  Purpose: This paper presents a new dataset of Aerial Imagery from Robotics
simulator (abbr. AIR). AIR dataset aims to provide a starting point for
localization system development and to become a typical benchmark for accuracy
comparison of map-based localization algorithms, visual odometry, and SLAM for
high altitude flights.
  Design/methodology/approach: The presented dataset contains over 100 thousand
aerial images captured from Gazebo robotics simulator using orthophoto maps as
a ground plane. Flights with 3 different trajectories are performed on maps
from urban and forest environment at different altitudes, totaling over 33
kilometers of flight distance.
  Findings: The review of previous researches shows, that the presented dataset
is the largest currently available public dataset with downward facing camera
imagery.
  Originality/value: This paper presents the problem of missing publicly
available datasets for high altitude (100--3000 meters) UAV flights, the
current state-of-the-art researches performed to develop map-based localization
system for UAVs, depend on real-life test flights and custom simulated datasets
for accuracy evaluation of the algorithms. The presented new dataset solves
this problem and aims to help the researchers to improve and benchmark new
algorithms for high-altitude flights.
