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

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 239-246
Author(s):  
M. S. A. Mohd Rapheal ◽  
A. Farhana ◽  
M. R. Mohd Salleh ◽  
M. Z. Abd Rahman ◽  
Z. Majid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Distribution ◽  
Laser Scanning ◽  
Early Stage ◽  
Electrical Power ◽  
Laser Scanner ◽  
Point Clouds ◽  
Base Station ◽  
Detection Accuracy ◽  
Distribution Management

Abstract. 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.

scholarly journals CLASSIFICATION OF AERIAL LASER SCANNING POINT CLOUDS USING MACHINE LEARNING: A COMPARISON BETWEEN RANDOM FOREST AND TENSORFLOW

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1105-1111 ◽  
Author(s):  
F. Pirotti ◽  
F. Tonion
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Training Data ◽  
Semantic Classification ◽  
The Neural Network

<p><strong>Abstract.</strong> In this investigation a comparison between two machine learning (ML) models for semantic classification of an aerial laser scanner point cloud is presented. One model is Random Forest (RF), the other is a multi-layer neural network, TensorFlow (TF). Accuracy results were compared over a growing set of training data, using a stratified independent sampling over classes from 5% to 50% of the total dataset. Results show RF to have average F1&amp;thinsp;=&amp;thinsp;0.823 for the 9 classes considered, whereas TF had average F1&amp;thinsp;=&amp;thinsp;0.450. F1 values where higher for RF than TF, due to complexity in the determination of a suitable composition of the hidden layers of the neural network in TF, and this can likely be improved to reach higher accuracy values. Further study in this sense is planned.</p>

Optical methods and wireless sensors for monitoring of bridges

2019 ◽  
Author(s):  
Cosmin Popescu ◽  
Björn Täljsten ◽  
Thomas Blanksvärd ◽  
Gabriel Sas ◽  
Alexander Jimenez ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Crack Opening ◽  
Point Clouds ◽  
Base Station ◽  
Optical Methods ◽  
Railway Bridges ◽  
Wireless Monitoring ◽  
Building Information ◽  
Close Range ◽  
Traffic Disturbance

<p>Six railway bridges have been scanned using infrared scanning (IR), close range photogrammetry (CRP) and terrestrial laser scanning (TRS) to reconstruct point clouds and evaluate the potential of the technologies for building information modelling (BIM) and assessment purposes. The results may also help to improve bridge inspection routines. This is done by evaluating the accuracy and quality of the point clouds, time consumption, safety and traffic disturbance.</p><p>Wireless Monitoring has been used in a demonstration project in Sweden. It consists of a base station and nodes. The base station receives signals from the node antennas and transmits the signals to the cloud. The nodes are equipped with strain gauges, crack opening devices, temperature sensors or other suitable sensors for the investigation purpose. Results from the methods and conclusions regarding further use will be presented.</p>

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals INTEGRATING TOPOGRAPHIC, PHOTOGRAMMETRIC AND LASER SCANNING TECHNIQUES FOR A SCAN-TO-BIM PROCESS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 883-890
Author(s):  
M. Lo Brutto ◽  
E. Iuculano ◽  
P. Lo Giudice
Keyword(s):  
Laser Scanning ◽  
Building Information Modeling ◽  
Laser Scanner ◽  
Point Clouds ◽  
Parametric Modeling ◽  
Information Modeling ◽  
Survey Method ◽  
The Real ◽  
Building Information ◽  
Heritage Building

Abstract. The preservation of historic buildings can often be particularly difficult due to the lack of detailed information about architectural features, construction details, etc.. However, in recent years considerable technological innovation in the field of Architecture, Engineering, and Construction (AEC) has been achieved by the Building Information Modeling (BIM) process. BIM was developed as a methodology used mainly for new construction but, given its considerable potential, this approach can also be successfully used for existing buildings, especially for buildings of historical and architectural value. In this case, it is more properly referred to as Historic – or Heritage – Building Information Modeling (HBIM). In the HBIM process, it is essential to precede the parametric modeling phase of the building with a detailed 3D survey that allows the acquisition of all geometric information. This methodology, called Scan-to-BIM, involves the use of 3D survey techniques for the production of point clouds as a geometric “database” for parametric modeling. The Scan-to-BIM approach can have several issues relating to the complexity of the survey. The work aims to apply the Scan-to-BIM approach to the survey and modeling of a historical and architectural valuable building to test a survey method, based on integrating different techniques (topography, photogrammetry and laser scanning), that improves the data acquisition phase. The “Real Cantina Borbonica” (Cellar of Royal House of Bourbon) in Partinico (Sicily, Italy) was chosen as a case study. The work has allowed achieving the HBIM of the “Real Cantina Borbonica” and testing an approach based exclusively on a topographic constraint to merge in the same reference system all the survey data (laser scanner and photogrammetric point clouds).

scholarly journals COMBINATION OF THERMAL AND GEOMETRIC INFORMATION FOR BIM ENRICHMENT

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 719-725 ◽  
Author(s):  
H. Macher ◽  
M. Boudhaim ◽  
P. Grussenmeyer ◽  
M. Siroux ◽  
T. Landes
Keyword(s):  
Laser Scanning ◽  
Thermal Analyses ◽  
Laser Scanner ◽  
Point Clouds ◽  
Existing Buildings ◽  
Energy Audit ◽  
Geometric Information ◽  
Information Models ◽  
Building Information ◽  
Reduction Of Energy Consumption

<p><strong>Abstract.</strong> In the context of building renovation, infrared (IR) cameras are widely used to perform the energy audit of buildings. They allow analysing precisely the energetic performances of existing buildings and thermal analyses represent a key step for the reduction of energy consumption. They are also used to assess the thermal comfort of people living or working in a building. Building Information Models (BIM) are widespread to plan the rehabilitation of existing buildings and laser scanning is now commonly used to capture the geometry of buildings for as-built BIM creation. The combination of thermographic and geometric data presents a high number and variety of applications (Lagüela and Díaz-Vilariño, 2016). However, geometric and thermal information are generally acquired separately by different building stakeholders and thermal analyses are performed with independence of geometry. In this paper, the combination of thermal and geometric information is investigated for indoor of buildings. The aim of the project is to create 3D thermographic point clouds based on data acquired by a laser scanner and a thermal camera. Based on these point clouds, BIM models might be enriched with thermal information through the scan-to-BIM process.</p>

A novel method for extracting skeleton of fruit tree from 3D point clouds

2020 ◽  
pp. 2050051
Author(s):  
Shenglian lu ◽  
Guo Li ◽  
Jian Wang
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Tree Canopy ◽  
3D Point Clouds ◽  
Novel Method ◽  
Manual Interaction ◽  
Traditional Approaches

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

Influence of ULS data acquisition characteristics on the achievable stem reconstruction accuracies

2020 ◽  
Author(s):  
Moritz Bruggisser ◽  
Johannes Otepka ◽  
Norbert Pfeifer ◽  
Markus Hollaus
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Diameter ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Detection Rates ◽  
Point Count ◽  
Tree Stem

&lt;p&gt;Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation.&amp;#160;&lt;br&gt;The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.&lt;/p&gt;

scholarly journals Geoarchaeological site documentation and analysis of 3D data derived by terrestrial laser scanning

2014 ◽  
Vol II-5 ◽  
pp. 173-179 ◽  
Author(s):  
D. Hoffmeister ◽  
S. Zellmann ◽  
K. Kindermann ◽  
A. Pastoors ◽  
U. Lang ◽  
...  
Keyword(s):  
Cross Sections ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
3D Data ◽  
Research Problems ◽  
Survey Results ◽  
Rtk Gps ◽  
Derived Data

Terrestrial laser scanning was conducted to document and analyse sites of geoarchaeological interest in Jordan, Egypt and Spain. In those cases, the terrestrial laser scanner LMS-Z420i from Riegl was used in combination with an accurate RTK-GPS for georeferencing of the point clouds. Additionally, local surveying networks were integrated by established transformations and used for indirect registration purposes. All data were integrated in a workflow that involves different software and according results. The derived data were used for the documentation of the sites by accurate plans and cross-sections. Furthermore, the 3D data were analysed for geoarchaeological research problems, such as volumetric determinations, the ceiling thickness of a cave and lighting simulations based on path tracing. The method was reliable in harsh environmental conditions, but the weight of the instrument, the measuring time and the minimum measurement distance were a drawback. However, generally an accurate documentation of the sites was possible. Overall, the integration in a 3D GIS is easily possible by the accurate georeference of the derived data. In addition, local survey results are also implemented by the established transformations. Enhanced analyses based on the derived 3D data shows promising results.

scholarly journals Monitoring marginal erosion in hydroelectric reservoirs with terrestrial mobile laser scanner

2014 ◽  
Vol XL-5 ◽  
pp. 589-596 ◽  
Author(s):  
A. M. G. Tommaselli ◽  
M. V. A. Moraes ◽  
L. S. L. Silva ◽  
M. F. Rubio ◽  
G. J. Carvalho ◽  
...  
Keyword(s):  
Cross Sections ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Field Work ◽  
Hydroelectric Power ◽  
Suitable Alternative ◽  
Joint Project ◽  
Erosion Processes ◽  
Marginal Erosions

Marginal erosions in reservoirs of hydroelectric plants have caused economic and environmental problems concerning hydroelectric power generation, reduction of productive areas and devaluing land parcels. The real extension and dynamics of these erosion processes are not well known for Brazilian reservoirs. To objectively assess these problems Unesp (Univ Estadual Paulista) and Duke Energy are developing a joint project which aims at the monitoring the progression of some erosive processes and understanding the causes and the dynamics of this phenomenon. Mobile LASER scanning was considered the most suitable alternative for the challenges established in the project requirements. A MDL DynaScan Mobile LASER M150 scanner was selected which uses RTK for real time positioning integrated to an IMU, enabling instantaneous generation of georeferenced point clouds. Two different reservoirs were choose for monitoring: Chavantes (storage plant) and Rosana (run-of-river plant), both in the Paranapanema River, border of São Paulo and Paraná States, Brazil. The monitoring areas are scanned quarterly and analysed with base on the point cloud, meshes, contours and cross sections. Cross sections are used to visualize and compute the rate and the dynamics of erosion. Some examples and quantitative results are presented along with an analysis of the proposed technique. Some recommendations to improve the field work and latter data processing are also introduced.

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