Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR

Building Information Modeling (BIM) has a crucial role in smart road applications, not only limited to the design and construction stages, but also to traffic monitoring, autonomous vehicle navigation, road condition assessment, and real-time data delivery to drivers, among others. Point clouds collected through LiDAR are a powerful solution to capture as-built conditions, notwithstanding the lack of commercial tools able to automatically reconstruct road geometry in a BIM environment. This paper illustrates a two-step procedure in which roads are automatically detected and classified, providing GIS layers with basic road geometry that are turned into parametric BIM objects. The proposed system is an integrated BIM-GIS with a structure based on multiple proposals, in which a single project file can handle different versions of the model using a variable level of detail. The model is also refined by adding parametric elements for buildings and vegetation. Input data for the integrated BIM-GIS can also be existing cartographic layers or outputs generated with algorithms able to handle LiDAR data. This makes the generation of the BIM-GIS more flexible and not limited to the use of specific algorithms for point cloud processing.

[1]  Ping Ma,et al.  Combined Lane Mapping Using a Mobile Mapping System , 2019, Remote. Sens..

[2]  Fabio Menna,et al.  A CRITICAL REVIEW OF AUTOMATED PHOTOGRAMMETRICPROCESSING OF LARGE DATASETS , 2017 .

[3]  Jun Wang,et al.  Comparative analysis on the adoption and use of BIM in road infrastructure projects , 2016 .

[4]  Hanjin Hu,et al.  Building Information Modeling (BIM) for transportation infrastructure – Literature review, applications, challenges, and recommendations , 2018, Automation in Construction.

[5]  Alberto Holgado-Barco,et al.  An automated approach to vertical road characterisation using mobile LiDAR systems: Longitudinal profiles and cross-sections , 2014 .

[6]  Cheng Wang,et al.  A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[7]  Diego González-Aguilera,et al.  Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[8]  Ying Li,et al.  Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review , 2018, Remote. Sens..

[9]  Uwe Stilla,et al.  FUSION OF FEATURE BASED AND DEEP LEARNING METHODS FOR CLASSIFICATION OF MMS POINT CLOUDS , 2019 .

[10]  Karel Pavelka,et al.  AUTOMATIC CLASSIFICATION OF POINT CLOUDS FOR HIGHWAY DOCUMENTATION , 2018 .

[11]  Pankaj Kumar,et al.  Automated road markings extraction from mobile laser scanning data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Abhijit Mukherjee,et al.  Community Sensor Network for Monitoring Road Roughness Using Smartphones , 2017, J. Comput. Civ. Eng..

[13]  R. Brumana,et al.  Generative HBIM modelling to embody complexity (LOD, LOG, LOA, LOI): surveying, preservation, site intervention—the Basilica di Collemaggio (L’Aquila) , 2018, Applied Geomatics.

[14]  F. Crosilla,et al.  LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories , 2013 .

[15]  Ajai Kumar Singh,et al.  Extraction of road surface from mobile LiDAR data of complex road environment , 2017 .

[16]  Mattia Previtali,et al.  Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing , 2018 .

[17]  Xiangyu Wang,et al.  A State-of-the-Art Review on the Integration of Building Information Modeling (BIM) and Geographic Information System (GIS) , 2017, ISPRS Int. J. Geo Inf..

[18]  Pedro Arias,et al.  Automatic Inventory of Road Cross‐Sections from Mobile Laser Scanning System , 2017, Comput. Aided Civ. Infrastructure Eng..

[19]  Alberto Holgado-Barco,et al.  Semiautomatic Extraction of Road Horizontal Alignment from a Mobile LiDAR System , 2015, Comput. Aided Civ. Infrastructure Eng..

[20]  E. Zelniker,et al.  Detection and vectorization of roads from lidar data , 2007 .

[21]  Jie Wu,et al.  CRSM: a practical crowdsourcing-based road surface monitoring system , 2016, Wirel. Networks.

[22]  Shuanggen Jin,et al.  Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization , 2016 .

[23]  Stefan Hinz,et al.  Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers , 2015 .

[24]  Jaehoon Jung,et al.  Efficient and robust lane marking extraction from mobile lidar point clouds , 2019 .

[25]  Ruzena Bajcsy,et al.  Computer Recognition of Roads from Satellite Pictures , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[26]  Boris Jutzi,et al.  SHAPE DISTRIBUTION FEATURES FOR POINT CLOUD ANALYSIS - A GEOMETRIC HISTOGRAM APPROACH ON MULTIPLE SCALES , 2014 .

[27]  Robert John Lark,et al.  BIM for infrastructure: An overall review and constructor perspective , 2016 .

[28]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Zhen Dong,et al.  AUTOMATIC ROAD STRUCTURE DETECTION AND VECTORIZATION USING MLS POINT CLOUDS , 2019 .

[30]  Jack Chin Pang Cheng,et al.  Analytical review and evaluation of civil information modeling , 2016 .

[31]  Marco Scaioni,et al.  METHODS FROM INFORMATION EXTRACTION FROM LIDAR INTENSITY DATA AND MULTISPECTRAL LIDAR TECHNOLOGY , 2018 .

[32]  Yong Li,et al.  Road detection from airborne LiDAR point clouds adaptive for variability of intensity data , 2015 .

[33]  Juan B. Mena,et al.  State of the art on automatic road extraction for GIS update: a novel classification , 2003, Pattern Recognit. Lett..

[34]  Kurt E. Brassel,et al.  A Procedure to Generate Thiessen Polygons , 2010 .

[35]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[36]  Yongjun Zhang,et al.  Road Centerline Extraction in Complex Urban Scenes From LiDAR Data Based on Multiple Features , 2014, IEEE Transactions on Geoscience and Remote Sensing.