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Impact of temperature stabilization on the strapdown airborne gravimetry: a case study in Central Turkey / Mehmet Simav in Journal of geodesy, vol 94 n°4 (April 2020)
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[article]
Titre : Impact of temperature stabilization on the strapdown airborne gravimetry: a case study in Central Turkey Type de document : Article/Communication Auteurs : Mehmet Simav, Auteur ; David Becker, Auteur ; Hasan Yildiz, Auteur ; Matthias Hoss, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] accéléromètre
[Termes IGN] centrale inertielle à composants liés
[Termes IGN] contrôle thermique
[Termes IGN] étalonnage d'instrument
[Termes IGN] filtre de Kalman
[Termes IGN] gravimétrie aérienne
[Termes IGN] réalité de terrain
[Termes IGN] température
[Termes IGN] TurquieRésumé : (auteur) Airborne gravimetry with strapdown inertial sensors has been a valuable tool for many years to fill in the gravity data gaps on the areas not accessible by land. Accuracies of 1 mGal level with off-the-shelf navigation-grade inertial measurement units (IMU) can only be achieved provided that the accelerometer drifts mainly caused by the temperature variations inside the IMU housing are separated from the gravity signal. Although there are several strategies proposed in the literature to deal with this inseparability problem, we use a thermal stabilization system (iTempStab) added on an iNAT-RQH navigation-grade IMU and investigate its performance over a test region in central Turkey with moderate topography and highly qualified ground truth gravity data. Two test flights were performed in 2017 and 2018 with and without iTempStab add-on following almost the same flight trajectories. During the first flight in 2017 with iNAT-RQH only, which lasted almost 5.5 h, there were considerable temperature variations inside the IMU housing from 39.1 to 46.0 °C. A simple thermal correction based on a laboratory calibration done before the flight was applied to the vertical Z-accelerometer in the pre-processing stage. However, temperature changes were within 0.1 °C during the second test flight in 2018 with TempStab add-on. The temperature stabilization gained by the iTempStab add-on produced better cross-over statistics. While the RMSE of the non-adjusted cross-over residuals was about 2.6 mGal, it reduced by 50% with iTempStab add-on. The adjusted cross-over differences of the 2018 flight yielded an RMSE of about 0.5 mGal, which is a remarkable precision for the strapdown gravimetry. The comparison with upward continued ground gravity data at flight altitudes suggests that the thermal stabilization system shows also remarkable improvements in the residual statistics. The range of the residuals decreases from ± 10 to ± 5 mGal, the standard deviation decreases from 2.19 to 0.94 mGal, and the RMSE decreases from 2.24 to 1.48 mGal, respectively, with the iTempStab add-on. It can be concluded that the thermal stabilization system significantly improves the accelerometer stability and therefore the precision and accuracy of the strapdown airborne gravity estimates. Numéro de notice : A2020-158 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01369-5 Date de publication en ligne : 17/03/2020 En ligne : https://doi.org/10.1007/s00190-020-01369-5 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94812
in Journal of geodesy > vol 94 n°4 (April 2020)[article]Improved kinematic precise point positioning performance with the use of map constraints / Emerson Pereira Cavalheri in Journal of applied geodesy, vol 14 n° 2 (April 2020)
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Titre : Improved kinematic precise point positioning performance with the use of map constraints Type de document : Article/Communication Auteurs : Emerson Pereira Cavalheri, Auteur ; Marcelo Carvalho dos Santos, Auteur Année de publication : 2020 Article en page(s) : pp191 –2 04 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] convergence
[Termes IGN] phase
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement ponctuel précis
[Termes IGN] réalité de terrain
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) A positioning approach combining satellite measurements with a map representing the ground-truth trajectory is developed with the main objective of improving the availability of solutions for a mobile vehicle. For the positioning model, the Precise Point Positioning (PPP) technique is augmented with an alternative map-matching to find a probable space where the true vehicle or platform position is located. Then, by using a selection criterion based on the precise carrier phase residuals, the best candidate position within the space can be determined. This process provides an accurate initial position to the PPP filter, different from the standard PPP approach that relies on a point position using the less accurate pseudorange observables. A controlled experiment of a mobile receiver navigating over a pre-defined trajectory was conducted. The results show that the approach offers an instantaneous initial convergence, eliminating the re-convergences during two GNSS obstructions of 32 and 17 seconds, while constantly keeping the solution on the correct trajectory, even when tracking 3 to 2 satellites. This approach outperforms the standard PPP and RTK solutions in terms of convergences and re-convergences. These results are corroborated when comparing the average and standard deviation of residuals to the standard PPP model. For the pseudorange residuals, improvements of 17.5 cm and 24.3 cm in the average and standard deviation respectively were achieved. The carrier phase residuals standard deviation of the proposed approach was 3 cm better than that of the standard PPP. Numéro de notice : A2020-343 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0034 Date de publication en ligne : 14/01/2020 En ligne : https://doi.org/10.1515/jag-2019-0034 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95222
in Journal of applied geodesy > vol 14 n° 2 (April 2020) . - pp191 –2 04[article]Comparative usability of an augmented reality sandtable and 3D GIS for education / Antoni B. Moore in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)
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Titre : Comparative usability of an augmented reality sandtable and 3D GIS for education Type de document : Article/Communication Auteurs : Antoni B. Moore, Auteur ; Benjamin Daniel, Auteur ; greg Leonard, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 229 - 250 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] enseignement supérieur
[Termes IGN] hydrologie
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation 3D
[Termes IGN] Nouvelle-Zélande
[Termes IGN] réalité augmentée
[Termes IGN] réalité de terrain
[Termes IGN] réalité virtuelle
[Termes IGN] sable
[Termes IGN] test de performanceRésumé : (auteur) Augmented Reality (AR) sandtables facilitate the shaping of sand to form a surface that is transformed into a digital terrain map which is projected back onto the sand. Although a mature technology, there are still few instances of sandtables being used in surface analysis. Fundamentally there has not been any reported formal assessment of how well sandtables perform in an educational context compared to other conventional learning environments. We compared learning outcomes from using an AR sandtable versus a conventional 3D GIS to convey key concepts in terrain and hydrological analyses via usability and knowledge testing. Overall results from students at a research-intensive New Zealand university reveal a faster task performance and more learning satisfaction when using the sandtable to undertake experimental tasks. Effectiveness and knowledge quiz results revealed no significant difference between the technologies though there was a trend for more accurate answers with 3D GIS tasks. Student learning wise, the sandtable integrated core concepts (especially morphometry) more effectively though both technologies were otherwise similar. We conclude that sandtables have high potential in geospatial teaching, fostering accessible and engaging means of introducing terrain and hydrological concepts, prior to undertaking a more accurate and precise surface analysis with 3D GIS. Numéro de notice : A2020-028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1656810 Date de publication en ligne : 27/08/2019 En ligne : https://doi.org/10.1080/13658816.2019.1656810 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94481
in International journal of geographical information science IJGIS > vol 34 n° 2 (February 2020) . - pp 229 - 250[article]Learning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)
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Titre : Learning and geometric approaches for automatic extraction of objects from remote sensing images Type de document : Thèse/HDR Auteurs : Nicolas Girard, Auteur Editeur : Nice : Université Côte d'Azur Année de publication : 2020 Importance : 169 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat Présentée en vue de l’obtention du grade de docteur en Automatique, Traitement du Signal et des Images de l'Université Côte d’AzurLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] alignement
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] erreur
[Termes IGN] figure géométrique
[Termes IGN] filtrage du bruit
[Termes IGN] jeu de données
[Termes IGN] polygonation
[Termes IGN] réalité de terrain
[Termes IGN] segmentation d'image
[Termes IGN] vectorisationIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Creating a digital double of the Earth in the form of a map has many applications in e.g. autonomous driving, automated drone delivery, urban planning, telecommunications, and disaster management. Geographic Information Systems (GIS) are the frameworks used to integrate geolocalized data and represent maps. They represent shapes of objects in a vector representation so that it is as sparse as possible while representing shapes accurately, as well as making it easier to edit than raster data. With the increasing amount of satellite and aerial images being captured every day, automatic methods are being developed to transfer the information found in those remote sensing images into Geographic Information Systems. Deep learning methods for image segmentation are able to delineate the shapes of objects found in images, but they do so with a raster representation, in the form of a mask. Post-processing vectorization methods then convert that raster representation into a vector representation compatible with GIS. Another challenge in remote sensing is to deal with a certain type of noise in the data, which is the misalignment between different layers of geolocalized information (e.g. between images and building cadaster data). This type of noise is frequent due to various errors introduced during the processing of remote sensing data. This thesis develops combined learning and geometric approaches with the purpose to improve automatic GIS mapping from remote sensing images. We first propose a method for correcting misaligned maps over images, with the first motivation for them to match, but also with the motivation to create remote sensing datasets for image segmentation with alignment-corrected ground truth. Indeed training a model on misaligned ground truth would not lead to a nice segmentation, whereas aligned ground truth annotations will result in better segmentation models. During this work we also observed a denoising effect of our alignment model and use it to denoise a misaligned dataset in a self-supervised manner, meaning only the misaligned dataset was used for training.
We then propose a simple approach to use a neural network to directly output shape information in the vector representation, in order to by-pass the post-processing vectorization step. Experimental results on a dataset of solar panels show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs. Our simple method is limited to predicting polygons with a fixed number of vertices though. While more recent methods for learning directly in the vector representation are not limited to a fixed number of vertices, they still have other limitations in terms of the type of object shapes they can predict. More complex topological cases such as objects with holes or buildings touching each other (with a common wall which is very typical of European city centers) are not handled by these fully deep learning methods. We thus propose a hybrid approach alleviating those limitations by training a neural network to output a segmentation probability map as usual and also to output a frame field aligned with the contours of detected objects (buildings in our case). The frame field constitutes additional shape information learned by the network. We then propose our highly parallelizable polygonization method for leveraging that frame field information to vectorize the segmentation probability map efficiently. Because our polygonization method has access to additional information in the form of a frame field, it can be less complex than other advanced vectorization methods and is thus faster. Lastly, requiring an image segmentation network to also output a frame field only adds two convolutional layers and virtually does not increase inference time, making the use of a frame field only beneficial.Note de contenu : 1- Introduction
2- Building alignment
3- Building alignment from noisy ground truth
4- PolyCNN: learning polygons
5- Frame field learning
6- Polygonization by frame field
7- Conclusions and perspectivesNuméro de notice : 28501 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du Signal et des Images : Côte d’Azur : 2020 Organisme de stage : Inria Sophia-Antipolis nature-HAL : Thèse DOI : sans En ligne : https://hal.inria.fr/tel-03111628/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96940
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 SciencesLangues : 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 stéréoscopiqueRésumé : (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- ConclusionsNuméro 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 Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
PermalinkMapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece / Maria Kampouri in Geocarto international, vol 34 n° 12 ([15/09/2019])
PermalinkPermalinkPermalinkPredicting foreground object ambiguity and efficiently crowdsourcing the segmentation(s) / Danna Gurari in International journal of computer vision, vol 126 n° 7 (July 2018)
PermalinkGeometric quality assessment of trajectory-generated VGI road networks based on the symmetric arc similarity / Yan Lyu in Transactions in GIS, vol 21 n° 5 (October 2017)
PermalinkMotion priors based on goals hierarchies in pedestrian tracking applications / Francisco Madrigal in Machine Vision and Applications, vol 28 n° 3-4 (May 2017)
PermalinkGenerative models for road network reconstruction / Colin Kuntzsch in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)
PermalinkLearning grammars for architecture-specific facade parsing / Raghudeep Gadde in International journal of computer vision, vol 117 n° 3 (May 2016)
PermalinkSpatial accuracy of UAV- derived orthoimagery and topography: Comparing photogrammetric models processed with direct geo-referencing and ground control points / Chris H. Hugenholtz in Geomatica, vol 70 n° 1 (March 2016)
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