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Accidents and fatalities during scaffolding work are significant concerns in the construction industry. Scaffold monitoring technology based on 3D reconstruction can offer a potential solution to prevent such fatalities. However, the practical application of this technology at actual construction sites faces a notable challenge due to the presence of safety nets. Safety nets hinder the acquisition of complete scaffold point clouds necessary for reconstruction. To address this issue, we propose a novel registration method for scaffold point clouds that considers safety nets, employing sensor fusion and deep learning techniques. The method consists of three steps: 1) LiDAR-camera calibration and SLAM-based point cloud data acquisition, 2) object detection and coordinate transformation, and 3) identification of scaffold installation stages and point cloud registration. The proposed method was verified to be effective in registering scaffold point clouds without safety nets. • The proposed method registers scaffold point clouds considering the installation of safety nets for 3D reconstruction. • Sensor fusion and deep learning are used to estimate the global coordinates of objects during scaffolding work. • By identifying the installation stages of scaffolds, a point cloud map without safety nets is obtained. • The effectiveness of the method is validated at the real scaffold installation site. [ABSTRACT FROM AUTHOR]

Falls from scaffolds cause the majority of accidents and fatalities at construction sites. A deep learning-based 3D reconstruction technology could provide a solution to prevent such fatalities through automated scaffold monitoring. However, when the technology was used at a large-scale construction site, there were limitations, such as the scarcity of point cloud data and the non-uniformity of points. To address this issue, this paper presents a large-scale scaffold reconstruction method using synthetic scaffold datasets and an upsampling adversarial network. The method consists of four steps: 1) data acquisition of scaffold point cloud through a mobile laser scanning (MLS) system, 2) 3D semantic segmentation using synthetic datasets, 3) upsampling of the segmented scaffold points, and 4) automatic generation of a 3D CAD model. The performance of the segmentation model trained with synthetic datasets achieved an 80.83% F1 score, which improved to 94.93% after upsampling. • The proposed method reconstructs a large-scale scaffold using a MLS system. • A synthetic dataset generation algorithm was developed to overcome data scarcity. • Scaffold points became dense after using an upsampling adversarial network. • An F1 score for scaffold point clouds was 94.93%. [ABSTRACT FROM AUTHOR]

Building accessibility diagnosis is of high interest especially in case of people with reduced mobility. This paper proposes a methodology for automated detection of inaccessible steps in building façade entrances from MLS (mobile laser scanner) data. Our approach uses the MLS trajectory to automatically subdivide urban point clouds into regular stretches. From each stretch, the lower zone of façade is isolated and selected as region of interest. Points belonging to vertical elements are projected onto a 2D image and steps are detected and classified as inaccessible areas according to the comparison of geometrical features such as height jump, proximity to ground and width, with regulation. The methodology has been tested in four real datasets, which constitute > 400 m of different urban scenarios. Results exhibit a robust performance under urban scenes with a high variability of façade geometry due to the presence of different entrance types to shops and dwellings. Results have been quantitatively evaluated and they show global F1 value around 93%. Moreover, the methodology is very fast since 100 m are processed in < 2 min. [ABSTRACT FROM AUTHOR]

Vehicle-related ground occlusion is a common problem in MLS data. This study aims to design a detection and reconstruction method of static vehicle-related ground occlusion for MLS data. Ground extraction and vehicle segmentation are performed on the input point cloud data in advance. Then an α-shape boundary based on the prior vehicle geometry is designed to split non-ground empty area and ground occlusions. The occlusion is detected and matched with its corresponding vehicle using the relative position between them. This relative position relation and the height difference are used to detect the curb direction as the local road direction. Finally, the occlusions are reconstructed using two different methods: (1) a cell-based linear interpolation and (2) a point-based mathematical morphology. The methodology is tested by original scanned data and multi-temporal evaluation data captured from a residential area in Delft, the Netherlands with vehicle-mounted LiDAR sensors. The result shows that all occlusions cause by vehicles are successfully detected and the curb (road) direction is correctly extracted in most of the occluded areas. Both reconstructed results can visually integrate the original scanned data and recover the curb structure. The reconstruction errors of the linear interpolation method are 0.045 m in the z-axis direction and 0.051 m in total and the reconstruction errors of mathematical morphology are 0.048 m in the z-axis direction and 0.052 m in total. • Ground boundaries are detected with an α-shape based on prior knowledge of vehicle. • Occlusions and vehicles are matched with the relative positions between them. • The local road direction is accurately estimated by detecting the curb direction. • A fast linear interpolation method is designed to reconstruct occlusions. • A point-based mathematical morphology method is proposed for reconstructions. [ABSTRACT FROM AUTHOR]

Although a scaffold is an essential structure in the construction industry, it may also be a dangerous factor that causes fatalities. However, the process of monitoring the scaffold is labor-intensive because it is conducted by the subjective observation of safety managers. To address this issue, we propose an automatic scaffold 3D reconstruction method using 3D point cloud data acquired using a robot dog. The method consists of three steps: 1) data acquisition of scaffold point clouds through a robot dog scanning system, 2) deep learning-based 3D semantic segmentation, and 3) automatic formation of a 3D CAD model. We created 15 robot dog datasets for training the segmentation model. The proposed method was tested at a different site where a scaffold with a representative structure was attached to the wall. The proposed method demonstrated an excellent performance of point cloud segmentation with a 90.84% F1 score. • The proposed method reconstructs a scaffold using a robot dog scanning system. • A robot dog efficiently acquired 3D point cloud of scaffold in various positions. • The method is based on the 3D semantic segmentation model with transfer learning. • In robot dog datasets, F1 score was 90.84%. [ABSTRACT FROM AUTHOR]

The development of the IFC 4.3 candidate standard sets the pathway for interoperability amongst different infrastructures that have been harmonized under the buildingSMART project proposals. Furthermore, the use of laser scanning as a tool for the monitoring of infrastructures has also been rising. This paper showcases a combination of BIM and remote sensing for infrastructure in a road scenario. A semi-automatic approach is presented for the IFC 4.1 compliant modelling of traffic signs and guardrails, along with the inclusion of some semantics via property sets. The input data for the IFC modelling procedure is obtained from the automatic processing of a 3D point cloud, that extracts road alignment, traffic signs, and guardrails, and from standardized dimensions for certain assets. The point cloud processing is validated for the guardrail identification and the IFC modelling is described alongside an UML, exemplifying the possibility of modelling the as-is state of the road infrastructure. • IFC-compliant model generation of guardrails and traffic signs • IFC model of road asset containing target elements and additional semantics • Processing of 3D points clouds to extract guardrails and traffic signs • Guardrail extraction obtains F-score of 98% on the modelled point cloud [ABSTRACT FROM AUTHOR]

The amount of sight distance available to drivers is an extremely critical design element with significant impacts on the safe operation of highways. If the available sight distance on a roadway is limited, this reduces the ability of drivers to safely carry out certain maneuvers such as stopping or passing. Obstructions to sight distance are often caused by a range of different factors. This includes roadside vegetation overgrowing into the road's right of way and obstructions related to the road's alignment. To ensure that sufficient sight distance is available throughout a road's service life, transportation agencies are required to conduct regular long site visits to identify the existence of any obstructions while identifying the types of those obstructions. Although many studies have attempted quantifying available sight distance in the past, not much work has been done to automatically identify and study the types of obstructions causing sight distance limitations. This information is critical to entities looking to decide on the appropriate construction activity required to alleviate sight distance limitations. To fill in this gap, this paper proposes a fully automated novel method to assess sight distance on highways scanned using mobile LiDAR technology, while also proposing a method for the automated classification of the type of obstruction detected. The algorithm is tested on multiple roadway segments in Alberta, Canada and was successful in identifying limitations in sight distance and classifying obstructions into different types of objects. • Automated LiDAR based assessment of visibility on highways. • An algorithm for automated detection of sight distance obstructions is proposed. • The algorithm also automatically classifies obstructions into different objects. • High accuracy achieved after testing on multiple highway segments in Alberta. • Obstructions due to road, signs, vegetation...etc. are detected and classified. [ABSTRACT FROM AUTHOR]

During an emergency inside large buildings such as hospitals and shopping malls, the availability of up-to-date information is critical. One common source of information is the 2D layout of buildings and emergency exits. For most buildings, this information is represented as tangled floor plans, which in most cases are outdated. One solution to update the data of buildings after each renovation is to recreate 3D models of buildings in a quick and automatic approach. These 3D models provide proactively crucial building information in a digital format for first responders to be used in emergency cases. Thanks to advances in remote sensing, laser scanners can be used to generate an accurate spatial representation of buildings quickly. However, such devices provide point clouds, which are unstructured data. In this paper, we introduce a complete workflow that allows to generate 3D models from point clouds of buildings and extract fine-grained indoor navigation networks from those models, to support advanced path planning for disaster management and navigation of different types of agents. The process extracts structural elements of buildings such as walls, slabs, ceiling and openings, and reconstruct their volumetric shapes. Additionally, the furnishing elements in the input point clouds are identified and reconstructed as the obstacles. Stairs are also reconstructed to allow multistory navigation path planning. Our algorithm is fully 3D and can handle vertical and slanted structures. We test it on several real datasets, compared it to the state-of-the-art approaches and provide a process to check the consistency of the reconstruction, which allows in return to further improve its result. • A method is presented for indoor 3D reconstruction from point clouds of multi-story buildings. • A room reconstruction method is developed based on space-enclosure. • Modeling of furniture, obstacles, doors and stairs is included for optimal path finding. • The flexible space subdivision (FSS) provides a fine-grained navigation network in 2D and 3D. • Several heuristic control rules verify the consistency of the final 3D model. [ABSTRACT FROM AUTHOR]

The application of Deep Learning techniques to point clouds for urban object classification is limited by the large number of samples needed. Acquiring and tagging point clouds is more expensive and tedious labour than its image equivalent process. Point cloud online datasets contain few samples for Deep Learning or not always the desired classes This work focuses on minimizing the use of point cloud samples for neural network training in urban object classification. The method proposed is based on the conversion of point clouds to images (pc-images) because it enables: the use of Convolutional Neural Networks, the generation of several samples (images) per object (point clouds) by means of multi-view, and the combination of pc-images with images from online datasets (ImageNet and Google Images). The study is conducted with ten classes of objects extracted from two street point clouds from two different cities. The network selected for the job is the InceptionV3. The training set consists of 5000 online images with a variable percentage (0% to 10%) of pc-images. The validation and testing sets are composed exclusively of pc-images. Although the network trained only with online images reached 47% accuracy, the inclusion of a small percentage of pc-images in the training set improves the classification to 99.5% accuracy with 6% pc-images. The network is also applied at IQmulus & TerraMobilita Contest dataset and it allows the correct classification of elements with few samples. • Automatic point cloud urban object classification • Training set mainly formed by images obtained from online datasets • Conversion of point clouds to images to take advantage of Convolutional Neural Networks • 99.5% accuracy with only 6% images obtained from point clouds in the training set [ABSTRACT FROM AUTHOR]

Accurate data collected from on-going construction projects assists project field engineers with tracking the progress of construction work. The comparison of as-planned schedules against the as-built status of construction enables the involved parties to determine project performance. Periodic monitoring of project performance enables timely identification of discrepancies between the schedule baseline and the project schedule. The rapid identification of discrepancies allows necessary measures to be taken to minimize the impact of a delay on the construction workflow. In transportation projects, the traditional means of acquiring as-built data from a construction site prevents involved parties from receiving the required information from the site in a timely manner. The delay in the communication of information ultimately causes subsequent delays in implementing necessary courses of action targeted toward improving the workflow. Therefore, this project develops a technology-supplemented progress monitoring approach. The approach implements as-built data with a high level of detail is obtained from a jobsite within a reasonable period of time using lidar technology while ensuring the safety of the data collector. The proposed framework utilizes point cloud data obtained using mobile lidar technology and 4D design models to identify deviations of the performed work from the planned work. The framework was tested using as-built data acquired from an on-going bridge construction project, and Percentage of Completion (POC) for the as-built bridge elements are calculated and compared with the as-planned POC. The differences between these two POC values for every element, on a particular scan date, are used for assessing the performance of the proposed framework. The obtained POC difference values ranged from −7% to 6% for most elements, which shows that the developed framework enables tracking the completion of individual bridge elements in an accurate and efficient manner. • 3D design models, construction schedule and mobile lidar data were used to report project progress information. • POC for the as-built bridge elements are calculated and compared with the as-planned POC. • Individual bridge elements can be monitored accurately and efficiently using the proposed framework. • The manual processing time for each epoch is approximately 1 h and 15 min for the case study. [ABSTRACT FROM AUTHOR]

Shield subway tunnels are critical urban infrastructure and precise, rapid deformation detection method is required for the maintenance of these tunnels. A tunnel deformation detection system called Railway Mobile Measurement System (RMMS) was developed based on mobile laser scanning technique. Herein, detailed analysis is given to prove that profiles from the laser scanner can be seen as cross-sections of the tunnel and that the corresponding error can be ignored. A wavelet filtering algorithm was developed to filter the point cloud of tunnel ancillary facilities in the cross-section points. A subsequent ellipse fitting and deformation detection method is proposed. A cross-section location method for subway tunnels is also presented based on the greyscale image converted from the tunnel's point cloud. A tunnel ring segments seams detection method is proposed to detect tunnel ring segments dislocation. The accuracy of the system was verified in a subway tunnel in Wuhan, China with a total station. The accuracy of cross-section measurement was 1.5 mm, the repeatability of the overall and local deformation measurement was 0.5 and 0.9 mm, respectively. This system completely fulfils the accuracy requirement of railway tunnels deformation detection and its efficiency is significantly higher than that of the traditional method. • The error of directly obtained laser scanning cross-section points of subway tunnels is analyzed in detail. • A wavelet filtering algorithm is proposed to filter point cloud of tunnel ancillary facilities. • Tunnel's overall and local deformation is detected based on ellipse fitting of cross-section points. • A tunnel ring segments dislocation detection method based on template matching and segments seams detection is proposed. [ABSTRACT FROM AUTHOR]

Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3% and 93.6%, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70% of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network. • Power lines are mapped automatically from mobile laser scanning (MLS) point clouds. • MLS complements aerial power line inspection reducing costs and increasing quality. • Accuracy of automatic power line mapping using all-terrain MLS is higher than 93%. • Both all-terrain MLS platforms, ATV and personal backpack, provide similar accuracy. Accuracy is higher in the open field compared to forest because of GNSS outages. [ABSTRACT FROM AUTHOR]