Linear features segmentation from aerial images

TL;DR

This paper presents a deep learning-based method for segmenting city road dashed lines from aerial images, including a technique to recover missed lines and extract coordinates for urban planning.

Contribution

It introduces a novel approach combining U-Net and SegNet for dashed line segmentation and a method to add missed lines, enhancing accuracy in aerial image analysis.

Findings

Effective segmentation of dashed lines achieved

Method improves detection of occluded or poorly painted lines

Coordinates extraction supports urban planning applications

Abstract

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.