  Buildings are the basic carrier of social production and human life; roads
are the links that interconnect social networks. Building and road information
has important application value in the frontier fields of regional coordinated
development, disaster prevention, auto-driving, etc. Mapping buildings and
roads from very high-resolution (VHR) remote sensing images have become a hot
research topic. However, the existing methods often ignore the strong spatial
correlation between roads and buildings and extract them in isolation. To fully
utilize the complementary advantages between buildings and roads, we propose a
building-road collaborative extraction method based on multi-task and
cross-scale feature interaction to improve the accuracy of both tasks in a
complementary way. A multi-task interaction module is proposed to interact
information across tasks and preserve the unique information of each task,
which tackle the seesaw phenomenon in multitask learning. By considering the
variation in appearance and structure between buildings and roads, a
cross-scale interaction module is designed to automatically learn the optimal
reception field for different tasks. Compared with many existing methods that
train each task individually, the proposed collaborative extraction method can
utilize the complementary advantages between buildings and roads by the
proposed inter-task and inter-scale feature interactions, and automatically
select the optimal reception field for different tasks. Experiments on a wide
range of urban and rural scenarios show that the proposed algorithm can achieve
building-road extraction with outstanding performance and efficiency.
