Locating power lines in satellite images with semantic segmentation

Abstract: The inspection of power lines is an important process to maintain a stable electrical infrastructure. Simultaneously it is very time consuming task considering there are 164 000 km of power lines in Sweden alone. A cheaper and more sustainable approach is an automatic inspection with drones. But for a successful inspection with drones, exact power line coordinates is needed, which is not always available. To identify power lines in satellite images a machine learning approach was implemented. In machine learning, semantic segmentation is the process of pixel-wise classification of an image. Where you not only label the entire image, but every pixel individually. This way not only the existence of a power line will be identified, but their position inside the image. This thesis aims to investigate if semantic segmentation is an effective approach to locate power lines in satellite images. And what methods can be used on the segmented output data to extract linestring coordinates representing the power line. Linear regression and a polygon centerline extraction method was implemented on the segmented output data to define a line that represents the true location of the power line. The semantic segmentation model could find power lines where they were clearly visible, but struggled where they were not very visible. From good output data from the segmentation model, the linear regression and the polygon centerline extraction methods could successfully extract linestring coordinates that represented the true location of the power line. In the best case around 67% of power lines was correctly identified. But still, with good output data from the model, complex shapes such as intersections might still get bad results. Even if the approach needs further work, and cannot reliably identify all power lines in the current state, it has proven that this could be a promising method to identify power lines in satellite images.