Automatic classification of common building materials from 3D terrestrial laser scan data

Li, Yuan; Guo, Jingjing; Wang, Qian

doi:10.1016/j.autcon.2019.103017

Cited by 53 publications

(18 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are various ML classifiers which are being adopted by the researchers such as random forest (RF), decision tree (DT), bayesian, k-nearest neighbours (KNN), gaussian mixture modes (GMM), logistic regression (LR), support vector machine (SVM), and artificial neural networks (ANN), etc. However, ANN and SVM are the most favourite techniques among researchers, when it comes to material classification [5,8,14]. The material classification has been performed by researchers on various sources data input such as digital images taken with the help of a camera [15], smartphones, drones [16], and 3D point cloud models generated on collected images via structure from motion (SfM) [17], or laser scanners [8].…”

Section: Discussionmentioning

confidence: 99%

“…Eleven construction materials (sandstorms, paving, gravel, stone, cement-granular, brick, soil, wood, asphalt, clay hollow block, and concrete block) were classified in this study, and good accuracy was achieved by VGG16 algorithm, even for images that were hard to identify by the human eye. Yuan et al [8] proposed an automatic material classification method with the help of 2D digital images using the graphical characteristics of building materials. A coloured laser scan data was generated using a terrestrial laser scanner (TLS) with a built-in camera, which contained the surface geometries, material reflectance and surface roughness of building materials.…”

Section: Discussionmentioning

confidence: 99%

“…The varying light conditions have a substantial impact on the visual properties of the materials, which create difficulties in classifying the image-based building material. Moreover, the weak textures on surfaces and uncertain points of view often adversely affect the effectiveness and precision process of image-based content classification [7,8]. Material classification is considered a vital activity of any vision-based framework to generate conceptual as-built 3D models for automatic progress monitoring in construction projects.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Alaloul¹,

Qureshi²

2021

Deep Learning Applications

View full text Add to dashboard Cite

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Alaloul¹,

Qureshi²

2021

Deep Learning Applications

View full text Add to dashboard Cite

show abstract

“…They also introduced a new dataset. Other authors (Yuan et al, 2020) classified different building materials from a Terrestrial Laser Scanner (TLS), using a machine learning approach basing on three features: material reflectance, Hue Saturation Value (HSV) colour, surface roughness.…”

Section: Related Workmentioning

confidence: 99%

“…plane (Díaz-Vilariño et al, 2016). The Intensity is a function of several variables, including the distance from the laser, the angle of incidence of the laser beam on the surface and the specific material reflectance (Yuan et al, 2020). Since the method considers different paving materials, the intensity is an appropriate features.…”

Section: Sidewalk Segmentationmentioning

confidence: 99%

Sidewalk Detection and Pavement Characterisation in Historic Urban Environments From Point Clouds: Preliminary Results

Treccani

Díaz-Vilariño

Adami

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The definition of physical accessibility in urban environments is a topic of recognized importance by policy makers and by international organizations. A first step to address the accessibility topic is the definition and characterisation of urban elements, like sidewalks, roads, and ramps. Sidewalk inventory plays a crucial role in this phase. In literature there are several ways to extract sidewalks from a point cloud, but they are all tailored on modern and standardized situations. For example the presence of a curb is assumed as the normality and the roads are supposed to have the same width along the path. When dealing with an Urban Heritage, some difficulties arise. In fact, in an historic urban environment ground irregularities should be taken in consideration: the paving is composed by different materials, curbs are not always present, and a Z difference between road and sidewalks is not so sure. In such cases existing methodologies cannot be applied. This paper present a method to semantically segment a point cloud, labelling sidewalks and roads. Sidewalks are also characterized by detecting their pavings. The method is tested on an Urban Heritage: the Unesco site of Sabbioneta, in northern Italy. The results are promising, sidewalks are detected with a precision of 80%, main errors are in corner areas. Paving characterisation is based on thresholds derived from some samples, and the method shows an high precision (more than 90%) in all the pavings considered.

show abstract

Open‐source terrestrial laser scanner for the virtualization of geometrical entities in AEC classrooms

Ramonell

Chacón

2022

Comp Applic In Engineering

View full text Add to dashboard Cite

This paper depicts a case study that shows an open-source Terrestrial Laser Scanner (TLS) for use on the virtualization of simple yet precise geometrical entities in AEC classrooms. For bringing this technology to AEC classrooms, an open-source TLS was developed. The physical model was built using digital fabrication, its connectivity was established using accessible, affordable, opensource sensors and actuators and its digital representation was developed using generative design tools. All elements together are synchronized in realtime and the TLS becomes live, digitally twinned, geometry generator. The points that are generated by the TLS are gathered in a 3D virtual space in the form of virtual points. The present digitally twinned vision of laser scanning has thus three identified educational uses: (i) illustration of the measuring principle using open-source hardware together with mathematics and statistics, (ii) illustration of the generation and visualization of point clouds in realtime within a CAD environment, and (iii) perhaps with a vaster scope in AEC classrooms, illustration of the usage, analysis and identification of these point clouds. The development of this tool belongs to a vaster project for infusing Construction 4.0 technologies in AEC classrooms under development at the School of Civil Engineering of UPC-BarcelonaTech. The illustration to students of all these concepts with accessible technological tools is expected to enlarge their vision of more advanced constructional technologies such as cyber-physical systems for monitoring and surveying as well as digital technologies for reproducing "As-Built" models.

show abstract

Automatic classification of common building materials from 3D terrestrial laser scan data

Cited by 53 publications

References 30 publications

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Sidewalk Detection and Pavement Characterisation in Historic Urban Environments From Point Clouds: Preliminary Results

Open‐source terrestrial laser scanner for the virtualization of geometrical entities in AEC classrooms

Contact Info

Product

Resources

About