  Building footprints data is of importance in several urban applications and
natural disaster management. In contrast to traditional surveying and mapping,
using high spatial resolution aerial images, deep learning-based building
footprints extraction methods can extract building footprints accurately and
efficiently. With rapidly development of deep learning methods, it is hard for
novice to harness the powerful tools in building footprints extraction. The
paper aims at providing the whole process of building footprints extraction
from high spatial resolution images using deep learning-based methods. In
addition, we also compare the commonly used methods, including Fully
Convolutional Networks (FCN)-8s, U-Net and DeepLabv3+. At the end of the work,
we change the data size used in models training to explore the influence of
data size to the performance of the algorithms. The experiments show that, in
different data size, DeepLabv3+ is the best algorithm among them with the
highest accuracy and moderate efficiency; FCN-8s has the worst accuracy and
highest efficiency; U-Net shows the moderate accuracy and lowest efficiency. In
addition, with more training data, algorithms converged faster with higher
accuracy in extraction results.
