  Our previous works have demonstrated that visually realistic 3D meshes can be
automatically reconstructed with low-cost, off-the-shelf unmanned aerial
systems (UAS) equipped with capable cameras, and efficient photogrammetric
software techniques. However, such generated data do not contain semantic
information/features of objects (i.e., man-made objects, vegetation, ground,
object materials, etc.) and cannot allow the sophisticated user-level and
system-level interaction. Considering the use case of the data in creating
realistic virtual environments for training and simulations (i.e., mission
planning, rehearsal, threat detection, etc.), segmenting the data and
extracting object information are essential tasks. Thus, the objective of this
research is to design and develop a fully automated photogrammetric data
segmentation and object information extraction framework. To validate the
proposed framework, the segmented data and extracted features were used to
create virtual environments in the authors previously designed simulation tool
i.e., Aerial Terrain Line of Sight Analysis System (ATLAS). The results showed
that 3D mesh trees could be replaced with geo-typical 3D tree models using the
extracted individual tree locations. The extracted tree features (i.e., color,
width, height) are valuable for selecting the appropriate tree species and
enhance visual quality. Furthermore, the identified ground material information
can be taken into consideration for pathfinding. The shortest path can be
computed not only considering the physical distance, but also considering the
off-road vehicle performance capabilities on different ground surface
materials.
