Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Path Planning for UAV-based Multi-Resolution Semantic
  Segmentation   In this paper, we address the problem of adaptive path planning for accurate
semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The
usage of UAVs for terrain monitoring and remote sensing is rapidly gaining
momentum due to their high mobility, low cost, and flexible deployment.
However, a key challenge is planning missions to maximize the value of acquired
data in large environments given flight time limitations. To address this, we
propose an online planning algorithm which adapts the UAV paths to obtain
high-resolution semantic segmentations necessary in areas on the terrain 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 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 the application of crop/weed segmentation
in precision agriculture using real-world field data.

11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph