This paper
presents a pipeline based on CycleGAN to enhance wildfire datasets and a novel
fusion method that integrates paired RGB images as attribute conditioning in
the generators of both directions, improving the accuracy of the generated
images.
