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Opportunities and challenges for augmented reality situated geographical visualization / María-Jesús Lobo in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)
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Titre : Opportunities and challenges for augmented reality situated geographical visualization Type de document : Article/Communication Auteurs : María-Jesús Lobo , Auteur ; Sidonie Christophe , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 4 Article en page(s) : pp 163 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] réalité augmentée
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Augmented reality (AR) enables to display situated geographical visualizations, i.e visualizations that use virtual elements that are displayed in a geographical location. The place where the data is displayed complements the visualization. Many applications that take advantage of AR and situated visualizations exist, but they differ in the visualizations they present, their relationship to the geographic locations and goals. To better understand why and how AR based situated geovisualization is used, we review 45 papers coming from Human Computer Interaction, Visualization and Geographical Information Science venues that present such applications. Inspired by existing classifications, we characterize these papers according to the data they visualize and the geographical distance between the visualization and the data the visualization represents. This analysis reveals existing opportunities for situated geovisualization applications using AR. Numéro de notice : A2020-462 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2020-163-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2020-163-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95529
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2020 (August 2020) . - pp 163 - 170[article]A regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)
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Titre : A regression model of spatial accuracy prediction for Openstreetmap buildings Type de document : Article/Communication Auteurs : Ibrahim Maidaneh Abdi , Auteur ; Arnaud Le Guilcher , Auteur ; Ana-Maria Olteanu-Raimond , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 4 Article en page(s) : pp 39 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] modèle de régression
[Termes IGN] OpenStreetMap
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] qualité des donnéesRésumé : (auteur) Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationship between measures of extrinsic quality of OSM (i.e. obtained by comparison with reference spatial data) and the measures of intrinsic quality of OSM (i.e. OSM features themselves) in order to estimate extrinsic quality on an unevaluated OSM dataset. The approach was applied on OSM buildings. On our dataset, the resulting regression model predicts the values on the extrinsic quality indicators with 30% less variance than an uninformed predictor. Numéro de notice : A2020-506 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2020-39-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2020-39-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95647
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2020 (August 2020) . - pp 39 - 47[article]SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)
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Titre : SemCity Toulouse: a benchmark for building instance segmentation in satellite images Type de document : Article/Communication Auteurs : Ribana Roscher, Auteur ; Michele Volpi, Auteur ; Clément Mallet , Auteur ; Lukas Drees, Auteur ; Jan Dirk Wegner, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 5, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 5 Article en page(s) : pp 109 - 116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] instance
[Termes IGN] Toulouse
[Termes IGN] zone urbaine denseRésumé : (auteur) In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning. Numéro de notice : A2020-503 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-5-2020-109-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-5-2020-109-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95639
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-5-2020 (August 2020) . - pp 109 - 116[article]Global Climate [in “State of the Climate in 2019"] / A. Ades in Bulletin of the American Meteorological Society, vol 101 n° 8 (August 2020)
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Titre : Global Climate [in “State of the Climate in 2019"] Type de document : Article/Communication Auteurs : A. Ades, Auteur ; R. Adler, Auteur ; et al., Auteur ; Olivier Bock , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp S9 - S128 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Atmosphère
[Termes IGN] changement climatique
[Termes IGN] circulation atmosphérique
[Termes IGN] climat terrestre
[Termes IGN] cryosphère
[Termes IGN] données météorologiques
[Termes IGN] gaz à effet de serre
[Termes IGN] humidité de l'air
[Termes IGN] humidité du sol
[Termes IGN] précipitation
[Termes IGN] sécheresse
[Termes IGN] température de l'airRésumé : (auteur) [introduction] The assessments and analyses presented in this chapter focus predominantly on the measured differences of climate and weather observables from previous conditions, years, and decades to place 2019 in context. Many of these differences have direct impacts on people, for example, their health and environment, as well as the wider biosphere, but are beyond the scope of these analyses. For the last few State of the Climate reports, an update on the number of warmer-than-average years has held no surprises, and this year is again no different. The year 2019 was among the three warmest years since records began in the mid-to-late 1800s. Only 2016, and for some datasets 2015, were warmer than 2019; all years after 2013 have been warmer than all others back to the mid-1800s. Each decade since 1980 has been successively warmer than the preceding decade, with the most recent (2010–19) being around 0.2°C warmer than the previous (2000–09). This warming of the land and ocean surface is reflected across the globe. For example, lake and permafrost temperatures have increased; glaciers have continued to lose mass, becoming thinner for the 32nd consecutive year, with the majority also becoming shorter during 2019. The period during which Northern Hemisphere (NH) lakes were covered in ice was seven days shorter than the 1981–2010 long-term average, based on in situ phenological records. There were fewer cool extremes and more warm extremes on land; regions including Europe, Japan, Pakistan, and India all experienced heat waves. More strong than moderate marine heat waves were recorded for the sixth consecutive year. And in Australia (discussed in more detail in section 7h4), moisture deficits and prolonged high temperatures led to severe impacts during late austral spring and summer, including devastating wildfires. Smoke from these wildfires was detected across large parts of the Southern Hemisphere (SH). [...] Numéro de notice : A2020-798 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1175/BAMS-D-20-0104.1 Date de publication en ligne : 12/08/2020 En ligne : https://doi.org/10.1175/BAMS-D-20-0104.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96582
in Bulletin of the American Meteorological Society > vol 101 n° 8 (August 2020) . - pp S9 - S128[article]Preface: the 2020 edition of the XXIVth ISPRS congress / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2020 (August 2020)
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Titre : Preface: the 2020 edition of the XXIVth ISPRS congress Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; Florent Lafarge, Auteur ; Martyna Poreba , Auteur ; Ewelina Rupnik , Auteur ; Gaétan Bahl, Auteur ; Nicolas Girard, Auteur ; Anatol Garioud , Auteur ; Ian J. Dowman, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 1, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France ISPRS OA Annals Commission 1 Article en page(s) : pp 1 - 6 Langues : Anglais (eng) Résumé : (auteur) We report key elements and figures related to the proceedings of the 2020 edition of the XXIVth ISPRS Congress. The COVID-19 pandemic causes global travel challenges and restrictions for the full year 2020. Consequently, the Congress planned in June 2020 in Nice (France) was postponed to July 2021. Papers were already submitted and the review process was almost complete. Thus, it has been decided to achieve the publication of the proceedings of these papers under the label ”2020 Edition”. The authors of published papers have the opportunity to present their work during a Virtual Event (31 August – 2 September 2020), while the papers of the 2021 edition will be presented during the (physical) Congress in July 2021. Numéro de notice : A2020-500 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-1-2020-1-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-1-2020-1-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95641
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2020 (August 2020) . - pp 1 - 6[article]Towards structureless bundle adjustment with two- and three-view structure approximation / Ewelina Rupnik in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkA worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkExtended reality in spatial sciences: A review of research challenges and future directions / Arzu Çöltekin in ISPRS International journal of geo-information, vol 9 n° 7 (July 2020)PermalinkMapping the French green infrastructure – an exercise in homogenizing heterogeneous regional data / Lucille Billon in International journal of cartography, Vol 6 n° 2 (July 2020)PermalinkA change of theme: the role of generalization in thematic mapping / Paulo Raposo in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkCoastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)PermalinkGéodésie de poche : toute la géodésie dans votre main / Gilles Canaud in XYZ, n° 163 (juin 2020)PermalinkTraffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)PermalinkDeep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, TOSAS, vol 6 n° 3 (May 2020)PermalinkExploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)Permalink