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Semi-automated framework for generating cycling lane centerlines on roads with roadside barriers from noisy MLS data / Yang Ma in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 396 - 417 Titre : Semi-automated framework for generating cycling lane centerlines on roads with roadside barriers from noisy MLS data Type de document : Article/Communication Auteurs : Yang Ma, Auteur ; Yubing Zheng, Auteur ; Said Easa, Auteur ; Jianchuan Cheng, Auteur Année de publication : 2020 Article en page(s) : pp 396 - 417 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] axe médian [Termes IGN] bicyclette [Termes IGN] classification par nuées dynamiques [Termes IGN] classification semi-dirigée [Termes IGN] numérisation automatique [Termes IGN] rastérisation [Termes IGN] sécurité routière [Termes IGN] segmentation [Termes IGN] télémétrie laser mobile [Termes IGN] tracé routier Résumé : (auteur) Cycling lane centerlines (CLC) play an important role in the evaluation of safety-related conditions and guiding systems for cyclists along road corridors. The unavailability of design files or undocumented changes in the road infrastructures after improvements has created great difficulty in delineating CLC on existing roads. In this study, mobile laser scanning (MLS) data are introduced into this domain and a four-step semi-automated framework is proposed for generating CLC on roads with roadside barriers (RB). First, MLS data are restructured into the aligned scan-pattern grid using the mapping trajectory data. Second, a rasterization-based clustering approach is applied to segment the off-ground objects from the reorganized MLS data. Third, the RB amongst the segmented objects are identified using a sequential application of the k-Means clustering method and the proposed unidirectional growing method. Finally, the moving average technique and natural cubic spline are applied to generate CLC from the critical positions alongside the identified RB. Testing on three road sections with different types of RB demonstrated that the developed framework can successfully generate CLC from MLS data in the presence of considerable noises. The results also show that the proposed procedure shows better accuracy performance on processing roads with wide RB than a road with narrow RB. Numéro de notice : A2020-550 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.009 Date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95779 [article]

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