  In this paper we address three different aspects of semantic segmentation
from remote sensor data using deep neural networks. Firstly, we focus on the
semantic segmentation of buildings from remote sensor data and propose ICT-Net.
The proposed network has been tested on the INRIA and AIRS benchmark datasets
and is shown to outperform all other state of the art by more than 1.5% and
1.8% on the Jaccard index, respectively.
  Secondly, as the building classification is typically the first step of the
reconstruction process, we investigate the relationship of the classification
accuracy to the reconstruction accuracy.
  Finally, we present the simple yet compelling concept of latent learning and
the implications it carries within the context of deep learning. We posit that
a network trained on a primary task (i.e. building classification) is
unintentionally learning about auxiliary tasks (e.g. the classification of
road, tree, etc) which are complementary to the primary task. We extensively
tested the proposed technique on the ISPRS benchmark dataset which contains
multi-label ground truth, and report an average classification accuracy (F1
score) of 54.29% (SD=17.03) for roads, 10.15% (SD=2.54) for cars, 24.11%
(SD=5.25) for trees, 42.74% (SD=6.62) for low vegetation, and 18.30% (SD=16.08)
for clutter.
  The source code and supplemental material is publicly available at
http://www.theICTlab.org/lp/2019ICT-Net/.
