Adaptive path planning for UAVs for multi-resolution semantic segmentation?

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 highresolution 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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses the problem of adaptive path planning for accurate semantic segmentation using UAVs. An online planning algorithm is proposed to adjust the UAV paths based on detected details in incoming images in order to obtain high-resolution semantic segmentations. The approach is evaluated on real-world data, demonstrating its effectiveness and generality.