Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

► Journal Browser

Remote Sensing for Power Line Corridor Surveys

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 19332

Share This Special Issue

Special Issue Editors

Dr. Levente Klein

E-Mail Website
Guest Editor

IBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA
Interests: remote sensing; deep learning; big data; geo-spatial data
Special Issues, Collections and Topics in MDPI journals

Dr. Andre Dias

E-Mail Website
Guest Editor

INESC Technology and Science, Centre for Robotics and Autonomous Systems, 4200-465 Porto, Portugal
Interests: autonomous systems; field robotics; mobile robotics; intelligent robots; marine robotics

Dr. Mohammad Awrangjeb

E-Mail Website
Guest Editor

Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia
Interests: deep learning; remote sensing image processing; point cloud processing; change detection; object recognition; object modelling; remote sensing data registration; remote sensing of environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Critical assets like power lines, oil and gas pipelines and transportation networks play a critical role in global economy. These assets span millions of kilometres in length and crossing both remote and dense urban areas and they become unoperational due to accidental human activity, natural hazards or deliberate harm. Continuous observations of the critical assets and their surroundings can enable reliable operations, asset protection and real time preventive maintenance. Fusion of optical satellite, visual and thermographic cameras, Synthetic Aperture Radar, and LiDAR data enables near real time detection of hazardous conditions. Machine learning techniques enables these assets’ reliability monitoring and automated identification of vegetation overgrowth, illegal construction in right of way, and natural hazard detection events (landslides and flooding). Future challenges brought by climate change will require development of new detection technologies and machine learning models on remote sensing imagery to increase power grid reliability, prevent power outages and to minimize the potential for wildfires.

We encourage submissions of original manuscripts, focusing on scalable and accurate power line corridor monitoring technology, including, but not limited to:

Vegetation detection in power line corridors using optical satellite, SAR and LiDAR imagery
Development of new imaging techniques including drones and autonomous vehicles
Detection of hazard conditions around critical assets
Remote sensing of infrastructure; pipeline, railways, power lines
Integration of remote sensing data in outage prediction
Multi-Sensor fusion for detection and anomaly identification

Dr. Levente Klein
Dr. Andre Dias
Dr. Mohammad Awrangjeb

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

LiDAR, autonomous vehicles
Visual and thermographic cameras
Satellite and synthetic aperture radar
Vegetation management
Pipeline, Railway and Power line assets monitoring
Climate change and Natural hazard around critical assets

Published Papers (7 papers)

Download All Papers

Research

Jump to: Review

18 pages, 5263 KiB

Open AccessArticle

End-to-End Powerline Detection Based on Images from UAVs

by Jingwei Hu, Jing He and Chengjun Guo

Remote Sens. 2023, 15(6), 1570; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061570 - 13 Mar 2023

Viewed by 1601

Abstract

Transmission line detection is the basic task of using UAVs for transmission line inspection and other related tasks. However, the detection results based on traditional methods are vulnerable to noise, and the results may not meet the requirements. The deep learning method based on segmentation may cause a lack of vector information and cannot be applied to subsequent high-level tasks, such as distance estimation, location, and so on. In this paper, the characteristics of transmission lines in UAV images are summarized and utilized, and a lightweight powerline detection network is proposed. In addition, due to the reason that powerlines often run through the whole image and are sparse compared to the background, the FPN structure with Hough transform and the neck structure with multi-scale output are introduced. The former can make better use of edge information in a deep neural network as well as reduce the training time. The latter can reduce the error caused by the imbalance between positive and negative samples, make it easier to detect the lines running through the whole image, and finally improve the network performance. This paper also constructs a powerline detection dataset. While the net this paper proposes can achieve real-time detection, the f-score of the detection dataset reaches 85.6%. This method improves the effect of the powerline extraction task and lays the groundwork for subsequent possible high-level tasks. Full article

(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)

► Show Figures

Figure 1

27 pages, 2376 KiB

Open AccessArticle

Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network

by Rytis Maskeliūnas, Raimondas Pomarnacki, Van Khang Huynh, Robertas Damaševičius and Darius Plonis

Remote Sens. 2023, 15(1), 194; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010194 - 29 Dec 2022

Cited by 5 | Viewed by 2034

Abstract

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for analyzing and monitoring power line integrity. The results of experiments performed over 32 km long power line under different scenarios are presented. The proposed framework may be useful for monitoring traditional power lines as well as alternative energy source parks and large users like industries. We discovered that the quantity of data transferred changes based on the problem and the size of the planned data packet. When all phases were absent from all meters, we noted a significant decrease in the amount of data collected from the power line of interest. This implies that there is a power outage during the monitoring. When even one phase is reconnected, we only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network was able to identify and classify simulated 190 entire power outages and 700 single phase outages. The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of conceivable cases of errors and disturbances of 0.12% for the whole operation. Full article

(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)

► Show Figures

Figure 1

28 pages, 37409 KiB

Open AccessArticle

Dual-View Stereovision-Guided Automatic Inspection System for Overhead Transmission Line Corridor

by Yaqin Zhou, Chang Xu, Yunfeng Dai, Xingming Feng, Yunpeng Ma and Qingwu Li

Remote Sens. 2022, 14(16), 4095; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164095 - 21 Aug 2022

Cited by 6 | Viewed by 1832

Abstract

Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected according to the generated 3D points of the transmission line corridor. Tests on both corresponding datasets and practical 220-kV transmission line corridors are conducted. The experimental results of different modules reveal that our proposed system can be applied in practical inspection environments and has good performance. Full article

(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)

► Show Figures

Graphical abstract

20 pages, 7785 KiB

Open AccessArticle

A Transmission Tower Tilt State Assessment Approach Based on Dense Point Cloud from UAV-Based LiDAR

by Zhumao Lu, Hao Gong, Qiuheng Jin, Qingwu Hu and Shaohua Wang

Remote Sens. 2022, 14(2), 408; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020408 - 17 Jan 2022

Cited by 14 | Viewed by 3000

Abstract

Transmission towers are easily affected by various meteorological and geological disasters. In this paper, a transmission tower tilt state assessment approach—based on high precision and dense point cloud from UAV LiDAR—was proposed. First, the transmission tower point cloud was rapidly located and extracted from the 3D point cloud obtained by UAV-LiDAR line patrol. A robust histogram local extremum extraction method with additional constraints was proposed to achieve adaptive segmentation of the tower head and tower body point cloud. Second, an accurate and efficient extraction and simplification strategy of the contour of the feature plane point cloud was proposed. The central axis of the tower was constrained by the contour of the feature plane through the four-prism structure to calculate the tilt angle of the tower and evaluate the state of the tower. Finally, the point cloud of tower head from UAV-based LiDAR was accurately matched with the designed tower head model from database, and a tower head state evaluation model based on matching offset parameters was proposed to evaluate tower head tilt state. The experimental results of simulation and measured data showed that the calculation accuracy of the tilt parameters of transmission tower body was better than 0.5 degrees, that the proposed method can effectively evaluate the risk of tower head with complex structure, and improve the rapid and mass intelligent perception level of the risk state of the transmission line tower, which has a wide prospects for application. Full article

(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)

► Show Figures

Figure 1

24 pages, 7079 KiB

Open AccessArticle

An Entropy-Weighting Method for Efficient Power-Line Feature Evaluation and Extraction from LiDAR Point Clouds

by Junxiang Tan, Haojie Zhao, Ronghao Yang, Hua Liu, Shaoda Li and Jianfei Liu

Remote Sens. 2021, 13(17), 3446; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173446 - 30 Aug 2021

Cited by 18 | Viewed by 2414

Abstract

Power-line inspection is an important means to maintain the safety of power networks. Light detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. However, it is still a challenge to automatically detect power lines with high precision. To achieve efficient and accurate power-line extraction, this paper proposes an algorithm using entropy-weighting feature evaluation (EWFE), which is different from the existing hierarchical-multiple-rule evaluation of many geometric features. Six significant features are selected (Height above Ground Surface (HGS), Vertical Range Ratio (VRR), Horizontal Angle (HA), Surface Variation (SV), Linearity (LI) and Curvature Change (CC)), and then the features are combined to construct a vector for quantitative evaluation. The feature weights are determined by an entropy-weighting method (EWM) to achieve optimal distribution. The point clouds are filtered out by the HGS feature, which possesses the highest entropy value, and a portion of non-power-line points can be removed without loss of power-line points. The power lines are extracted by evaluation of the other five features. To decrease the interference from pylon points, this paper analyzes performance in different pylon situations and performs an adaptive weight transformation. We evaluate the EWFE method using four datasets with different transmission voltage scales captured by a light unmanned aerial vehicle (UAV) LiDAR system and a mobile LiDAR system. Experimental results show that our method demonstrates efficient performance, while algorithm parameters remain consistent for the four datasets. The precision F value ranges from 98.4% to 99.7%, and the efficiency ranges from 0.9 million points/s to 5.2 million points/s. Full article

(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)

► Show Figures

Graphical abstract

29 pages, 5527 KiB

Open AccessArticle

Advanced Power Line Diagnostics Using Point Cloud Data—Possible Applications and Limits

by Marek Siranec, Marek Höger and Alena Otcenasova

Remote Sens. 2021, 13(10), 1880; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101880 - 11 May 2021

Cited by 11 | Viewed by 3037

Abstract

The advance in remote sensing techniques, especially the development of LiDAR scanning systems, allowed the development of new methods for power line corridor surveys using a digital model of the powerline and its surroundings. The advanced diagnostic techniques based on the acquired conductor geometry recalculation to extreme operating and climatic conditions were proposed using this digital model. Although the recalculation process is relatively easy and straightforward, the uncertainties of input parameters used for the recalculation can significantly compromise such recalculation accuracy. This paper presents a systematic analysis of the accuracy of the recalculation affected by the inaccuracies of the conductor state equation input variables. The sensitivity of the recalculation to the inaccuracy of five basic input parameters was tested (initial temperature and mechanical tension, elasticity modulus, specific gravity load and tower span) by comparing the conductor sag values when input parameters were affected by a specific inaccuracy with an ideal sag-tension table. The presented tests clearly showed that the sag recalculation inaccuracy must be taken into account during the safety assessment process, as the sag deviation can, in some cases, reach values comparable to the minimal clearance distances specified in the technical standards. Full article

(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)

► Show Figures

Graphical abstract

Review

Jump to: Research

23 pages, 1672 KiB

Open AccessReview

Power Line Extraction and Reconstruction Methods from Laser Scanning Data: A Literature Review

by Nosheen Munir, Mohammad Awrangjeb and Bela Stantic

Remote Sens. 2023, 15(4), 973; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040973 - 10 Feb 2023

Cited by 3 | Viewed by 2755

Abstract

Electricity has become an indispensable source of energy, and power lines play a crucial role in the functioning of modern societies. It is essential to inspect power lines promptly and precisely in order to ensure the safe and secure delivery of electricity. In steep and mountainous terrain, traditional surveying methods cannot inspect power lines precisely due to their nature. Remote sensing platforms, such as satellite and aerial images, thermal images, and light detection and ranging (LiDAR) points, were utilised for the detection and inspection of power lines. Nevertheless, with the advancements of remote sensing technologies, in recent years, LiDAR surveying has been favoured for power line corridor (PLC) inspection due to active and weather-independent nature of laser scanning. Laser ranging data and the precise location of the LiDAR can be used to generate a three-dimensional (3D) image of the PLC. The resulting 3D point cloud enables accurate extraction of power lines and measurement of their distances from the forest below. In the literature, there have been many proposals for power line extraction and reconstruction for PLC modelling. This article examines the pros and cons of each domain method, providing researchers involved in three-dimensional modelling of power lines using innovative LiDAR scanning systems with useful guidelines. To achieve these objectives, research papers were analysed, focusing primarily on geoscience-related journals and conferences for the extraction and reconstruction of power lines. There has been a growing interest in examining the extraction and reconstruction of power line spans with single and multi-conductor configurations using different image and point-based techniques. Our study provides a comprehensive overview of the methodologies offered by various approaches using laser scanning data from the perspective of power line extraction applications, as well as to discuss the benefits and drawbacks of each approach. The comparison revealed that, despite the tremendous potential of aerial and mobile laser scanning systems, human intervention and post-processing actions are still required to achieve the desired results. In addition, the majority of the methods have been evaluated on the small datasets, and very few methods have been focused on multi-conductor extraction and reconstruction for power lines modelling. These barriers hinder the automated extraction and reconstruction of power line using LiDAR data and point to unexplored areas for further research and serve as useful guidelines for future research directions. Several promising directions for future LiDAR experiments using deep learning methods are outlined in the hope that they will pave the way for applications of PLC modelling and assessment at a finer scale and on a larger scale. Full article

(This article belongs to the Special Issue Remote Sensing for Power Line Corridor Surveys)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sensing for Power Line Corridor Surveys

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI