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GNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes / Omar Garcia Crespillo (2022)  

Public Titre : GNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes Type de document : Thèse/HDR Auteurs : Omar Garcia Crespillo, Auteur ; Jan Skaloud, Directeur de thèse ; Michael Meurer, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2022 Importance : 180 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée pour l'obtention du grade de Docteur ès Sciences Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] corrélation temporelle [Termes IGN] couplage GNSS-INS [Termes IGN] filtre de Kalman [Termes IGN] fréquence multiple [Termes IGN] modèle d'erreur [Termes IGN] modèle de Gauss-Markov [Termes IGN] navigation inertielle [Termes IGN] norme [Termes IGN] positionnement par GNSS [Termes IGN] Receiver Autonomous Integrity Monitoring [Termes IGN] système d'extension Résumé : (auteur) Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. Over the last decade, significant effort has been put to guarantee a bounded position estimation by using Global Navigation Satellite Systems (GNSS) by means of satellite-based or ground-based augmentation systems (SBAS, GBAS) and Advanced Receiver Autonomous Integrity Monitoring (ARAIM) for aviation. This has been achieved by carefully designing models that overbound the different residual error components in range measurements (e.g., satellite clock and orbit, tropospheric and multipath among others). On the other hand, and as part of Aircraft based Augmentation Systems (ABAS), the use of Inertial Reference Systems (IRS) has been traditionally included as additional source of redundant navigation information. More recently, the use of Inertial Navigation Systems (INS) with a wider spectrum of possible inertial sensor qualities in tighter integration with single-frequency GNSS has seen its way in a new Minimum Operational Performance Standard (MOPS). New GNSS/INS systems and standards could still benefit from the methodologies and aspects developed for future dual-frequency/multiconstellation GNSS standards. However, safety-related GNSS systems like ARAIM are snapshot-based, that is, the position estimation is performed independently at every epoch, whereas GNSS/INS systems are typically based on Kalman filtering (KF). Therefore, the existing error overbounding models and methodologies are not enough to produce a robust KF position estimation since the impact of time-correlation in measurements must also be accounted for. Moreover, it has been observed that the time-correlation of different GNSS errors presents also some level of uncertain behavior, which makes very challenging for linear dynamic systems to produce a guaranteed solution. As proposed by GNSS Minimum Operational Performance Standards (MOPS), there are sources of time-correlated errors that can be well modelled using a first order Gauss-Markov process (GMP). Using this GMP parametric model, it is possible to capture the uncertain timecorrelated nature of error processes by allowing the variance and time correlation constant of the GMP model to be in a bounded range. Under this situation, the first part of this thesis studies the propagation of the uncertain models through the Kalman filter estimation and provides new theoretical tools in time and frequency domain to bound the KF error estimation covariance. As a result, tight stationary bounding models on the GMP uncertain processes are derived in both continuous and discrete time domain. This is extended to non-stationary models that provide tighter error bounding during an initial transient phase when measurements are first introduced (which will be relevant in scenarios with changing number of visible satellites). The new models can very easily be used during the KF implementation which might be very attractive by regulators and designers. In the second part of the thesis, the new overbounding GMP models are applied for a dual-frequency GPS-Galileo tightly-coupled GNSS/INS integration. The design of the filter and of error models is performed following compatibility with current aviation standards and ARAIM Working Group C results. The impact of the use of the new models is analysed in terms of conservativeness, integrity and continuity based on realistic operational simulations linked to airport runways. The benefit of an overbounded GNSS/INS solution is also compared with the current baseline ARAIM algorithm solution. This thesis supports the evolution of safe GNSS-based positioning systems from only snapshot based to filtered solutions. Ensuring integrity for Kalman filter in general and for GNSS/INS systems in particular is a game changer to achieve higher performance levels for future dualfrequency multi-constellation aviation services and is of vital importance for new ground and air applications like autonomous vehicles or urban air mobility. Note de contenu : Introduction 1- Preliminaries 2- Bounding Kalman Filter with uncertain error processes 3- Application to GNSS/INS integraty monitoring 4- Closing 5- Appendix Numéro de notice : 28688 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : Lausanne : 2022 DOI : sans En ligne : https://infoscience.epfl.ch/record/292087?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100103

Context-aware image super-resolution using deep neural networks / Mohammad Saeed Rad (2021)  

Public Titre : Context-aware image super-resolution using deep neural networks Type de document : Thèse/HDR Auteurs : Mohammad Saeed Rad, Auteur ; Jean-Philippe Thiran, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2021 Importance : 148 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée pour l'obtention du grade de Docteur ès Sciences Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image à basse résolution [Termes IGN] image à haute résolution [Termes IGN] pouvoir de résolution spectrale [Termes IGN] reconstruction d'image [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation sémantique [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (auteur) Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the performance of the single image super-resolution task significantly by benefiting from machine learning and AI-powered algorithms, and more specifically, with the advent of Deep Learning-based approaches. Although these advances allow a machine to learn and have better exploitation of an image and its content, recent methods are still unable to constrain the plausible solution space based on the available contextual information within an image. This limitation mostly results in poor reconstructions, even for well-known types of objects and textures easily recognizable for humans. In this thesis, we aim at proving that the categorical prior, which characterizes the semantic class of a region in an image (e.g., sky, building, plant), is crucial in super-resolution task for reaching a higher reconstruction quality. In particular, we propose several approaches to improve the perceived image quality and generalization capability of deep learning-based methods by exploiting the context and semantic meaning of images. To prove the effectiveness of this categorical information, we first propose a convolutional neural network-based framework that is able to extract and use semantic information to super-resolve a given image by using multitask learning, simultaneously for learning image super-resolution and semantic segmentation. The proposed decoder is forced to explore categorical information during training, as this setting employs only one shared deep network for both semantic segmentation and super-resolution tasks. We further investigate the possibility of using semantic information by a novel objective function to introduce additional spatial control over the training process. We propose penalizing images at different semantic levels using appropriate loss terms by benefiting from our new OBB (Object, Background, and Boundary) labels generated from segmentation labels. Then, we introduce a new test time adaptation-based technique to leverage high-resolution images with perceptually similar context to a given test image to improve the reconstruction quality. We further validate this approach's effectiveness by using a novel numerical experiment analyzing the correlation between filters learned by our network and what we define as `ideal' filters. Finally, we present a generic solution to enable adapting all our previous contributions in this thesis, as well as other recent super-resolution works trained on synthetic datasets, to real-world super-resolution problem. Real-world super-resolution refers to super-resolving images with real degradations caused by physical imaging systems, instead of low-resolution images from simulated datasets assuming a simple and uniform degradation model (i.e., bicubic downsampling). We study and develop an image-to-image translator to map the distribution of real low-resolution images to the well-understood distribution of bicubically downsampled images. This translator is used as a plug-in to integrate real inputs into any super-resolution framework trained on simulated datasets. We carry out extensive qualitative and quantitative experiments for each mentioned contribution, including user studies, to compare our proposed approaches to state-of-the-art method. Note de contenu : 1- Introduction 2- Brief image super-resolution review 3- Extracting image context by multi-task learning 4- Spatial control over image genertion process 5- Test-time adaptation based on perceptual similarity 6- Integrating into real-world SR 7- Conclusion Numéro de notice : 28652 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : EPFL, Lausanne : 2021 DOI : sans En ligne : https://infoscience.epfl.ch/record/286804?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99790

Spatiotemporal patterns of urbanization during the last four decades in Switzerland and their impacts on urban heat islands / Marti Bosch Padros (2021)  

Public Titre : Spatiotemporal patterns of urbanization during the last four decades in Switzerland and their impacts on urban heat islands Type de document : Thèse/HDR Auteurs : Marti Bosch Padros, Auteur ; Jérôme Chenal, Directeur de thèse ; Stéphane Joost, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2021 Importance : 145 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée pour l'obtention du grade de Docteur ès Sciences Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aménagement du territoire [Termes IGN] analyse diachronique [Termes IGN] analyse spatio-temporelle [Termes IGN] changement climatique [Termes IGN] croissance urbaine [Termes IGN] densité de population [Termes IGN] espace vert [Termes IGN] étalement urbain [Termes IGN] ilot thermique urbain [Termes IGN] modèle de simulation [Termes IGN] modèle dynamique [Termes IGN] occupation du sol [Termes IGN] paysage urbain [Termes IGN] service écosystémique [Termes IGN] Suisse [Termes IGN] urbanisation Index. décimale : THESE Thèses et HDR Résumé : (auteur) Urbanization is nowadays a global phenomenon which is increasingly concentrating the world’s population in cities. In Switzerland, recent decades have seen an unprecedented loss of arable land due to urbanization, which has triggered amendments in the spatial planning laws with the aim of promoting urban densification. Nevertheless, despite remarkable efforts, the environmental impacts of distinctive urban patterns such as compact cities and urban sprawl remain poorly understood. One of the most remarkable environmental impacts of urbanization is the urban heat island effect, a phenomenon by which urban temperatures are warmer than in its rural surroundings. Central Europe, and therefore Switzerland, is among the regions in the world where temperatures are rising faster and the urban heat island effect is most prominent, which represents a central challenge for spatial planning. Most studies suggest that the urban heat island effect can be aggravated in compact cities, especially when considering the larger share of urban dwellers that are exposed to the highest temperatures. At the same time, the literature on the subject has seen a growing development of mitigation strategies, which suggest that the urban heat island effect can be significantly alleviated by an adequate planning of the building materials and urban green spaces. This doctoral dissertation intends to address the issues expressed above by performing a quantitative evaluation of the spatiotemporal patterns of urbanization in Switzerland and their impact on the urban heat island effect. To that end, the thesis adopts a landscape ecology perspective to quantify urban patterns and to spatially simulate the biophysical processes that underpin the urban heat island effect. The first article presents PyLandStats, an opensource library to compute landscape metrics in a repeatable and reproducible manner. In the second article, such a library is used to evaluate the spatiotemporal patterns of urbanization observed in the urban agglomerations of Bern, Lausanne and Zurich from 1980 to 2016. The results reveal that the outer zones of Bern and Lausanne are still undergoing diffusive urban expansion, whereas infill development is the dominant growth mode in both the inner and outer zones of Zurich. The thesis follows with the development of a spatially-explicit method to simulate urban heat mitigation using a recent model of urban cooling based on three biophysical mechanisms, namely tree shade, evapotranspiration and albedo. The study introduces an automated procedure to calibrate the parameters of the model, and shows that the proposed approach can outperform regression models based on remote sensing features. Then, in the fourth article, such an approach is applied to Lausanne in order to evaluate heat mitigation in a variety of urban greening scenarios which modify both the abundance and spatial configuration of the tree canopy cover. The simulations suggest a potential alleviation of the maximum nighttime temperatures of 2°C, which represents a major reduction of the human exposure to the urban heat island effect. Finally, a concluding chapter summarizes the main contributions of the dissertation and reviews key implications for urban planning in Switzerland. Overall, rather than prescribing urban densification as the customary strategy for spatial development, land use regulations and local plans should incorporate spatially-explicit evaluations of the ecosystem services provided by urban green spaces. Future research should extend the proposed approach to include further ecosystem services and explore trade-offs and spatially design solutions. Note de contenu : 1Urban greening scenarios for urban heat mitigation- Introduction 2- Quantifying spatial patterns of landscapes 3- Spatiotemporal patterns of urbanization in three Swiss urban agglomerations 4- Spatially-explicit simulation of urban heat islands 6- Synthesis and outlook Numéro de notice : 28666 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : EPFL : 2021 DOI : sans En ligne : https://infoscience.epfl.ch/record/285477?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99863

Learning stereo reconstruction with deep neural networks / Stepan Tulyakov (2020)  

Public Titre : Learning stereo reconstruction with deep neural networks Type de document : Thèse/HDR Auteurs : Stepan Tulyakov, Auteur ; François Fleuret, Directeur de thèse ; Anton Ivanov, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2020 Importance : 139 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée à l'Ecole Polytechnique Fédérale de Lausanne pour l’obtention du grade de Docteur ès Sciences Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification semi-dirigée [Termes IGN] contrainte géométrique [Termes IGN] couple stéréoscopique [Termes IGN] entropie [Termes IGN] estimateur [Termes IGN] étalonnage géométrique [Termes IGN] modèle stéréoscopique [Termes IGN] profondeur [Termes IGN] réalité de terrain [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'image [Termes IGN] vision par ordinateur [Termes IGN] vision stéréoscopique Résumé : (auteur) Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these methods, is that they typically utilize a single depth cue, such as parallax, defocus blur or shading, and thus are not as robust as a human visual system that simultaneously relies on a range of monocular and binocular cues. This is mainly because it is hard to manually design a model, accounting for multiple depth cues. In this work, we address this problem by focusing on deep learning-based stereo methods that can discover a model for multiple depth cues directly from training data with ground truth depth. The complexity of deep learning-based methods, however, requires very large training sets with ground truth depth, which is often hard or costly to collect. Furthermore, even when training data is available it is often contaminated with noise, which reduces the effectiveness of supervised learning. In this work, in Chapter 3 we show that it is possible to alleviate this problem by using weakly supervised learning, that utilizes geometric constraints of the problem instead of ground truth depth. Besides the large training set requirement, deep stereo methods are not as application-friendlyas traditional methods. They have a large memory footprint and their disparity range is fixed at training time. For some applications, such as satellite stereo i magery, these are serious problems since satellite images are very large, often reaching tens of megapixels, and have a variable baseline, depending on a time difference between stereo images acquisition. In this work, in Chapter 4 we address these problems by introducing a novel network architecture with a bottleneck, capable of processing large images and utilizing more context, and an estimator that makes the network less sensitive to stereo matching ambiguities and applicable to any disparity range without re-training. Because deep learning-based methods discover depth cues directly from training data, they can be adapted to new data modalities without large modifications. In this work, in Chapter 5 we show that our method, developed for a conventional frame-based camera, can be used with a novel event-based camera, that has a higher dynamic range, smaller latency, and low power consumption. Instead of sampling intensity of all pixels with a fixed frequency, this camera asynchronously reports events of significant pixel intensity changes. To adopt our method to this new data modality, we propose a novel event sequence embedding module, that firstly aggregates information locally, across time, using a novel fully-connected layer for an irregularly sampled continuous domain, and then across discrete spatial domain. One interesting application of stereo is a reconstruction of a planet’s surface topography from satellite stereo images. In this work, in Chapter 6 we describe a geometric calibration method, as well as mosaicing and stereo reconstruction tools that we developed in the framework of the doctoral project for Color and Stereo Surface Imaging System onboard of ESA’s Trace Gas Orbiter, orbiting Mars. For the calibration, we propose a novel method, relying on starfield images because large focal lengths and complex optical distortion of the instrument forbid using standard methods. Scientific and practical results of this work are widely used by a scientific community. Note de contenu : 1- Introduction 2- Background 3- Weakly supervised learning of deep patch-matching cost 4- Applications-friendly deep stereo 5- Dense deep event-based stereo 6- Calibration of a satellite stereo system 7- Conclusions Numéro de notice : 25795 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : Lausanne : 2020 En ligne : https://infoscience.epfl.ch/record/275342?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95025

Machine learning and geographic information systems for large-scale mapping of renewable energy potential / Dan Assouline (2019)  

Public Titre : Machine learning and geographic information systems for large-scale mapping of renewable energy potential Type de document : Thèse/HDR Auteurs : Dan Assouline, Auteur ; Jean-Louis Scartezzini, Directeur de thèse ; Nahid Mohajeri Pour Rayeni, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2019 Importance : 294 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée pour l'obtention du grade de Docteur ès Sciences à l'Ecole Polytechnique Fédérale de Lausanne Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données météorologiques [Termes IGN] données topographiques [Termes IGN] énergie éolienne [Termes IGN] énergie géothermique [Termes IGN] énergie renouvelable [Termes IGN] énergie solaire [Termes IGN] méthode fondée sur le noyau [Termes IGN] modélisation spatio-temporelle [Termes IGN] occupation du sol [Termes IGN] prédiction [Termes IGN] Suisse Résumé : (auteur) A promising pathway to follow in order to reach sustainable development goals is an increased reliance on renewable sources of energy. The optimized use of these energy sources, however, requires the assessment of their potential supply, along with the demand loads in locations of interest. In particular, large-scale supply estimation studies are needed in order to evaluate areas of high potential for each type of energy source for a particular region, and allow for the elaboration of efficient global energy strategies. In Switzerland, the “Energy Strategy 2050”, initiated in 2011 by the Swiss Federal Council, sets an example with the ambitious goal of reaching a 50-80% reduction of CO2 emissions by the year 2050, with a clear course of action: phasing-out nuclear power, improving energy efficiency, and greatly increasing the use of renewables. This thesis develops a general data-driven strategy combining Geographic Information Systems and Machine Learning methods to map the large-scale energy potential for three very popular sources of decentralized energy systems: wind energy (using horizontal axis wind turbines), geothermal energy (using very shallow ground source heat pumps) and solar energy (using photovoltaic solar panels over rooftops). For each of the three considered energy sources, an adapted methodology is suggested to assess its large-scale potential, by estimating multiple variables of interest (with a suitable time resolution, e.g. monthly or yearly), using widely available data, and combining these variables into potential values. These latter estimated variables, dictating the potential, include: (i) the monthly wind speed, and rural and urban topographic/obstacle configuration for wind energy, (ii) the ground thermal conductivity, volumetric heat capacity and monthly temperature gradient for geothermal energy, (iii) the monthly solar radiation, available area for PV panels over rooftops, geometrical characteristics of rooftops and monthly shading factors over rooftops for solar energy. The use of Machine Learning algorithms (notably Support Vector Machines and Random Forests) allows, given adequate features and training data (examples for some locations), for the prediction of the latter variables at unknown locations, along with the uncertainty attached to the predictions. In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies. Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for RooftopMounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017.In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies. Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for RooftopMounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017. Note de contenu : 1- Introduction 2- Machine Learning 3- Theory and modeling of renewable energy systems 4- Wind energy: a theoretical potential estimation 5- Very shallow geothermal energy: a theoretical potential estimation 6- Solar energy: a technical potential estimation at commune scale 7- Solar energy: an improved potential estimation at pixel scale 8- Conclusion Numéro de notice : 25797 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences : EPFLausanne : 2019 nature-HAL : Thèse DOI : sans En ligne : https://infoscience.epfl.ch/record/264971?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95038

Parameter estimation with GNSS-reflectometry and GNSS synthetic aperture techniques / Miguel Angel Ribot Sanfelix (2018)  

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Toward a systematic integration of optical remote sensing for inland waters studies / Vincent Maurice Nouchi (2018)  

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Vehicle dynamic model based navigation for small UAVs / Mehran Khaghani (2018)  

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Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)

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Vision-based detection of aircrafts and UAVs / Artem Rozantsev (2017)  

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Saisie de la couverture neigeuse de sites avalancheux par des systèmes aéroportés / Julien Vallet (2002)    

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Nouveaux cadres de référence et transformations de coordonnées en géomatique / François Golay (2001)

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Proceedings, International workshop on emerging technologies for geo-based applications, Ascona, Switzerland, May 22-25, 2000 / Ecole polytechnique fédérale de Lausanne (2000)

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Approche de la concertation territoriale par l'analyse lexicale systémique et l'analyse lexicale du discours des acteurs / Wladimir Major (1999)

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Concept et méthode de gestion des eaux en milieu semi-aride à l'aide d'un système d'information géographique / M. Nguetora (1999)

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