  Scene understanding of high resolution aerial images is of great importance
for the task of automated monitoring in various remote sensing applications.
Due to the large within-class and small between-class variance in pixel values
of objects of interest, this remains a challenging task. In recent years, deep
convolutional neural networks have started being used in remote sensing
applications and demonstrate state of the art performance for pixel level
classification of objects. \textcolor{black}{Here we propose a reliable
framework for performant results for the task of semantic segmentation of
monotemporal very high resolution aerial images. Our framework consists of a
novel deep learning architecture, ResUNet-a, and a novel loss function based on
the Dice loss. ResUNet-a uses a UNet encoder/decoder backbone, in combination
with residual connections, atrous convolutions, pyramid scene parsing pooling
and multi-tasking inference. ResUNet-a infers sequentially the boundary of the
objects, the distance transform of the segmentation mask, the segmentation mask
and a colored reconstruction of the input. Each of the tasks is conditioned on
the inference of the previous ones, thus establishing a conditioned
relationship between the various tasks, as this is described through the
architecture's computation graph. We analyse the performance of several
flavours of the Generalized Dice loss for semantic segmentation, and we
introduce a novel variant loss function for semantic segmentation of objects
that has excellent convergence properties and behaves well even under the
presence of highly imbalanced classes.} The performance of our modeling
framework is evaluated on the ISPRS 2D Potsdam dataset. Results show
state-of-the-art performance with an average F1 score of 92.9\% over all
classes for our best model.
