Task Planning Support for Arborists and Foresters: Comparing Deep
  Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on
  UAV-Data
Jonas-Dario Troles Richard Nieding Sonia Simons Ute Schmid
Climate crisis and correlating prolonged, more intense periods of drought
threaten tree health in cities and forests. In consequence, arborists and
foresters suffer from increasing workloads and, in the best case, a consistent
but often declining workforce. To optimise workflows and increase productivity,
we propose a novel open-source end-to-end approach that generates helpful
information and improves task planning of those who care for trees in and
around cities. Our approach is based on RGB and multispectral UAV data, which
is used to create tree inventories of city parks and forests and to deduce tree
vitality assessments through statistical indices and Deep Learning. Due to EU
restrictions regarding flying drones in urban areas, we will also use
multispectral satellite data and fifteen soil moisture sensors to extend our
tree vitality-related basis of data. Furthermore, Bamberg already has a
georeferenced tree cadastre of around 15,000 solitary trees in the city area,
which is also used to generate helpful information. All mentioned data is then
joined and visualised in an interactive web application allowing arborists and
foresters to generate individual and flexible evaluations, thereby improving
daily task planning.