TY  - JOUR
SP  - 2730
VL  - 11
KW  - 3D Point Cloud
KW  -  3D Objects
KW  -  LiDAR Models
KW  -  Sight Distance
KW  -  Road Safety
JF  - Remote Sensing
Y1  - 2019/11//
PB  - MDPI
TI  - Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations
AV  - public
N2  - 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.
A1  - González Gómez, Keila
A1  - Iglesias Martínez, Luis
A1  - Rodríguez-Solano Suárez, Roberto
A1  - Castro Malpica, María
SN  - 2072-4292
ID  - upm57717
EP  - 2730
IS  - 23
UR  - https://www.mdpi.com/2072-4292/11/23/2730
ER  -