Efficient data collection methods play a major role in helping us better
understand the Earth and its ecosystems. In many applications, the usage of
unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly
gaining momentum due to their high mobility, low cost, and flexible deployment.
A key challenge is planning missions to maximize the value of acquired data in
large environments given flight time limitations. This is, for example,
relevant for monitoring agricultural fields. This paper addresses the problem
of adaptive path planning for accurate semantic segmentation of using UAVs. We
propose an online planning algorithm which adapts the UAV paths to obtain
high-resolution semantic segmentations necessary in areas with fine details as
they are detected in incoming images. This enables us to perform close
inspections at low altitudes only where required, without wasting energy on
exhaustive mapping at maximum image resolution. A key feature of our approach
is a new accuracy model for deep learning-based architectures that captures the
relationship between UAV altitude and semantic segmentation accuracy. We
evaluate our approach on different domains using real-world data, proving the
efficacy and generability of our solution.

本文提出了一种基于深度学习的无人机自适应路径规划算法，解决了无人机低空精细监测和高效数据采集的问题，并提出了一种新的深度学习精度模型，可以在不浪费能源的情况下获得高分辨率的语义分割图像。实验结果表明本方法可推广应用于不同领域。