  Convolutional neural networks (CNNs) have received increasing attention over
the last few years. They were initially conceived for image categorization,
i.e., the problem of assigning a semantic label to an entire input image.
  In this paper we address the problem of dense semantic labeling, which
consists in assigning a semantic label to every pixel in an image. Since this
requires a high spatial accuracy to determine where labels are assigned,
categorization CNNs, intended to be highly robust to local deformations, are
not directly applicable.
  By adapting categorization networks, many semantic labeling CNNs have been
recently proposed. Our first contribution is an in-depth analysis of these
architectures. We establish the desired properties of an ideal semantic
labeling CNN, and assess how those methods stand with regard to these
properties. We observe that even though they provide competitive results, these
CNNs often underexploit properties of semantic labeling that could lead to more
effective and efficient architectures.
  Out of these observations, we then derive a CNN framework specifically
adapted to the semantic labeling problem. In addition to learning features at
different resolutions, it learns how to combine these features. By integrating
local and global information in an efficient and flexible manner, it
outperforms previous techniques. We evaluate the proposed framework and compare
it with state-of-the-art architectures on public benchmarks of high-resolution
aerial image labeling.
