Automated production of synthetic point clouds of truss bridges for semantic and instance segmentation using deep learning models

The cost of obtaining large volumes of bridge data with technologies like laser scanners hinders the training of deep learning models. To address this, this paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The method generates point clouds by specifying the dimensions and components of the bridge, resulting in high variability in the generated dataset. A deep learning model is trained using the generated point clouds, which is an adapted version of JSNet. The accuracy of the results surpasses previous heuristic methods. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges. Furthermore, the success of the deep learning approach suggests its potential for semantic and instance segmentation of complex point clouds beyond truss bridges.Agencia Estatal de Investigación | Ref. PID2021-124236OB-C33Agencia Estatal de Investigación | Ref. RYC2021-033560-IUniversidade de Vigo/CISU

Generating IFC-compliant models and structural graphs of truss bridges from dense point clouds

The IFC schema has been evolving towards the infrastructure domain. Furthermore, the use of laser scanning technologies as means to digitalize and monitor infrastructures has also significantly increased. This work presents an automated modelling approach for truss bridges that utilizes laser scanning data as its source for geometrical information. The methodology takes a partially instance-segmented point cloud of a truss bridge and generates both an IFC-compliant information model of the truss and the corresponding structural graph. This process uses bounding boxes and their collisions to overcome the missing data from the partial segmentation to create the truss model, as well as to identify the nodes that connect the different truss members. The methodology was tested on a use case made of 272 members and obtained the truss model and structural graph files.Universidade de Vigo | Ref. PREUVIGO-21Agencia Estatal de Investigación | Ref. FJC2020–046370-IAgencia Estatal de Investigación | Ref. PID2021-124236OB-C33Agencia Estatal de Investigación | Ref. FJC2020–046370-IFinanciado para publicación en acceso aberto: Universidade de Vigo/CISU

Automatic road inventory using a low-cost mobile mapping system and based on a semantic segmentation deep learning model

Road maintenance is crucial for ensuring safety and government compliance, but manual measurement methods can be time-consuming and hazardous. This work proposes an automated approach for road inventory using a deep learning model and a 3D point cloud acquired by a low-cost mobile mapping system. The road inventory includes the road width, number of lanes, individual lane widths, superelevation, and safety barrier height. The results are compared with a ground truth on a 1.5 km subset of road, showing an overall intersection-over-union score of 84% for point cloud segmentation and centimetric errors for road inventory parameters. The number of lanes is correctly estimated in 81% of cases. This proposed method offers a safer and more automated approach to road inventory tasks and can be extended to more complex objects and rules for road maintenance and digitalization. The proposed approach has the potential to pave the way for building digital models from as-built infrastructure acquired by mobile mapping systems, making the road inventory process more efficient and accurate.Agencia Estatal de Investigación | Ref. RYC2021-033560-ICentro para el Desarrollo Tecnológico Industrial | Ref. IDI-2018111

Analysis of sun glare on roundabouts with aerial laser scanning data

Road geometry and sun glares play an important role concerning road safety. In this research, the direct sunlight in a roundabout sited in Ávila (Spain) is analysed using Aerial Laser Scanning (ALS) point clouds. First, the roundabout is divided in 8 sections, obtaining the driver bearing vectors of the roundabout. Entrances and exits driver bearing vectors of the roundabout are also considered. Then, sun rays are generated for a specific location of the roundabout and in a specific day and time. The incidence of the sun rays with the driver’s vision angle is analysed based on human vision model. Finally, intersections of sun rays with obstacles are calculated utilizing ALS point clouds. ALS data is processed (removing outliers, reducing point density, and computing a Delaunay Triangulation) in order to obtain accurate intersection results with obstacles and optimise the computational time. The method was tested in a roundabout, considering different driver bearings, the slope of the road and the elevation of the terrain. The results show that sun glares are detected at any day and time of the year, therefore areas with risk of direct sun glare within the roundabout are identified. The sun ray’s incidence in the vision angle of the driver is higher during winter solstice, and intersections with obstacles occur mainly during sunrise and sunset. In roundabout vector 7, during winter solstice there is direct sun glare for 7 hours 30 minutes, at the equinoxes for 6 hours 15 minutes and during summer solstice there is no direct sun glare.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. TIN2016-77158 -C4-2-RMinisterio de Ciencia e Innovación | Ref. FJC2018-035550-

Instance and semantic segmentation of point clouds of large metallic truss bridges

Several methods have been developed for the semantic segmentation of reinforced concrete bridges, however, there is a gap for truss bridges. Therefore, in this study a state-of-the-art methodology for the instance and semantic segmentation of point clouds of truss bridges for modelling purposes is presented, which, to the best of the authors' knowledge, is the first such methodology. This algorithm segments each truss element and classifies them as a chord, diagonal, vertical post, interior lateral brace, bottom lateral brace, or strut. The algorithm consists of a sequence of methods, including principal component analysis or clustering, that analyse each point and its neighbours in the point cloud. Case studies show that by adjusting only six manually measured parameters, the algorithm can automatically segment a truss bridge point cloud.Agencia Estatal de Investigación | Ref. PID2021-124236OB-C3Agencia Estatal de Investigación | Ref. RYC2021–033560-IUniversidade de Vigo/CISU

Pavement crack detection and clustering via region-growing algorithm from 3D MLS point clouds

Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. FJC2018-035550-

Automated calibration of FEM models using LiDAR point clouds

In present work it is pretended to estimate elastic parameters of beams through the combined use of precision geomatic techniques (laser scanning) and structural behaviour simulation tools. The study has two aims, on the one hand, to develop an algorithm able to interpret automatically point clouds acquired by laser scanning systems of beams subjected to different load situations on experimental tests; and on the other hand, to minimize differences between deformation values given by simulation tools and those measured by laser scanning. In this way we will proceed to identify elastic parameters and boundary conditions of structural element so that surface stresses can be estimated more easily.Ministerio de Interior | Ref. SPIP2017-02122Ministerio de Economía, Industria y Competitividad | Ref. EUIN2017- 87598Ministerio de Educación, Cultura y Deporte | Ref. CAS15/00126Xunta de Galicia | Ref. ED431C2016‐03

Recoñecemento de obxectos en ámbitos urbanos e de estrada a partires de datos obtidos por sensores de mapeado móbil

This thesis pursues the development of algorithms for detection and semantic characterization of elements of importance in traffic infrastructures of road and urban environments, using data from mobile mapping systems, and aiming at improving the automatic inventory of the mentioned infrastructures. These algorithms will analyse and process geomatic information (point clouds) and graphic information (imagery), and will try to find a symbiosis between them so drawbacks of one source of information can be balanced with the advantages of the other. Fulfillment of this objective implies the development of a strong knowledge base on machine learning techniques. The application of probabilistic models will be necessary to automatically classify different objects in clouds which contain millions of points. The already mentioned semantic characterization of urban objects will follow the indications of the international standard of representation, storage and exchange of 3D models of objects in urban contexts, CityGML. This standard is currently in a development stage, so new entities or extensions of the standard may be needed, and that is a topic of interest for researching, hence a possible objective for this thesis.El objetivo principal de la tesis doctoral es el desarrollo de algoritmia de detección y caracterización semántica de elementos de importancia en las infraestructuras de tráfico en entornos urbanos y de carretera a partir de sistemas de mapeado móvil, con el fin de favorecer el inventariado automático de dichas infraestructuras. Estos algoritmos analizarán y procesarán por un lado información geomática (nubes de puntos) y por otro información gráfica (imágenes fotográficas), y tratarán de encontrar una simbiosis entre ambos tipos de información, donde una aporte sus ventajas a los inconvenientes de la otra. El cumplimiento de este objetivo implicará el desarrollo de una sólida base de conocimiento en lo que a técnicas de aprendizaje artificial se refiere, ya que será necesaria la correcta aplicación de modelos probabilísticos que permitan clasificar de forma automática distintos objetos en nubes que pueden constar de decenas de millones de puntos. La ya mencionada caracterización semántica de elementos urbanos seguirá las indicaciones del estándar internacional de representación, almacenamiento e intercambio de modelos 3D de objetos en contextos urbanos, CityGML. Debido a que el estándar se encuentra en una etapa de desarrollo, podrá surgir la necesidad de crear nuevas entidades o extensiones, hecho que también presenta interés desde el punto de vista de la investigación y es, por tanto, un objetivo a tener en cuenta en el desarrollo de la tesis.O obxectivo principal da tesis é o desenvolvemento de algoritmia de detección e caracterización semántica de elementos de importancia nas infrastructuras de tráfico en ámbitos urbanos e de estradas a partires de sistemas de mapeado móbil, co fin de favorecer o inventariado automático de ditas infraestructuras. Estes algoritmos analizarán e procesarán por un lado información xeomática (nubes de puntos) e por outro información gráfica (imaxes fotográficas), e tratarán de atopar unha simbiosis entre ámbolos tipos de información, onde unha aporte as súas vantaxes ós inconvintes da outra. O cumplimento deste obxectivo implicará o desenvolvemento dunha sólida base de coñecemento no que a técnicas de aprendizaxe artificial se refire, xa que será necesaria a correcta aplicación de modelos probabilísticos que permitan clasificar de xeito automático distintos obxectos en nubes que poden constar de decenas de millóns de puntos. A xa mencionada caracterización semántica de elementos urbanos seguirá as indicacións do estándar internacional de representación, almacenamento e intercambio de modelos 3D de obxectos en contextos urbanos, CityGML. Debido a que o estándar atópase en etapa de desenvolvemento, poderá surxir a necesidade de crear novas entidades ou extensións, feito que tamén presenta interese dende o punto de vista da investigación que e é, polo tanto, un obxectivo a ter en conta no desenvolvemento da tese

3D Point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads

Building information modeling (BIM) is a process that has shown great potential in the building industry, but it has not reached the same level of maturity for transportation infrastructure. There is a standardization need for information exchange and management processes in the infrastructure that integrates BIM and Geographic Information Systems (GIS). Currently, the Industry Foundation Classes standard has harmonized different infrastructures under the Industry Foundation Classes (IFC) 4.3 release. Furthermore, the usage of remote sensing technologies such as laser scanning for infrastructure monitoring is becoming more common. This paper presents a semi-automated framework that takes as input a raw point cloud from a mobile mapping system, and outputs an IFC-compliant file that models the alignment and the centreline of each road lane in a highway road. The point cloud processing methodology is validated for two of its key steps, namely road marking processing and alignment and road line extraction, and a UML diagram is designed for the definition of the alignment entity from the point cloud data.Horizon 2020 Framework Programme | Ref. 769255Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-095893-B-C21Ministerio de Ciencia e Innovación y Universidades | Ref. FJC2018-035550-

Automatic extraction of road features in urban environments using dense ALS data

This paper describes a methodology that automatically extracts semantic information from urban ALS data for urban parameterization and road network definition. First, building façades are segmented from the ground surface by combining knowledge-based information with both voxel and raster data. Next, heuristic rules and unsupervised learning are applied to the ground surface data to distinguish sidewalk and pavement points as a means for curb detection. Then radiometric information was employed for road marking extraction. Using high-density ALS data from Dublin, Ireland, this fully automatic workflow was able to generate a F-score close to 95% for pavement and sidewalk identification with a resolution of 20 cm and better than 80% for road marking detection.Ministry of Economy and Competitiveness | Ref. TIN201346801-C4-4-RHuman Resources program FPI | Ref. BES-2014-067736Fundación Barri