  We present an approach for road segmentation that only requires image-level
annotations at training time. We leverage distant supervision, which allows us
to train our model using images that are different from the target domain.
Using large publicly available image databases as distant supervisors, we
develop a simple method to automatically generate weak pixel-wise road masks.
These are used to iteratively train a fully convolutional neural network, which
produces our final segmentation model. We evaluate our method on the Cityscapes
dataset, where we compare it with a fully supervised approach. Further, we
discuss the trade-off between annotation cost and performance. Overall, our
distantly supervised approach achieves 93.8% of the performance of the fully
supervised approach, while using orders of magnitude less annotation work.
