The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Estimating and measuring building height has become one of the significant factors in urban planning, legal and illegal construction inspection, urban disaster warning and assessing, as well as providing initial mapping data for creating three dimensional (3D) digital city models. In this paper we examine the feasibility of extracting building height information using computer vision algorithms with...
In urban environments the most interesting and effective factors for localization and navigation are landmark buildings. This paper proposes a novel method to detect such buildings that stand out, i.e. would be given the status of 'landmark'. The method works in a fully unsupervised way, i.e. it can be applied to different cities without requiring annotation. First, salient points are detected, based...
This paper proposes a method to automatically register multi-view Terrestrial Laser Scanning Point Clouds in complex urban environment. Firstly, the point cloud segmentation method is applied to cluster the cross-section by self-adaptive distance cluster algorithm. Then, geometric primitives are extracted from point clouds by fitting lines or cylinders, whose spatial continuity is used to extract...
In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.