  Mainstreamed weakly supervised road extractors rely on highly confident
pseudo-labels propagated from scribbles, and their performance often degrades
gradually as the image scenes tend various. We argue that such degradation is
due to the poor model's invariance to scenes with different complexities,
whereas existing solutions to this problem are commonly based on crafted priors
that cannot be derived from scribbles. To eliminate the reliance on such
priors, we propose a novel Structure-aware Mixup and Invariance Learning
framework (SA-MixNet) for weakly supervised road extraction that improves the
model invariance in a data-driven manner. Specifically, we design a
structure-aware Mixup scheme to paste road regions from one image onto another
for creating an image scene with increased complexity while preserving the
road's structural integrity. Then an invariance regularization is imposed on
the predictions of constructed and origin images to minimize their conflicts,
which thus forces the model to behave consistently on various scenes. Moreover,
a discriminator-based regularization is designed for enhancing the connectivity
meanwhile preserving the structure of roads. Combining these designs, our
framework demonstrates superior performance on the DeepGlobe, Wuhan, and
Massachusetts datasets outperforming the state-of-the-art techniques by 1.47%,
2.12%, 4.09% respectively in IoU metrics, and showing its potential of
plug-and-play. The code will be made publicly available.
