eprintid: 57717 rev_number: 11 eprint_status: archive userid: 1903 dir: disk0/00/05/77/17 datestamp: 2020-04-07 13:32:11 lastmod: 2020-04-07 13:32:11 status_changed: 2020-04-07 13:32:11 type: article metadata_visibility: show creators_name: González Gómez, Keila creators_name: Iglesias Martínez, Luis creators_name: Rodríguez-Solano Suárez, Roberto creators_name: Castro Malpica, María creators_id: keila.gonzalez.gomez@upm.es creators_id: luis.iglesias@upm.es creators_id: roberto.rodriguezsolano@upm.es creators_id: maria.castro@upm.es title: Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations publisher: MDPI rights: by-nc-nd ispublished: pub subjects: construccion subjects: transporte full_text_status: public keywords: 3D Point Cloud, 3D Objects, LiDAR Models, Sight Distance, Road Safety abstract: Existing roads require periodic evaluation in order to ensure safe transportation. Estimations of the available sight distance (ASD) are fundamental to make sure motorists have sufficient visibility to perform basic driving tasks. Mobile LiDAR Systems (MLS) can provide these evaluations with accurate three-dimensional models of the road and surroundings. Similarly, Geographic Information System (GIS) tools have been employed to obtain ASD. Due to the fact that widespread GIS formats used to store digital surface models handle elevation as an attribute of location, the presented methodology has separated the representation of ground and aboveground elements. The road geometry and surrounding ground are stored in digital terrain models (DTM). Correspondingly, abutting vegetation, manmade structures, road assets and other roadside elements are stored in 3D objects and placed on top of the DTM. Both the DTM and 3D objects are accurately obtained from a denoised and classified LiDAR point cloud. Based on the consideration that roadside utilities and most manmade structures are well-defined geometric elements, some visual obstructions are extracted and/or replaced with 3D objects from online warehouses. Different evaluations carried out with this method highlight the tradeoff between the accuracy of the estimations, performance and geometric complexity as well as the benefits of the individual consideration of road assets. date_type: published date: 2019-11 publication: Remote Sensing volume: 11 number: 23 pagerange: 2730-2730 id_number: 10.3390/rs11232730 institution: Caminos department: Ingenieria_Civil_2014_4 refereed: TRUE issn: 2072-4292 official_url: https://www.mdpi.com/2072-4292/11/23/2730 comprojects_type: MINECO comprojects_code: TRA2015-63579-R comprojects_leader: González Gómez, Keila comprojects_title: Nuevas tecnologías geoespaciales para estudios de seguridad vial citation: González Gómez, Keila and Iglesias Martínez, Luis and Rodríguez-Solano Suárez, Roberto and Castro Malpica, María (2019). Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations. "Remote Sensing", v. 11 (n. 23); p. 2730. ISSN 2072-4292. https://doi.org/10.3390/rs11232730 . document_url: http://oa.upm.es/57717/1/INVE_MEM_2019_309508.pdf