  Large-scale vector mapping is important for transportation, city planning,
and survey and census. We propose GraphMapper, a unified framework for
end-to-end vector map extraction from satellite images. Our key idea is a novel
unified representation of shapes of different topologies named "primitive
graph", which is a set of shape primitives and their pairwise relationship
matrix. Then, we convert vector shape prediction, regularization, and topology
reconstruction into a unique primitive graph learning problem. Specifically,
GraphMapper is a generic primitive graph learning network based on global shape
context modelling through multi-head-attention. An embedding space sorting
method is developed for accurate primitive relationship modelling. We
empirically demonstrate the effectiveness of GraphMapper on two challenging
mapping tasks, building footprint regularization and road network topology
reconstruction. Our model outperforms state-of-the-art methods in both tasks on
public benchmarks. All code will be publicly available.
