3D Reconstruction of 138 KV Power-lines from Airborne LiDAR Data

Due to infrequent and imprecise maintenance inspection in power-line corridors, accidents can be caused by interferences, for instance, surrounding trees. Transmission power-line inspection conventionally relies on the participation of ground personnel and airborne camera to patrol power-lines, and is limited by intensive labour, and difficult working conditions and management. Airborne light detection and ranging (LiDAR) has proven a powerful tool to overcome these limitations to enable more efficient inspection. Active airborne LiDAR systems directly capture the 3D information of power infrastructure and surrounding objects. This study aims at building a semi-automatic 3D reconstruction workflow for power-lines extracted from airborne LiDAR data of 138 kV transmission line corridors (500 m by 340 m) in Nanaimo, BC, Canada. The proposed workflow consists of three components: detection, extraction, and fitting. The power-lines are automatically detected with regular geometric shape using a set of algorithms, including density-based filtering, Hough transform and concatenating algorithm. The complete power-lines are then extracted using a rectangular searching technique. Finally, the 3D power-lines are reconstructed through fitting by a hyperbolic cosine function and least-squares fitting. A case study is carried out to evaluate the proposed workflow for hazard tree detection in the corridor. The results obtained demonstrate that power-lines can be reconstructed in 3D, which are useful in detection of hazard trees to support power-line corridor management

Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data

Paper describes the development and testing of a three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud

Detection of Power Line Supporting Towers via Interpretable Semantic Segmentation of 3D Point Clouds

The inspection and maintenance of energy transmission networks are demanding and crucial tasks for any transmission system operator. They rely on a combination of on-theground staff and costly low-flying helicopters to visually inspect the power grid structure. Recently, LiDAR-based inspections have shown the potential to accelerate and increase inspection precision. These high-resolution sensors allow one to scan an environment and store it in a 3D point cloud format for further processing and analysis by maintenance specialists to prevent fires and damage to the electrical system. However, this task is especially demanding to handle on time when we consider the extensive area that the transmission network covers. Nonetheless, the transition to point cloud data allows us to take advantage of Deep Learning to automate these inspections, by detecting collisions between the grid and the revolving scene. Deep Learning is a recent and powerful tool that has been successfully applied to a myriad of real-life problems, such as image recognition and speech generation. With the introduction of affordable LiDAR sensors, the application of Deep Learning on 3D data emerged, with numerous methods being proposed every day to address difficult problems, from 3D object detection to 3D point cloud segmentation. Alas, state-of-the-art methods are remarkably complex, composed of millions of trainable parameters, and take several weeks, if not months, to train on specific hardware, which makes it difficult for traditional companies, like utilities, to employ them. Therefore, we explore a novel mathematical framework that allows us to define tailored operators that incorporate prior knowledge regarding our problem. These operators are then integrated into a learning agent, called SCENE-Net, that detects power line supporting towers in 3D point clouds. SCENE-Net allows for the interpretability of its results, which is not possible in conventional models, it shows an efficient training and inference time of 85 mn and 20 ms on a regular laptop. Our model is composed of 11 trainable geometrical parameters, like the height of a cylinder, and has a Precision gain of 24% against a comparable CNN with 2190 parameters.A inspeção e manutenção de redes de transmissão de energia são tarefas cruciais para operadores de rede. Recentemente, foram adotadas inspeções utilizando sensores LiDAR de forma a acelerar este processo e aumentar a sua precisão. Estes sensores são objetos de alta precisão que conseguem inspecionar ambientes e guarda-los no formato de nuvens de pontos 3D, para serem posteriormente analisadas por specialistas que procuram prevenir fogos florestais e danos à estruta eléctrica. No entanto, esta tarefa torna-se bastante difícil de concluir em tempo útil pois a rede de transmissão é bastasnte vasta. Por isso, podemos tirar partido da transição para dados LiDAR e utilizar aprendizagem profunda para automatizar as inspeções à rede. Aprendizagem profunda é um campo recente e em grande desenvolvimento, sendo aplicado a vários problemas do nosso quotidiano e facilmente atinge um desempenho superior ao do ser humano, como em reconhecimento de imagens, geração de voz, entre outros. Com o desenvolvimento de sensores LiDAR acessíveis, o uso de aprendizagem profunda em dados 3D rapidamente se desenvolveu, apresentando várias metodologias novas todos os dias que respondem a problemas complexos, como deteção de objetos 3D. No entanto, modelos do estado da arte são incrivelmente complexos e compostos por milhões de parâmetros e demoram várias semanas, senão meses, a treinar em GPU potentes, o que dificulta a sua utilização em empresas tradicionais, como a EDP. Portanto, nós exploramos uma nova teoria matemática que nos permite definir operadores específicos que incorporaram conhecimento sobre o nosso problema. Estes operadores são integrados num modelo de aprendizagem prounda, designado SCENE-Net, que deteta torres de suporte de linhas de transmissão em nuvens de pontos. SCENE-Net permite a interpretação dos seus resultados, aspeto que não é possível com modelos convencionais, demonstra um treino eficiente de 85 minutos e tempo de inferência de 20 milissegundos num computador tradicional. O nosso modelo contém apenas 11 parâmetros geométricos, como a altura de um cilindro, e demonstra um ganho de Precisão de 24% quando comparado com uma CNN com 2190 parâmetros

Airborne laser sensors and integrated systems

The underlying principles and technologies enabling the design and operation of airborne laser sensors are introduced and a detailed review of state-of-the-art avionic systems for civil and military applications is presented. Airborne lasers including Light Detection and Ranging (LIDAR), Laser Range Finders (LRF), and Laser Weapon Systems (LWS) are extensively used today and new promising technologies are being explored. Most laser systems are active devices that operate in a manner very similar to microwave radars but at much higher frequencies (e.g., LIDAR and LRF). Other devices (e.g., laser target designators and beam-riders) are used to precisely direct Laser Guided Weapons (LGW) against ground targets. The integration of both functions is often encountered in modern military avionics navigation-attack systems. The beneficial effects of airborne lasers including the use of smaller components and remarkable angular resolution have resulted in a host of manned and unmanned aircraft applications. On the other hand, laser sensors performance are much more sensitive to the vagaries of the atmosphere and are thus generally restricted to shorter ranges than microwave systems. Hence it is of paramount importance to analyse the performance of laser sensors and systems in various weather and environmental conditions. Additionally, it is important to define airborne laser safety criteria, since several systems currently in service operate in the near infrared with considerable risk for the naked human eye. Therefore, appropriate methods for predicting and evaluating the performance of infrared laser sensors/systems are presented, taking into account laser safety issues. For aircraft experimental activities with laser systems, it is essential to define test requirements taking into account the specific conditions for operational employment of the systems in the intended scenarios and to verify the performance in realistic environments at the test ranges. To support the development of such requirements, useful guidelines are provided for test and evaluation of airborne laser systems including laboratory, ground and flight test activities

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

With renewables for energy security

Taking into account the possible future exhaustion of fossil energy sources, the actual and near danger of climate change, the drastic increase of the greenhouse gases in the last 200 years, as well as the growing need for sustainable development, consumption and liveable environment, the increasing necessity of renewable energy sources becomes clear. Utilization of these energy sources have to acquire a bigger role in the field of energy supply, in order to enhance the energy security of Hungary, to decline the energy import dependence, to reduce the negative environmental impacts, and to recover the economy. The world’s hunger for energy is growing exponentially; this is why it is crucial to establish feasibility scenarios in the next decades, which are able to meet these expectations, and to increase the safety of the energy supply

With renewables for energy security

Taking into account the possible future exhaustion of fossil energy sources, the actual and near danger of climate change, the drastic increase of the greenhouse gases in the last 200 years, as well as the growing need for sustainable development, consumption and liveable environment, the increasing necessity of renewable energy sources becomes clear. Utilization of these energy sources have to acquire a bigger role in the field of energy supply, in order to enhance the energy security of Hungary, to decline the energy import dependence, to reduce the negative environmental impacts, and to recover the economy. The world’s hunger for energy is growing exponentially; this is why it is crucial to establish feasibility scenarios in the next decades, which are able to meet these expectations, and to increase the safety of the energy supply