Comparative Analysis Between the Main 3D Scanning Techniques: Photogrammetry, Terrestrial Laser Scanner, and Structured Light Scanner in Religious Imagery: The Case of The Holy Christ of the Blood

In recent years, three-dimensional (3D) scanning has become the main tool for recording, documenting, and preserving cultural heritage in the long term. It has become the “document” most in demand today by historians, curators, and art restorers to carry out their work based on a “digital twin,” that is, a totally reliable and accurate model of the object in question. Thanks to 3D scanning, we can preserve reliable models in digital format of the real state of our heritage, some of which are currently destroyed. The first step is to digitize our heritage with the highest possible quality and precision. To do this, it will be necessary to identify the most appropriate technique. In this article, we will show some of the main digitization techniques currently used in sculpture heritage and the workflows associated with them to obtain high-quality models. Finally, a complete comparative analysis will be made to show their main advantages and disadvantages.

Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

The devices used for human position detection in mechanical safety mainly include safety light curtain, safety laser scanner, safety pad, and vision system. However, these devices may be bypassed when used, and human or equipment cannot be distinguished. To solve this problem, a depth camera is proposed as a human position detection device in mechanical safety. The process of human position detection based on depth camera image information is given; it mainly includes image information acquisition, human presence detection, and distance measurement. Meanwhile, a human position detection method based on Intel RealSense depth camera and MobileNet-SSD algorithm is proposed and applied to robot safety protection. The result shows that the image information collected by the depth camera can detect the human position in real time, which can replace the existing mechanical safety human position detection device. At the same time, the depth camera can detect only human but not mobile devices and realize the separation and early warning of people and mobile devices.

MACHINE LEARNING APPROACH FOR TENAGA NASIONAL BERHAD (TNB) OVERHEAD POWERLINE AND ELECTRICITY POLE INVENTORY USING MOBILE LASER SCANNING DATA

Electricity assets recognition and inventory is a fundamental task in the geospatial-based electrical power distribution management. In Malaysia, Tenaga Nasional Berhad (TNB) aims to complete their assets inventory throughout the country by 2022. Previous research has shown that a method for assets detection especially for TNB is still at an early stage, which mainly relied on manual extraction of the assets from different data sources including mobile laser scanner (MLS). This research aims at evaluating a geospatial method based on machine learning to classify the TNB assets using high density MLS data. The MLS data was collected using Riegl VMQ-1 HA scanner and supported by the base station and control points for point cloud registration purpose. In the first stage the point clouds were classified into ground and non-ground objects. The non-ground points were further classified into different landcover types i.e. vegetation, building, and other classes. The points classified as other classes were used for overhead powerline and electricity poles classification using random forest-based Machine Learning (ML) approach in LiDAR 360 software. Based on the classified point clouds, detailed characteristics of electricity poles (i.e. number of poles, height, diameter and inclination from ground) and overhead powerlines (number of cable segments) were estimated. This information was validated using field collected reference data. The results show that the detection accuracy for electricity poles and overhead power line are 65% and 63% respectively. The estimation of length, diameter and height of the spun pole from point clouds has produced Root Mean Square Error (RMSE) value of 0.081cm, 0.263 cm and 0.372 cm respectively. Meanwhile for the concrete pole, the length, diameter and height has been successfully estimated with the value of RMSE of 0.034 cm, 0.029 cm and 0.331 cm respectively. The length of overhead powerline was estimated with 59.02 cm RMSE. In conclusion, the MLS data had show promising results for a semi-automatic detection and characterization of TNB overhead powerlines and poles in the sub-urban area. Such outcome can be used to support the inventory and maintenance process of the TNB assets.

Validity and reliability of palatal rugae morphometric assessment with 3D laser scanned models

Aim: To assess the reliability and validity of morphometric features on 3D digital models produced by scanning maxillary dental casts of Malaysian Malay subjects. Methods: Dental casts of 20 subjects were scanned using a 3D laser scanner (Next Engine Inc., Santa Monica, California, USA). The palatal rugae morphometric features were assessed on the resulting 3D models using 3-Matic Research 9.0 software (Materialise NV, Heverlee, Belgium). The assessments were repeated by the first and second authors to assess the intra- and interexaminer reliability, respectively. Rugae morphometric features were also evaluated on the conventional plaster models to assess the validity of the 3D method. Results: Kappa values of the validity ranged from 0.807 to 0.922 for rugae shape, size category and direction. The intraclass correlation coefficient (ICC) for rugae number validity was 0.979. For intra-examiner reliability, kappa values ranged from 0.716-1.000 for rugae shape, size category and direction. The ICC for rugae number intra-examiner reliability was 0.949. Kappa values of interexaminer reliability for rugae shape, size category and direction were 0.723-885, while the ICC of rugae number was 0.896. Conclusion: Palatal rugae analyses on 3D digital models scanned by the 3D Next Engine laser scanner using 3-Matic Research 9.0 software are valid and reliable.

Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature Norway spruce forests

Newly developed positioning systems in cut-to-length harvesters enable georeferencing of individual trees with submeter accuracy. Together with detailed tree measurements recorded during processing of the tree, georeferenced harvester data are emerging as a valuable tool for forest inventory. Previous studies have shown that harvester data can be linked to airborne laser scanner (ALS) data to estimate a range of forest attributes. However, there is little empirical evidence of the benefits of improved positioning accuracy of harvester data. The two objectives of this study were to (1) assess the accuracy of timber volume estimation using harvester data and ALS data acquired with different scanners over multiple years and (2) assess how harvester positioning errors affect merchantable timber volume predicted and estimated from ALS data. We used harvester data from 33 commercial logging operations, comprising 93 731 harvested stems georeferenced with sub-meter accuracy, as plot-level training data in an enhanced area-based inventory approach. By randomly altering the tree positions in Monte Carlo simulations, we assessed how prediction and estimation errors were influenced by different combinations of simulated positioning errors and grid cell sizes. We simulated positioning errors of 1, 2, …, 15 m and used grid cells of 100, 200, 300 and 400 m. Values of root mean square errors obtained for cell-level predictions of timber volume differed significantly for the different grid cell sizes. The use of larger grid cells resulted in a greater accuracy of timber volume predictions, which were also less affected by positioning errors. Accuracies of timber volume estimates at logging operation level decreased significantly with increasing levels of positioning error. The results highlight the benefit of accurate positioning of harvester data in forest inventory applications. Further, the results indicate that when estimating timber volume from ALS data and inaccurately positioned harvester data, larger grid cells are beneficial.2

Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

Preliminary Experimental Trial of Effects of Lattice Fence Installation on Honey Bee Flight Height as Implications for Urban Beekeeping Regulations

Urban beekeeping has gained salience because of its significance in biodiversity conservation and community building. Despite this, beekeeping practices in urban areas have received negative perceptions from residents, which stem from public safety concerns. There is, therefore, a need to enhance and/or work on appropriate rules for maximizing the profits while minimizing the risks. Amongst the present regulations, the installation of barriers and setbacks is the most common rule for public safety. However, only a limited number of empirical studies have reported on their effective location and height. Thus, in this study, an experimental apiary was set up with different types of barriers installed with varying distances to observe and measure flyway patterns of honey bees. We used a 3D laser scanner, which obtained 8529 points of highly accurate flight location data in about five hours. Results showed that the heights (1.8 and 0.9 m) of the barriers installed were effective in increasing the flight altitudes. The distance of the fence, which was installed as close as 1 m from the hives, was effective as well. These findings, which showed that barriers and setbacks are effective, can have regulatory implications in designing apiaries in urban spaces, where location is often restricted.