  The knowledge about the placement and appearance of lane markings is a
prerequisite for the creation of maps with high precision, necessary for
autonomous driving, infrastructure monitoring, lane-wise traffic management,
and urban planning. Lane markings are one of the important components of such
maps. Lane markings convey the rules of roads to drivers. While these rules are
learned by humans, an autonomous driving vehicle should be taught to learn them
to localize itself. Therefore, accurate and reliable lane marking semantic
segmentation in the imagery of roads and highways is needed to achieve such
goals. We use airborne imagery which can capture a large area in a short period
of time by introducing an aerial lane marking dataset. In this work, we propose
a Symmetric Fully Convolutional Neural Network enhanced by Wavelet Transform in
order to automatically carry out lane marking segmentation in aerial imagery.
Due to a heavily unbalanced problem in terms of number of lane marking pixels
compared with background pixels, we use a customized loss function as well as a
new type of data augmentation step. We achieve a very high accuracy in
pixel-wise localization of lane markings without using 3rd-party information.
In this work, we introduce the first high-quality dataset used within our
experiments which contains a broad range of situations and classes of lane
markings representative of current transportation systems. This dataset will be
publicly available and hence, it can be used as the benchmark dataset for
future algorithms within this domain.
