  Aerial image segmentation is the basis for applications such as automatically
creating maps or tracking deforestation. In true orthophotos, which are often
used in these applications, many objects and regions can be approximated well
by polygons. However, this fact is rarely exploited by state-of-the-art
semantic segmentation models. Instead, most models allow unnecessary degrees of
freedom in their predictions by allowing arbitrary region shapes. We therefore
present a refinement of our deep learning model which predicts binary space
partitioning trees, an efficient polygon representation. The refinements
include a new feature decoder architecture and a new differentiable BSP tree
renderer which both avoid vanishing gradients. Additionally, we designed a
novel loss function specifically designed to improve the spatial partitioning
defined by the predicted trees. Furthermore, our expanded model can predict
multiple trees at once and thus can predict class-specific segmentations. As an
additional contribution, we investigate the impact of a non-optimal training
process in comparison to an optimized training process. While model
architectures optimized for aerial images, such as PFNet or our own model, show
an advantage under non-optimal conditions, this advantage disappears under
optimal training conditions. Despite this observation, our model still makes
better predictions for small rectangular objects, e.g., cars.
