Centre dedocumentation
scientifique

Sensor tasking for search and catalog maintenance of geosynchronous space objects / Han Cai in Acta Astronautica, vol 175 (October 2020)  

Public [article]  inActa Astronautica > vol 175 (October 2020) . - pp 234 - 248 Titre : Sensor tasking for search and catalog maintenance of geosynchronous space objects Type de document : Article/Communication Auteurs : Han Cai, Auteur ; Yang Yang, Auteur ; Steve Gehly, Auteur ; Changyong He , Auteur ; Moriba Jah, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 234 - 248 Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) The space object catalog provides orbital state and characteristic information of space objects for critical applications in space situational awareness. Maintaining accurate states for all objects in the catalog is essential, but it leads to large loads on sensors and limits the time available to search for new objects. This study proposes a novel sensor tasking method for search and catalog maintenance of space objects in Geosynchronous Earth Orbit (GEO). This new framework formulates sensor tasking as a multi-objective optimization problem. It seeks an optimal balance between sensor resources to search for new objects and to maintain precise state estimates for all objects in the catalog. In order to maintain custody of newly detected targets, an evidence-based decision-making method is used to effectively prompt follow-on tracking. The labeled multi-Bernoulli filter is employed to track existing and new space objects and provide refined orbital state estimation. Simulation results are presented, in which 100 cataloged GEO objects and 200 new GEO objects are tracked using a space-based sensor placed on a Sun-synchronous orbit. Numéro de notice : A2020-419 Affiliation des auteurs : ENSG+Ext (2012-2019) Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.actaastro.2020.05.063 date de publication en ligne : 01/06/2020 En ligne : https://doi.org/10.1016/j.actaastro.2020.05.063 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95550 [article]

OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier / Quy Thy Truong in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - n° 504 Titre : OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier Type de document : Article/Communication Auteurs : Quy Thy Truong , Auteur ; Guillaume Touya , Auteur ; Cyril de Runz, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] qualité des données Résumé : (auteur) Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. Numéro de notice : A2020-507 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090504 date de publication en ligne : 22/08/2020 En ligne : https://doi.org/10.3390/ijgi9090504 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95682 [article]

Vegetation unit assignments: phytosociology experts and classification programs show similar performance but low convergence / Lise Maciejewski in Applied Vegetation Science, vol inconnu (2020)  

Public [article]  inApplied Vegetation Science > vol inconnu (2020) Titre : Vegetation unit assignments: phytosociology experts and classification programs show similar performance but low convergence Type de document : Article/Communication Auteurs : Lise Maciejewski, Auteur ; Paulina E. Pinto, Auteur ; Stéphanie Wurpillot , Auteur ; Jacques Drapier , Auteur ; Serge Cadet, Auteur ; Serge Muller (1953-....), Auteur ; Pierre Agou, Auteur ; Benoit Renaux, Auteur ; Jean-Claude Gégout, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] cohérence des données [Termes descripteurs IGN] convergence [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] phytosociologie [Termes descripteurs IGN] unité phytosociologique [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Aims : Assigning vegetation plots to vegetation units is a key step in biodiversity management projects. Nevertheless, the process of plot assignment to types is usually non‐standardized, and assignment consistency remains poorly explored. To date, the efficiency of automatic classification programs has been assessed by comparing them with a unique expert judgment. Therefore, we investigated the consistency of five phytosociology expert judgments, and the consistency of these judgements with those of automatic classification programs. Location : Mainland France. Methods : We used 273 vegetation plots distributed across France and covering the diversity of the temperate and mountainous forest ecosystems of Western Europe. We asked a representative panel of five French organizations with recognized expertise in phytosociology to assign each plot to vegetation units. We provided a phytosociological classification including 228 associations, 43 alliances and eight classes. The assignments were compared among experts using an agreement ratio. We then compared the assignments suggested by three automatic classification programs with the expert judgments. Results : We observed small differences among the agreement ratios of the expert organizations; a given expert organization agreed with another one on association assignment one time in four on average, and one time in two on alliance assignment. The agreement ratios of the automatic classification programs were globally lower, but close to expert judgments. Conclusions : The results support the current trend toward unifying the existing classifications and specifying the assignment rules by creating guiding tools, which will decrease inter‐observer variation. As compared to a pool of phytosociology experts, programs perform similarly to individual experts in vegetation unit assignment, especially at the alliance level. Although programs still need to be improved, these results pave the way for the creation of habitat time series crucial for the monitoring and conservation of biodiversity. Numéro de notice : A2020-461 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/avsc.12516 date de publication en ligne : 12/07/2020 En ligne : https://doi.org/10.1111/avsc.12516 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95579 [article]

Opportunities and challenges for augmented reality situated geographical visualization / María-Jesús Lobo in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-4 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-4 (August 2020) . - pp 163 - 170 Titre : Opportunities and challenges for augmented reality situated geographical visualization Type de document : Article/Communication Auteurs : María-Jesús Lobo, Auteur ; Sidonie Christophe , Auteur Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France) , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 163 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] réalité augmentée [Vedettes matières IGN] Géovisualisation Résumé : (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 : 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 [article]

Preface: the 2020 edition of the XXIVth ISPRS congress / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-1 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-1 (August 2020) . - pp 1 - 6 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 Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France) , Commanditaire Année de publication : 2020 Projets : 1-Pas de projet / 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 : 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 [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, V-4 (August 2020)  

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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, V-5 (August 2020)  

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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, V-2 (August 2020)  

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A 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, V-2 (August 2020)  

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Extended 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)  

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Mapping the French green infrastructure – an exercise in homogenizing heterogeneous regional data / Lucille Billon in International journal of cartography, Vol 6 n° 2 (July 2020)  

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A 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)  

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Géodésie de poche : toute la géodésie dans votre main / Gilles Canaud in XYZ, n° 163 (juin 2020)

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Deep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, vol 6 n° 3 (May 2020)    

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Exploring 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)  

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