Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation

TL;DR

This paper introduces an adaptive UAV path planning algorithm that optimizes data collection for high-resolution semantic segmentation by dynamically adjusting flight paths based on incoming image analysis, improving efficiency in large-scale monitoring tasks.

Contribution

The paper presents a novel online planning algorithm with a new accuracy model linking UAV altitude to segmentation quality, enabling targeted high-resolution inspections.

Findings

Effective in real-world data scenarios

Reduces unnecessary high-altitude mapping

Improves segmentation accuracy in critical areas

Abstract

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.