  The most common approaches to instance segmentation are complex and use
two-stage networks with object proposals, conditional random-fields, template
matching or recurrent neural networks. In this work we present TernausNetV2 - a
simple fully convolutional network that allows extracting objects from a
high-resolution satellite imagery on an instance level. The network has popular
encoder-decoder type of architecture with skip connections but has a few
essential modifications that allows using for semantic as well as for instance
segmentation tasks. This approach is universal and allows to extend any network
that has been successfully applied for semantic segmentation to perform
instance segmentation task. In addition, we generalize network encoder that was
pre-trained for RGB images to use additional input channels. It makes possible
to use transfer learning from visual to a wider spectral range. For
DeepGlobe-CVPR 2018 building detection sub-challenge, based on public
leaderboard score, our approach shows superior performance in comparison to
other methods. The source code corresponding pre-trained weights are publicly
available at https://github.com/ternaus/TernausNetV2
