  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.
