Automatic extraction of vectorized roof structures above LOD2.0 remains challenging due to their geometric complexity and the presence of small and occluded elements over the roofs. Detecting fine-scale roof objects such as chimneys and dormer windows in very high-resolution aerial imagery is still an active research topic. This study presents a workflow for automated detection and vectorization roof structures at city scale using Line Segment Detection (LSD) networks. Compared to model-based building reconstruction approaches, LSD networks do not rely on pre-defined roof typologies and are able to extract complex roof structures and small objects over the building roofs. For this purpose, a dataset comprising approximately 139,000 buildings with LOD2.2 roof structures and more than 2.2 million roof segments is generated using 8 cm GSD aerial imagery. An automated end-to-end workflow is developed, trained and tested from the available data. Experimental results indicate that roof structures suitable for LOD2.2 3D roofs can be extracted and vectorized with high accuracy, achieving 58.4% msAP and 73.1% mAPJ with ULSD network. Robustness is further assessed by visual inspection in areas affected by roof-blocking objects such as trees and cast shadows. Source code of the proposed workflow is publicly available at: <code>https://github.com/3DOM-FBK/pix2poly</code>.