  We present an end-to-end trainable deep convolutional neural network (DCNN)
for semantic segmentation with built-in awareness of semantically meaningful
boundaries. Semantic segmentation is a fundamental remote sensing task, and
most state-of-the-art methods rely on DCNNs as their workhorse. A major reason
for their success is that deep networks learn to accumulate contextual
information over very large windows (receptive fields). However, this success
comes at a cost, since the associated loss of effecive spatial resolution
washes out high-frequency details and leads to blurry object boundaries. Here,
we propose to counter this effect by combining semantic segmentation with
semantically informed edge detection, thus making class-boundaries explicit in
the model, First, we construct a comparatively simple, memory-efficient model
by adding boundary detection to the Segnet encoder-decoder architecture.
Second, we also include boundary detection in FCN-type models and set up a
high-end classifier ensemble. We show that boundary detection significantly
improves semantic segmentation with CNNs. Our high-end ensemble achieves > 90%
overall accuracy on the ISPRS Vaihingen benchmark.
