Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Segmentation to Identify Safe Landing Zones for Unmanned Aerial
  Vehicles   There is a marked increase in delivery services in urban areas, and with Jeff
Bezos claiming that 86% of the orders that Amazon ships weigh less than 5 lbs,
the time is ripe for investigation into economical methods of automating the
final stage of the delivery process. With the advent of semi-autonomous drone
delivery services, such as Irish startup `Manna', and Malta's `Skymax', the
final step of the delivery journey remains the most difficult to automate. This
paper investigates the use of simple images captured by a single RGB camera on
a UAV to distinguish between safe and unsafe landing zones. We investigate
semantic image segmentation frameworks as a way to identify safe landing zones
and demonstrate the accuracy of lightweight models that minimise the number of
sensors needed. By working with images rather than video we reduce the amount
of energy needed to identify safe landing zones for a drone, without the need
for human intervention.

33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an