scholarly journals EXPERIMENTS WITH UAS IMAGERY FOR AUTOMATIC MODELING OF POWER LINE 3D GEOMETRY

2015 ◽  
Vol XL-1/W4 ◽  
pp. 403-409 ◽  
Author(s):  
G. Jóźków ◽  
B. Vander Jagt ◽  
C. Toth
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Aerial Images ◽  
Automatic Modeling ◽  
Dense Matching ◽  
3D Geometry ◽  
Mapping Technology

The ideal mapping technology for transmission line inspection is the airborne LiDAR executed from helicopter platforms. It allows for full 3D geometry extraction in highly automated manner. Large scale aerial images can be also used for this purpose, however, automation is possible only for finding transmission line positions (2D geometry), and the sag needs to be estimated manually. For longer lines, these techniques are less expensive than ground surveys, yet they are still expensive. UAS technology has the potential to reduce these costs, especially if using inexpensive platforms with consumer grade cameras. This study investigates the potential of using high resolution UAS imagery for automatic modeling of transmission line 3D geometry. <br><br> The key point of this experiment was to employ dense matching algorithms to appropriately acquired UAS images to have points created also on wires. This allowed to model the 3D geometry of transmission lines similarly to LiDAR acquired point clouds. Results showed that the transmission line modeling is possible with a high internal accuracy for both, horizontal and vertical directions, even when wires were represented by a partial (sparse) point cloud.

scholarly journals Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images

2020 ◽  
Vol 10 (4) ◽  
pp. 1275
Author(s):  
Zizhuang Wei ◽  
Yao Wang ◽  
Hongwei Yi ◽  
Yisong Chen ◽  
Guoping Wang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Aerial Images ◽  
Experimental Result ◽  
Fusion Procedure ◽  
3D Geometry ◽  
Dense Point ◽  
Semantic Point ◽  
Projection Error

Semantic modeling is a challenging task that has received widespread attention in recent years. With the help of mini Unmanned Aerial Vehicles (UAVs), multi-view high-resolution aerial images of large-scale scenes can be conveniently collected. In this paper, we propose a semantic Multi-View Stereo (MVS) method to reconstruct 3D semantic models from 2D images. Firstly, 2D semantic probability distribution is obtained by Convolutional Neural Network (CNN). Secondly, the calibrated cameras poses are determined by Structure from Motion (SfM), while the depth maps are estimated by learning MVS. Combining 2D segmentation and 3D geometry information, dense point clouds with semantic labels are generated by a probability-based semantic fusion method. In the final stage, the coarse 3D semantic point cloud is optimized by both local and global refinements. By making full use of the multi-view consistency, the proposed method efficiently produces a fine-level 3D semantic point cloud. The experimental result evaluated by re-projection maps achieves 88.4% Pixel Accuracy on the Urban Drone Dataset (UDD). In conclusion, our graph-based semantic fusion procedure and refinement based on local and global information can suppress and reduce the re-projection error.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 689-695
Author(s):  
Z. Li ◽  
W. Zhang ◽  
J. Shan
Keyword(s):  
Large Scale ◽  
Traditional Approach ◽  
Holistic Approach ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Optimization Strategy ◽  
Building Roof ◽  
Building Model ◽  
Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

New Methodology of On-Line Monitoring for Transmission Line Galloping

2013 ◽  
Vol 805-806 ◽  
pp. 867-870 ◽  
Author(s):  
Yu Sheng Quan ◽  
Enze Zhou ◽  
Guang Chen ◽  
Xin Zhao
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Large Scale ◽  
Online Monitoring ◽  
Large Area ◽  
Monitoring Method ◽  
Overhead Transmission Line ◽  
Additional Equipment ◽  
On Line ◽  
Wire Cross Section

When the overhead transmission line is galloping, a variety of natural disasters occur on the role of the natural conditions, the vibration of conductor is one of the more serious harm to the power system. Over the past decade, as the construction of EHV and UHV, wire cross-section, tension, suspension height and span of overhead transmission lines are increasing, and hence the number of conductor vibration is significantly increased. Vibration in a large scale will led to frequent tripping or even broken line or tower collapses, which cause large area power failures and impact security and stability operation. Online monitoring method for overhead transmission line dancing is mostly needed to add additional equipment, however, once situated on the route environment overlying ice or high winds and other inclement weather, online monitoring is difficult to achieve. This paper presents a method, which is made correlation analysis based on the voltage and current acquired from both ends of the transmission lines, online monitoring of line galloping can be achieved.

scholarly journals 3D Ocean Water Wave Surface Analysis on Airborne LiDAR Bathymetric Point Clouds

2021 ◽  
Vol 13 (19) ◽  
pp. 3918
Author(s):  
Sajjad Roshandel ◽  
Weiquan Liu ◽  
Cheng Wang ◽  
Jonathan Li
Keyword(s):  
Water Wave ◽  
Large Scale ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Coastal Hazards ◽  
Ocean Water ◽  
Coastal Zones ◽  
Wave Surface ◽  
Dbscan Clustering

Water wave monitoring is a vital issue for coastal research and plays a key role in geomorphological changes, erosion and sediment transportation, coastal hazards, risk assessment, and decision making. However, despite missing data and the difficulty of capturing the data of nearshore fieldwork, the analysis of water wave surface parameters is still able to be discussed. In this paper, we propose a novel approach for accurate detection and analysis of water wave surface from Airborne LiDAR Bathymetry (ALB) large-scale point clouds data. In our proposed method we combined the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method with a connectivity constraint and a multi-level analysis of ocean water surface. We adapted for most types of wave shape anatomies in shallow waters, nearshore, and onshore of the coastal zone. We used a wavelet analysis filter to detect the water wave surface. Then, through the Fourier Transformation Approach, we estimated the parameters of wave height, wavelength, and wave orientation. The comparison between the LiDAR measure estimation technique and available buoy data was then presented. We quantified the performance of the algorithm by measuring the precision and recall for the waves identification without evaluating the degree of over-segmentation. The proposed method achieves 87% accuracy of wave identification in the shallow water of coastal zones.

scholarly journals IMAGE TO POINT CLOUD TRANSLATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK FOR AIRBORNE LIDAR DATA

2021 ◽  
Vol V-2-2021 ◽  
pp. 169-174
Author(s):  
T. Shinohara ◽  
H. Xiu ◽  
M. Matsuoka
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Real Point ◽  
3D Point Cloud ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
3D Point Clouds ◽  
Latent Features

Abstract. This study introduces a novel image to a 3D point-cloud translation method with a conditional generative adversarial network that creates a large-scale 3D point cloud. This can generate supervised point clouds observed via airborne LiDAR from aerial images. The network is composed of an encoder to produce latent features of input images, generator to translate latent features to fake point clouds, and discriminator to classify false or real point clouds. The encoder is a pre-trained ResNet; to overcome the difficulty of generating 3D point clouds in an outdoor scene, we use a FoldingNet with features from ResNet. After a fixed number of iterations, our generator can produce fake point clouds that correspond to the input image. Experimental results show that our network can learn and generate certain point clouds using the data from the 2018 IEEE GRSS Data Fusion Contest.

Waveform Relaxation-Based Large-Scale Circuit Simulation for MOSFET and Lossy Coupled Transmission Line Circuits

2011 ◽  
Vol 383-390 ◽  
pp. 2031-2037
Author(s):  
Chun Jung Chen ◽  
Chih Jen Lee ◽  
Chang Lung Tsai ◽  
Allen Y. Chang ◽  
Tien Hao Shih
Keyword(s):  
Transmission Line ◽  
Circuit Design ◽  
Transmission Lines ◽  
Large Scale ◽  
Circuit Simulation ◽  
Waveform Relaxation ◽  
Full Time ◽  
Calculation Algorithm ◽  
Simulation Process ◽  
Coupled Transmission Lines

In this paper, we propose a modified Waveform Relaxation algorithm to perform large-scale circuit simulation for MOSFET circuits containing lossy coupled transmission lines that have been encountered in modern circuit design community, in which a full time-domain transmission line calculation algorithm based on the Method of Characteristic is adopted. New software techniques are proposed to enhance the robustness as well as efficiency of the simulation process. All proposed methods have been implemented and executed to justify the claimed advantages.

scholarly journals Segmentation and Reconstruction of Buildings with Aerial Oblique Photography Point Clouds

2015 ◽  
Vol XL-7/W4 ◽  
pp. 109-112 ◽  
Author(s):  
P. Liu ◽  
Y. C. Li ◽  
W. Hu ◽  
X. B. Ding
Keyword(s):  
Large Scale ◽  
Stereo Matching ◽  
Point Clouds ◽  
Dense Matching ◽  
Dense Point ◽  
Excellent Method ◽  
City Model ◽  
Oblique Images ◽  
Geometrical Relationship ◽  
Building Reconstruction

Oblique photography technology as an excellent method for 3-D city model construction has brought itself to large-scale recognition and undeniable high social status. Tilt and vertical images with the high overlaps and different visual angles can produce a large number of dense matching point clouds data with spectral information. This paper presents a method of buildings reconstruction with stereo matching dense point clouds from aerial oblique images, which includes segmentation of buildings and reconstruction of building roofs. We summarize the characteristics of stereo matching point clouds from aerial oblique images and outline the problems with existing methods. Then we present the method for segmentation of building roofs, which based on colors and geometrical derivatives such as normal and curvature. Finally, a building reconstruction approach is developed based on the geometrical relationship. The experiment and analysis show that the methods are effective on building reconstruction with stereo matching point clouds from aerial oblique images.

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