  Building footprint maps are vital to many remote sensing applications, such
as 3D building modeling, urban planning, and disaster management. Due to the
complexity of buildings, the accurate and reliable generation of the building
footprint from remote sensing imagery is still a challenging task. In this
work, an end-to-end building footprint generation approach that integrates
convolution neural network (CNN) and graph model is proposed. CNN serves as the
feature extractor, while the graph model can take spatial correlation into
consideration. Moreover, we propose to implement the feature pairwise
conditional random field (FPCRF) as a graph model to preserve sharp boundaries
and fine-grained segmentation. Experiments are conducted on four different
datasets: (1) Planetscope satellite imagery of the cities of Munich, Paris,
Rome, and Zurich; (2) ISPRS benchmark data from the city of Potsdam, (3) Dstl
Kaggle dataset; and (4) Inria Aerial Image Labeling data of Austin, Chicago,
Kitsap County, Western Tyrol, and Vienna. It is found that the proposed
end-to-end building footprint generation framework with the FPCRF as the graph
model can further improve the accuracy of building footprint generation by
using only CNN, which is the current state-of-the-art.
