We propose a new approach, named PolyMapper, to circumvent the conventional
pixel-wise segmentation of (aerial) images and predict objects in a vector
representation directly. PolyMapper directly extracts the topological map of a
city from overhead images as collections of building footprints and road
networks. In order to unify the shape representation for different types of
objects, we also propose a novel sequentialization method that reformulates a
graph structure as closed polygons. Experiments are conducted on both existing
and self-collected large-scale datasets of several cities. Our empirical
results demonstrate that our end-to-end learnable model is capable of drawing
polygons of building footprints and road networks that very closely approximate
the structure of existing online map services, in a fully automated manner.
Quantitative and qualitative comparison to the state-of-the-art also shows that
our approach achieves good levels of performance. To the best of our knowledge,
the automatic extraction of large-scale topological maps is a novel
contribution in the remote sensing community that we believe will help develop
models with more informed geometrical constraints.

PolyMapper 是一种新的方法，用于从空中图像中直接提取城市的拓扑地图，并提出了一种新的序列化方法以实现不同类型对象的统一形状表示，其能够在现有大规模数据集上以全自动方式绘制建筑物和道路的多边形，表现得很好。