Fusion of Interferometric SAR and Photogrammetric Elevation Data

Neurology & clinical neurophysiolog

Automated 3D model generation for urban environments [online]

Abstract In this thesis, we present a fast approach to automated generation of textured 3D city models with both high details at ground level and complete coverage for birds-eye view. A ground-based facade model is acquired by driving a vehicle equipped with two 2D laser scanners and a digital camera under normal traffic conditions on public roads. One scanner is mounted horizontally and is used to determine the approximate component of relative motion along the movement of the acquisition vehicle via scan matching; the obtained relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques using an aerial photograph or a Digital Surface Model as a global map. The second scanner is mounted vertically and is used to capture the 3D shape of the building facades. Applying a series of automated processing steps, a texture-mapped 3D facade model is reconstructed from the vertical laser scans and the camera images. In order to obtain an airborne model containing the roof and terrain shape complementary to the facade model, a Digital Surface Model is created from airborne laser scans, then triangulated, and finally texturemapped with aerial imagery. Finally, the facade model and the airborne model are fused to one single model usable for both walk- and fly-thrus. The developed algorithms are evaluated on a large data set acquired in downtown Berkeley, and the results are shown and discussed

Vegetation Detection and Classification for Power Line Monitoring

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

Deep learning methods applied to digital elevation models: state of the art

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

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Generation of Digital Surface Models (DSMs) from stereo satellite (spaceborne) images is classically performed by Ground Control Points (GCPs) which require site visits and precise measurement equipment. However, collection of GCPs is not always possible and such requirement limits the usage of spaceborne imagery. This study aims at developing a fast, fully automatic, GCP-free workflow for DSM generation. The problems caused by GCP-free workflow are overcome using freely-available, low resolution static DSMs (LR-DSM). LR-DSM is registered to the reference satellite image and the registered LR-DSM is used for i) correspondence generation and ii) initial estimate generation for 3-D reconstruction. Novel methods are developed for bias removal for LR-DSM registration and bias equalization for projection functions of satellite imaging. The LR-DSM registration is also shown to be useful for computing the parameters of simple, piecewise empirical projective models. Recent computer vision approaches on stereo correspondence generation and dense depth estimation are tested and adopted for spaceborne DSM generation. The study also presents a complete, fully automatic scheme for GCPfree DSM generation and demonstrates that GCP-free DSM generation is possible and can be performed in much faster time on computers. The resulting DSM can be used in various remote sensing applications including building extraction, disaster monitoring and change detection.Ph.D. - Doctoral Progra