  The rapid development of remote sensing technologies have gained significant
attention due to their ability to accurately localize, classify, and segment
objects from aerial images. These technologies are commonly used in unmanned
aerial vehicles (UAVs) equipped with high-resolution cameras or sensors to
capture data over large areas. This data is useful for various applications,
such as monitoring and inspecting cities, towns, and terrains. In this paper,
we presented a method for classifying and segmenting city road traffic dashed
lines from aerial images using deep learning models such as U-Net and SegNet.
The annotated data is used to train these models, which are then used to
classify and segment the aerial image into two classes: dashed lines and
non-dashed lines. However, the deep learning model may not be able to identify
all dashed lines due to poor painting or occlusion by trees or shadows. To
address this issue, we proposed a method to add missed lines to the
segmentation output. We also extracted the x and y coordinates of each dashed
line from the segmentation output, which can be used by city planners to
construct a CAD file for digital visualization of the roads.
