1,021 research outputs found

    Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid

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    LiDAR is currently one of the most utilized sensors to effectively monitor the status of power lines and facilitate the inspection of remote power distribution networks and related infrastructures. To ensure the safe operation of the smart grid, various remote data acquisition strategies, such as Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TSL) have been leveraged to allow continuous monitoring of regional power networks, which are typically surrounded by dense vegetation. In this article, an unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage, as well as the surrounding vegetation in a Power Line Corridor (PLC) solely from LiDAR data. Initially, the proposed approach eliminates the ground points from higher elevation points based on statistical analysis that applies density criteria and histogram thresholding. After denoising and transforming of the remaining candidate points by applying Principle Component Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a two-stage DBSCAN clustering to identify each power line individually. Finally, all high elevation points in the PLC are identified based on their distance to the newly segmented power lines. Conducted experiments illustrate that the proposed framework is an agnostic method that can efficiently detect the power lines and perform PLC-based hazard analysis.Comment: Accepted in the 22nd World Congress of the International Federation of Automatic Control [IFAC WC 2023

    Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

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    The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

    Vegetation Detection and Classification for Power Line Monitoring

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    Electrical network maintenance inspections must be regularly executed, to provide a continuous distribution of electricity. In forested countries, the electrical network is mostly located within the forest. For this reason, during these inspections, it is also necessary to assure that vegetation growing close to the power line does not potentially endanger it, provoking forest fires or power outages. Several remote sensing techniques have been studied in the last years to replace the labor-intensive and costly traditional approaches, be it field based or airborne surveillance. Besides the previously mentioned disadvantages, these approaches are also prone to error, since they are dependent of a human operator’s interpretation. In recent years, Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under debate, due to its flexibility and potential for customisation, as well as the fact it can fly close to the power lines. The present study proposes a vegetation management and power line monitoring method, using a UAV platform. This method starts with the collection of point cloud data in a forest environment composed of power line structures and vegetation growing close to it. Following this process, multiple steps are taken, including: detection of objects in the working environment; classification of said objects into their respective class labels using a feature-based classifier, either vegetation or power line structures; optimisation of the classification results using point cloud filtering or segmentation algorithms. The method is tested using both synthetic and real data of forested areas containing power line structures. The Overall Accuracy of the classification process is about 87% and 97-99% for synthetic and real data, respectively. After the optimisation process, these values were refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison and discussion of results is presented, providing the most important evaluation metrics and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso, durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede elétrica não a coloca em risco, provocando incêndios ou falhas elétricas. Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização, assim como o facto de conseguirem voar mais próximas das linhas elétricas. O presente estudo propõe um método para a gestão da vegetação e monitorização da rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo: deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas de classe através de um classificador baseado em features, vegetação ou estruturas da rede elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87% e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização, estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas de avaliação mais importantes e uma representação visual dos resultados obtidos

    Probabilistic and Deep Learning Algorithms for the Analysis of Imagery Data

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    Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework for the analysis of the NAIP dataset which includes (1) an unsupervised segmentation module based on the Statistical Region Merging algorithm, (2) a feature extraction module that extracts a set of standard hand-crafted texture features from the images, (3) a supervised classification algorithm based on Feedforward Backpropagation Neural Networks, and (4) a structured prediction framework using Conditional Random Fields that integrates the results of the segmentation and classification modules into a single composite model to generate the final class labels. Next, we introduce two new datasets SAT-4 and SAT-6 sampled from the NAIP imagery and use them to evaluate a multitude of Deep Learning algorithms including Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Autoencoders (SAE) for generating class labels. Finally, we propose a learning framework by integrating hand-crafted texture features with a DBN. A DBN uses an unsupervised pre-training phase to perform initialization of the parameters of a Feedforward Backpropagation Neural Network to a global error basin which can then be improved using a round of supervised fine-tuning using Feedforward Backpropagation Neural Networks. These networks can subsequently be used for classification. In the following discussion, we show that the integration of hand-crafted features with DBN shows significant improvement in performance as compared to traditional DBN models which take raw image pixels as input. We also investigate why this integration proves to be particularly useful for aerial datasets using a statistical analysis based on Distribution Separability Criterion. Then we introduce a new dataset called noisy-MNIST (n-MNIST) by adding (1) additive white gaussian noise (AWGN), (2) motion blur and (3) Reduced contrast and AWGN to the MNIST dataset and present a learning algorithm by combining probabilistic quadtrees and Deep Belief Networks. This dynamic integration of the Deep Belief Network with the probabilistic quadtrees provide significant improvement over traditional DBN models on both the MNIST and the n-MNIST datasets. Finally, we extend our experiments on aerial imagery to the class of general texture images and present a theoretical analysis of Deep Neural Networks applied to texture classification. We derive the size of the feature space of textural features and also derive the Vapnik-Chervonenkis dimension of certain classes of Neural Networks. We also derive some useful results on intrinsic dimension and relative contrast of texture datasets and use these to highlight the differences between texture datasets and general object recognition datasets

    A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

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    In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin
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