  Automatic road extraction from satellite imagery using deep learning is a
viable alternative to traditional manual mapping. Therefore it has received
considerable attention recently. However, most of the existing methods are
supervised and require pixel-level labeling, which is tedious and error-prone.
To make matters worse, the earth has a diverse range of terrain, vegetation,
and man-made objects. It is well known that models trained in one area
generalize poorly to other areas. Various shooting conditions such as light and
angel, as well as different image processing techniques further complicate the
issue. It is impractical to develop training data to cover all image styles.
This paper proposes to leverage OpenStreetMap road data as weak labels and
large scale satellite imagery to pre-train semantic segmentation models. Our
extensive experimental results show that the prediction accuracy increases with
the amount of the weakly labeled data, as well as the road density in the areas
chosen for training. Using as much as 100 times more data than the widely used
DeepGlobe road dataset, our model with the D-LinkNet architecture and the
ResNet-50 backbone exceeds the top performer of the current DeepGlobe
leaderboard. Furthermore, due to large-scale pre-training, our model
generalizes much better than those trained with only the curated datasets,
implying great application potential.
