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.

本文介绍了一种提高当前航拍野火数据集质量的方法，旨在适应相机技术的进步，并提出了一种基于 CycleGAN 的流程以及一种新颖的融合方法，通过将配对的 RGB 图像作为属性调节器集成到两个方向的生成器中，从而提高了生成图像的准确性。

热图像校准和修正的非配对循环一致对抗网络

Thermal Image Calibration and Correction using Unpaired Cycle-Consistent  Adversarial Networks

Teams of autonomous unmanned aircraft can be used to monitor wildfires,
enabling firefighters to make informed decisions. However, controlling multiple
autonomous fixed-wing aircraft to maximize forest fire coverage is a complex
problem. The state space is high dimensional, the fire propagates
stochastically, the sensor information is imperfect, and the aircraft must
coordinate with each other to accomplish their mission. This work presents two
deep reinforcement learning approaches for training decentralized controllers
that accommodate the high dimensionality and uncertainty inherent in the
problem. The first approach controls the aircraft using immediate observations
of the individual aircraft. The second approach allows aircraft to collaborate
on a map of the wildfire's state and maintain a time history of locations
visited, which are used as inputs to the controller. Simulation results show
that both approaches allow the aircraft to accurately track wildfire expansions
and outperform an online receding horizon controller. Additional simulations
demonstrate that the approach scales with different numbers of aircraft and
generalizes to different wildfire shapes.

本文研究了利用自主无人机团队监测野火的方法，通过使用两种深度强化学习方法训练分散控制器，可以有效地追踪野火的扩散，这两种方法在不同数量的飞行器和不同野火形状下都有较好的表现，模拟结果表明，这两种方法优于在线递归视角控制器。