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Institute
Forest management relies on the analysis of satellite
imagery and time intensive physical on-site inspections. Both
methods are costly and time consuming. Satellite based images
are often not updated in a sufficient frequency to react to
infestations or other occurring problems.
Forest management benefits greatly from accurate and recent
information about the local forest areas. In order to react
appropriately and in time to incidents such as areas damaged by
storms, areas infested by bark beetles and decaying ground water
level, this information can be extracted from high resolution
imagery.
In this work, we propose UAVs to meet this demand and
demonstrate that they are fully capable of gathering this information
in a cost efficient way. Our work focuses on the cartography
of trees to optimize forest-operation. We apply deep learning for
image processing as a method to identify and isolate individual trees for GPS tagging and add some additional information such as height and diameter.