  Establishing up-to-date large scale building maps is essential to understand
urban dynamics, such as estimating population, urban planning and many other
applications. Although many computer vision tasks has been successfully carried
out with deep convolutional neural networks, there is a growing need to
understand their large scale impact on building mapping with remote sensing
imagery. Taking advantage of the scalability of CNNs and using only few areas
with the abundance of building footprints, for the first time we conduct a
comparative analysis of four state-of-the-art CNNs for extracting building
footprints across the entire continental United States. The four CNN
architectures namely: branch-out CNN, fully convolutional neural network (FCN),
conditional random field as recurrent neural network (CRFasRNN), and SegNet,
support semantic pixel-wise labeling and focus on capturing textural
information at multi-scale. We use 1-meter resolution aerial images from
National Agriculture Imagery Program (NAIP) as the test-bed, and compare the
extraction results across the four methods. In addition, we propose to combine
signed-distance labels with SegNet, the preferred CNN architecture identified
by our extensive evaluations, to advance building extraction results to
instance level. We further demonstrate the usefulness of fusing additional near
IR information into the building extraction framework. Large scale experimental
evaluations are conducted and reported using metrics that include: precision,
recall rate, intersection over union, and the number of buildings extracted.
With the improved CNN model and no requirement of further post-processing, we
have generated building maps for the United States. The quality of extracted
buildings and processing time demonstrated the proposed CNN-based framework
fits the need of building extraction at scale.
