  Deep learning-based methods have been extensively explored for automatic
building mapping from high-resolution remote sensing images over recent years.
While most building mapping models produce vector polygons of buildings for
geographic and mapping systems, dominant methods typically decompose polygonal
building extraction in some sub-problems, including segmentation,
polygonization, and regularization, leading to complex inference procedures,
low accuracy, and poor generalization. In this paper, we propose a simple and
novel building mapping method with Hierarchical Transformers, called HiT,
improving polygonal building mapping quality from high-resolution remote
sensing images. HiT builds on a two-stage detection architecture by adding a
polygon head parallel to classification and bounding box regression heads. HiT
simultaneously outputs building bounding boxes and vector polygons, which is
fully end-to-end trainable. The polygon head formulates a building polygon as
serialized vertices with the bidirectional characteristic, a simple and elegant
polygon representation avoiding the start or end vertex hypothesis. Under this
new perspective, the polygon head adopts a transformer encoder-decoder
architecture to predict serialized vertices supervised by the designed
bidirectional polygon loss. Furthermore, a hierarchical attention mechanism
combined with convolution operation is introduced in the encoder of the polygon
head, providing more geometric structures of building polygons at vertex and
edge levels. Comprehensive experiments on two benchmarks (the CrowdAI and Inria
datasets) demonstrate that our method achieves a new state-of-the-art in terms
of instance segmentation and polygonal metrics compared with state-of-the-art
methods. Moreover, qualitative results verify the superiority and effectiveness
of our model under complex scenes.
