  This paper studies the problem of polygonal mapping of buildings by tackling
the issue of mask reversibility that leads to a notable performance gap between
the predicted masks and polygons from the learning-based methods. We addressed
such an issue by exploiting the hierarchical supervision (of bottom-level
vertices, mid-level line segments and the high-level regional masks) and
proposed a novel interaction mechanism of feature embedding sourced from
different levels of supervision signals to obtain reversible building masks for
polygonal mapping of buildings. As a result, we show that the learned
reversible building masks take all the merits of the advances of deep
convolutional neural networks for high-performing polygonal mapping of
buildings. In the experiments, we evaluated our method on the two public
benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method
obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For
the Inria dataset, our proposed method also obtains very competitive results on
the metrics of IoU and Accuracy. The models and source code are available at
https://github.com/SarahwXU.
