  Semantic labeling (or pixel-level land-cover classification) in ultra-high
resolution imagery (< 10cm) requires statistical models able to learn high
level concepts from spatial data, with large appearance variations.
Convolutional Neural Networks (CNNs) achieve this goal by learning
discriminatively a hierarchy of representations of increasing abstraction.
  In this paper we present a CNN-based system relying on an
downsample-then-upsample architecture. Specifically, it first learns a rough
spatial map of high-level representations by means of convolutions and then
learns to upsample them back to the original resolution by deconvolutions. By
doing so, the CNN learns to densely label every pixel at the original
resolution of the image. This results in many advantages, including i)
state-of-the-art numerical accuracy, ii) improved geometric accuracy of
predictions and iii) high efficiency at inference time.
  We test the proposed system on the Vaihingen and Potsdam sub-decimeter
resolution datasets, involving semantic labeling of aerial images of 9cm and
5cm resolution, respectively. These datasets are composed by many large and
fully annotated tiles allowing an unbiased evaluation of models making use of
spatial information. We do so by comparing two standard CNN architectures to
the proposed one: standard patch classification, prediction of local label
patches by employing only convolutions and full patch labeling by employing
deconvolutions. All the systems compare favorably or outperform a
state-of-the-art baseline relying on superpixels and powerful appearance
descriptors. The proposed full patch labeling CNN outperforms these models by a
large margin, also showing a very appealing inference time.
