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Are there detectable common aperiodic displacements at ITRF co-location sites? / Maylis Teyssendier de la Serve (2021)
Titre : Are there detectable common aperiodic displacements at ITRF co-location sites? Type de document : Article/Communication Auteurs : Maylis Teyssendier de la Serve , Auteur ; Paul Rebischung , Auteur ; Xavier Collilieux , Auteur ; Zuheir Altamimi , Auteur ; Laurent Métivier , Auteur Editeur : Washington DC [Etats-Unis] : Earth and Space Science Open Archive ESSOAr Année de publication : 2021 Projets : 1-Pas de projet / Conférence : AGU 2021 Fall Meeting 13/12/2021 17/12/2021 New Orleans and virtual Louisiane - Etats-Unis Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] co-positionnement
[Termes IGN] déformation de la croute terrestre
[Termes IGN] International Terrestrial Reference Frame
[Termes IGN] résidu
[Termes IGN] série temporelleRésumé : (auteur) Nowadays, the time evolution of ITRF station positions is described by piece-wise linear models extended with exponential and logarithmic functions to account for post-seismic displacements. The ITRF2020 will also account for seasonal deformation by means of annual and semi-annual sine waves. However, part of the Earth’s surface deformation is not captured by those deterministic functions, such as inter-annual hydrological loading deformation, or high-frequency atmospheric loading deformation. To account for such aperiodic displacements, a reference frame in the form of a time series could be considered. This would require aperiodic motions of the different space geodetic stations to be tied in a common frame by means of co-motion constraints. The relevance of such constraints is however debatable. Indeed, common aperiodic movements between co-located space geodetic stations have thus far not been evidenced. This presentation describes the comparison of station position time series from the different space geodetic techniques in order to highlight whether or not common aperiodic movements can be detected at co-location sites. Those time series are extracted from the solutions provided by the techniques international services for the ITRF2014. They are first carefully aligned to a common reference frame in order to minimize differential network effect. Then, they are cleaned from linear, post-seismic and periodic signals (including seasonal deformation and technique systematic errors). Residual time series from co-located stations are finally confronted with each other. Numéro de notice : C2021-069 Affiliation des auteurs : UMR IPGP-Géod (2020- ) Thématique : POSITIONNEMENT Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.1002/essoar.10509118.1 Date de publication en ligne : 06/12/2021 En ligne : https://doi.org/10.1002/essoar.10509118.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99604
Titre : Can graph convolution networks learn spatial relations? Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Abstracts of the ICA num. 3 Projets : 1-Pas de projet / Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] alignement
[Termes IGN] bati
[Termes IGN] objet géographique
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Vedettes matières IGN] GénéralisationRésumé : (auteur) [introduction] Maps are composed of spatially related geographic objects. Spatial relations are key information for human as they support the description of relative locations: the house is to the east of the city centre, near the interchange, or at the end of the path. Consequently, preserving these spatial relations is important during map generalisation. For example, building typification is a generalisation operation that seeks to reduce the quantity of building while preserving relation between and within homogeneous buildings groups (Regnauld, 2001). Building or road patterns are remarkable distributions of elements in the map from which high-level concepts and semantics (e.g. landuse types and urban morphology) can be inferred. Such patterns can be characterized by spatial relations (e.g. proximity, similarity and continuity of these elements) and hence are visually easy to identify by a human. To identify these patterns automatically is important for automated map generalisation (Christophe and Ruas, 2002). However, it remains challenging to devise algorithms that can resemble the human level performance. The goal of this paper is to illustrate the potential of graph convolutional networks (GCN) for the identification of patterns and relations important for map generalisation with two use cases: building patterns detection, and road segment selection. Both tasks require some degree of understanding of the spatial relations between map objects. Hence, our experiments constitute a first step in exploring the capability of deep neural network for learning representations of spatial relations. Numéro de notice : C2021-045 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-3-60-2021 Date de publication en ligne : 13/12/2021 En ligne : https://doi.org/10.5194/ica-abs-3-60-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99420
Titre : Copernicus Sentinel-2 geometric calibration status Type de document : Article/Communication Auteurs : Sébastien Clerc, Auteur ; Marion Neveu Van Malle, Auteur ; Stéphane Massera , Auteur ; Carine Quang, Auteur ; Alice Chambrelan, Auteur ; François Guyot, Auteur ; Laetitia Pessiot, Auteur ; Rosario Iannone, Auteur ; Valentina Boccia, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 1-Pas de projet / Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 8170 - 8172 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] étalonnage géométrique
[Termes IGN] image Sentinel-MSIRésumé : (auteur) The Sentinel-2 mission is a key element of the Copernicus Earth monitoring program of the European Union. The mission is currently composed of two satellites and provides a continuous observation of land and coastal areas at high spatial resolution and with a revisit time of 5 days at the equator. The geometric uncertainty of the Sentinel-2 product is a critical contributor to the performance of the mission. We present the approach used to calibrate the geometric performance of Sentinel-2 data and latest activities, especially related to the co-registration with the Global Reference Image (GRI). Generation of the GRI, coregistration algorithm, called geometric refinement, and preliminary results are presented. Numéro de notice : C2021-085 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9555090 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9555090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101284
Titre : COVID-19 geoviz for spatio-temporal structures detection Type de document : Article/Communication Auteurs : Jacques Gautier , Auteur ; María-Jesús Lobo , Auteur ; Benjamin Fau, Auteur ; Armand Drugeon, Auteur ; Sidonie Christophe , Auteur ; Guillaume Touya , Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Proceedings of the ICA num. 4 Projets : 1-Pas de projet / Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Proceedings IEEE Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse spatio-temporelle
[Termes IGN] cube espace-temps
[Termes IGN] données spatiotemporelles
[Termes IGN] exploration de données géographiques
[Termes IGN] maladie virale
[Vedettes matières IGN] GéovisualisationMots-clés libres : Grow Ring Map visualization Résumé : (auteur) The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic’s spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends. Numéro de notice : C2021-046 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-4-37-2021 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.5194/ica-proc-4-37-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99398
[article]
Titre : Editorial Type de document : Article/Communication Auteurs : Pascal Willis , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 1 Langues : Anglais (eng) Descripteur : [Termes IGN] évaluation par les pairs Numéro de notice : A2021-068 Affiliation des auteurs : UMR IPGP-Géod (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : 10.1016/j.asr.2020.12.009 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.asr.2020.12.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97004
in Advances in space research > vol 67 n° 1 (January 2021) . - pp 1 - 1[article]PermalinkPermalinkExploiting multi-camera constraints within bundle block adjustment: an experimental comparison / Eleonora Maset (2021)PermalinkExtracting event-related information from a corpus regarding soil industrial pollution / Chuanming Dong (2021)PermalinkUne généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue / Philippe Brion (2021)PermalinkGenerative adversarial networks to generalise urban areas in topographic maps / Azelle Courtial (2021)PermalinkPermalinkLes inventaires forestiers nationaux : des méthodes dynamiques pour un sujet dynamique / Olivier Bouriaud (2021)PermalinkPermalinkLeveraging class hierarchies with metric-guided prototype learning / Vivien Sainte Fare Garnot (2021)Permalink