Urban energy exchanges monitoring from space

One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. However, satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (-64.1, +69.3 W m-2 for ±2 K perturbation); and also underestimate anthropogenic heat flux. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling

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