TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for
  Improved Building Footprint Extraction
Muhammad Ahmad Waseem Muhammad Tahir Zubair Khalid Momin Uppal
This paper considers the problem of extracting building footprints from
satellite imagery -- a task that is critical for many urban planning and
decision-making applications. While recent advancements in deep learning have
made great strides in automated detection of building footprints,
state-of-the-art methods available in existing literature often generate
erroneous results for areas with densely connected buildings. Moreover, these
methods do not incorporate the context of neighborhood images during training
thus generally resulting in poor performance at image boundaries. In light of
these gaps, we propose a novel Tuning Fork Network (TFNet) design for deep
semantic segmentation that not only performs well for widely-spaced building
but also has good performance for buildings that are closely packed together.
The novelty of TFNet architecture lies in a a single encoder followed by two
parallel decoders to separately reconstruct the building footprint and the
building edge. In addition, the TFNet design is coupled with a novel
methodology of incorporating neighborhood information at the tile boundaries
during the training process. This methodology further improves performance,
especially at the tile boundaries. For performance comparisons, we utilize the
SpaceNet2 and WHU datasets, as well as a dataset from an area in Lahore,
Pakistan that captures closely connected buildings. For all three datasets, the
proposed methodology is found to significantly outperform benchmark methods.