Road Extraction and Vectorization From Aerial Image Data

Abstract

Abstract. The objective of this study is the automatic extraction of the road network in a scene of the urban area from high resolution aerial image data. Our approach includes two stages aiming to solve two important issues respectively, i.e., an effective road extraction pipeline, and a precise vectorized road map. In the first stage, we proposed a so-called all element road model which describes a multiple-level structure of the basic road elements, i.e. intersection, central line, side lines, and road plane based on their spatial relations. An advanced road network extraction scheme was proposed to address the issues of tedious steps on segmentation, recognition and grouping, using the digital surface model (DSM) only. The key feature of the proposed approach was the cross validation of the road basic elements, which was applied all the way through the entire procedure of road extraction. In the second stage, the regularized road map was produced where center line and side lines subject to parallel and even layout rules. It gives more accurate and reliable map by utilizing both the height information of the DSM and the color information of the ortho image. Road surface was extracted from the image by region growing. Then, a regularized center line was modeled by linear regression using all the pixels of the road surface. The road width was estimated and two road side lines were modeled as the straight lines parallel with the center line. Finally, the road model was built up in terms of vectorized points and lines. The experimental results showed that the proposed approach performed satisfactorily in our test site.

GisGCN: A Visual Graph-Based Framework to Match Geographical Areas through Time

“…As an example, ancient place names can be extracted from old maps and stored with their respective locations in some GIS data format. This operation can be performed manually, most often through collaborative approaches (see for example, https://geo.nls.uk/maps/gb1900/ (accessed on 28 October 2021) or https://geohistoricaldata.org/) (accessed on 28 October 2021), or semi-automatically, using collaborative correction or validation tools (such as http: //buildinginspector.nypl.org/ (accessed on 28 October 2021)), applied on data produced by automatic image segmentation and vectorization approaches [4,5].…”