  In recent years, photogrammetry has been widely used in many areas to create
photorealistic 3D virtual data representing the physical environment. The
innovation of small unmanned aerial vehicles (sUAVs) has provided additional
high-resolution imaging capabilities with low cost for mapping a relatively
large area of interest. These cutting-edge technologies have caught the US Army
and Navy's attention for the purpose of rapid 3D battlefield reconstruction,
virtual training, and simulations. Our previous works have demonstrated the
importance of information extraction from the derived photogrammetric data to
create semantic-rich virtual environments (Chen et al., 2019). For example, an
increase of simulation realism and fidelity was achieved by segmenting and
replacing photogrammetric trees with game-ready tree models. In this work, we
further investigated the semantic information extraction problem and focused on
the ground material segmentation and object detection tasks. The main
innovation of this work was that we leveraged both the original 2D images and
the derived 3D photogrammetric data to overcome the challenges faced when using
each individual data source. For ground material segmentation, we utilized an
existing convolutional neural network architecture (i.e., 3DMV) which was
originally designed for segmenting RGB-D sensed indoor data. We improved its
performance for outdoor photogrammetric data by introducing a depth pooling
layer in the architecture to take into consideration the distance between the
source images and the reconstructed terrain model. To test the performance of
our improved 3DMV, a ground truth ground material database was created using
data from the One World Terrain (OWT) data repository. Finally, a workflow for
importing the segmented ground materials into a virtual simulation scene was
introduced, and visual results are reported in this paper.
