Avaliação de Técnicas de Processamento de Imagem na Identificação de Impactos Ambientais em linha de Transmissão de Energia Elétrica

The projects in the energy sector are basically composed of cables and metal structures. While providing benefits, installation and operation of these projects have significant environmental impacts along its path. The aim of this study was to evaluate image processing techniques to identify environmental impacts in energy powerline (path: Ibicoara-Brumado in Bahia / Brazil). Tests were performed on orthophotos with MaxVer classification and segmentation process object-oriented, with experiments integrating LiDAR. The results were analyzed on the basis of commission errors (user accuracy), omission errors (producer accuracy) and kappa index. According to Kappa index calculated, all tests showed ratings classified as excellent. The use of integrated LiDAR with orthophotos demonstrated an improvement in refining the results, with higher accuracy in classification and segmentation. The main evidence of environmental impacts is areas with exposed soil related to access roads, implementation of the infrastructure and use/occupation with agriculture. The area related to exposed soil was 16% demonstrating the need for prevention and conservation actions in LT projects.Os empreendimentos do setor energético são compostos basicamente por cabos e estruturas metálicas. Embora traga benefícios, a instalação e operação desses empreendimentos apresentam impactos ambientais relevantes ao longo do seu trajeto. Este trabalho tem como objetivo avaliar técnicas de processamento de imagem para identificar impactos ambientais em Linha de Transmissão (LT) de Energia Elétrica (Trecho: Ibicoara/BA x Brumado/BA). Foram realizados testes em ortofotos com a classificação MaxVer e com processo de Segmentação orientado ao objeto, com experimentos integrando dados Laser Scanner. Os resultados foram analisados com base nos erros de comissão (acurácia do usuário), omissão (acurácia do produtor) e índice kappa. Segundo o índice Kappa calculado, todos os testes realizados apresentaram classificações enquadradas como excelentes.  A utilização de dados laser scanner integrados com ortofotos demonstrou um avanço no refinamento dos resultados, apresentando maior acurácia tanto na classificação quanto na segmentação. Os principais indícios de impactos ambiental localizados na área de estudo estão relacionados com a exposição do solo para aberturas de acessos e implantação das infraestruturas da LT e para o uso/ocupação na agropecuária. A área de solo exposto medida nos experimentos é de 16%, demonstrando a necessidade de ações de prevenção e conservação em projetos de LT

INTEGRAÇÃO DE IMAGEM AÉREA DE ALTA RESOLUÇÃO E DADOS DE VARREDURA A LASER NA CLASSIFICAÇÃO DE CENAS URBANAS PARA DETECTAR REGIÕES DE VIA

O problema de extração automática da malha viária urbana é extremamente complexo, uma vez que em cenas urbanas as vias apresentam forte interação com os outros objetos da cena (vegetação, edificações, veículos etc.). Esse problema pode ser simplificado se regiões correspondente às vias forem previamente isoladas. Na sequência, a malha viária urbana pode  ser extraída baseando-se apenas nessas regiões, reduzindo a área de busca e o esforço computacional. A classificação de imagens pode ser usada no intuito de isolar as regiões de via, mas em cenas urbanas complexas a utilização de somente dados espectrais pode não ser suficiente para separar com confiabilidade classes com comportamento espectral similar. Para contornar esse problema, é proposta a integração dos dados geométricos e radiométricos de varredura a laser com imagem aérea RGB de alta resolução numa classificação por Redes Neurais Artificiais, tendo por foco principal o isolamento de regiões de via. O benefício desta integração foi verificado  usando diferentes combinações de dados de entrada na rede. Os experimentos mostraram que a combinação que integra diferentes fontes de dados permitiu separar a classe via com melhor acurácia e que problemas relacionados com as respostas espectrais similares foram minimizados

Centralised and decentralised sensor fusion‐based emergency brake assist

Copyright: © 2021 by the authors. Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sen-sors. As mono‐sensor systems cannot provide reliable and consistent readings under all circum-stances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substan-tially improving the reliability of the multi‐sensor‐based automotive systems. This paper first high-lights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentral-ised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5‐based Ubuntu system, it was concluded through the experiments and analyt-ical comparisons that the decentralised fusion‐driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion‐driven EBA yields compara-tively less accurate results, but with the benefits of a higher frame rate and lesser computational cost

Multi-sensor data fusion for urban area classification

Nowadays many sensors for information acquisition are widely employed in remote sensing and different properties of the objects can be revealed. Unfortunately each imaging sensor has its own limits on scene recognition in the sense of thematic, temporal, and other interpretation. Integration (fusion) of different data types is expected to increase the quality of scene interpretation and decision making. In recent time integration of synthetic aperture radar (SAR), optical, topography or geographic information system data is widely performed for many tasks such as automatic classification, mapping or interpretation. In this paper we present an approach for very high resolution multi-sensor data fusion to solve several tasks such as urban area automatic classification and change detection. Datasets with different nature are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), dimensionality reduction, and supervised classification. Fusion of WorldView-2 optical data and laser Digital Surface Model (DSM) data allows for different types of urban objects to be classified into predefined classes of interest with increased accuracy. Numerical evaluation of the method comparing with other established methods illustrates advantage in the accuracy of structure classification into low-, medium-, and high-rise buildings together with other common urban classes

Earthquake damage analysis and mapping with the use of satellite remote sensing

After a seismic event a rapid and accurate evaluation of the impact of the damages is extremely important. Such evaluation may support rescue team operations and identify the actual dimensions of the event and its potential impact on the territory and on the population. The use of Earth Observation (EO) data has been significantly increasing in the last years, particularly the use of Very High Resolution (VHR) optical images, which are able to provide detailed information at single building level. However, most of the existing approaches mainly rely on the use of remote sensing data, either optical or SAR (Synthetic Aperture Radar), and perform a classification based on change detection techniques. In this work we aim at creating a flexible tool that is able to perform a damage classification taking into account, not only EO available data, but also additional information that is supposed to be available even before the occurrence of any seismic event (a-priori data). This data includes soil vulnerability, which can play a very important role on local amplification effects as well as structural information of the individual building. Such approach, pursued within the framework of the EC-FP7 funded project APhoRISM (Advanced Procedures for Volcanic and Seismic Monitoring- grant agreement n. 606738) aims at generating maps of damage caused by a seism using both satellite remote sensing data (SAR and/or optical sensors) and ground and structural data. The basic idea is to integrate both satellite remote sensing data (SAR and/or optical sensors) with structural and ground data to improve the accuracy and limit false alarms that derive by the use of EO data only. In order to do this, we first review the general approach and methods to data fusion and we identify what is the level of information that is better to merge referring to our goals. We also examine how the structural information is evaluated and we then focus on the description of Bayesian approaches and, more specifically, of Bayesian networks. Such type of graphical approach for our data fusion tool is implemented to assess post-earthquake building damage. We validate our Bayesian networks against the real test case based on L’ Aquila (Italy) earthquake which took place on April 6, 2009. In this case, we have a set of data available to build the Ground Truth validation test set. For what concerns remote sensing data, for this event, both COSMO-Skymed Radar and Quickbird VHR optical sensors were available thus allowing a complete remote sensing dataset. The in-situ information, though fragmentary, was built using data coming from different sources, mainly from INGV (Italian Geophysical and Volcano Institute) and the Italian Civil Protection Department. The promising results of different Bayesian networks are presented showing the step-by-step approach adopted, which aims at generalising the methodology in order to further implement the network in future cases