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Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
[article]
Titre : Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information Type de document : Article/Communication Auteurs : Hao Fang, Auteur ; Florent Lafarge, Auteur Année de publication : 2019 Article en page(s) : pp 246 - 258 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] compréhension de l'image
[Termes IGN] données localisées 3D
[Termes IGN] prise en compte du contexte
[Termes IGN] représentation multiple
[Termes IGN] scène
[Termes IGN] scène intérieure
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene understanding. Recent works demonstrated that deep learning architectures can operate directly on raw point clouds, i.e. without the use of intermediate grid-like structures. These architectures are however not designed to encode contextual information in-between objects efficiently. Inspired by a global feature aggregation algorithm designed for images (Zhao et al., 2017), we propose a 3D pyramid module to enrich pointwise features with multi-scale contextual information. Our module can be easily coupled with 3D semantic segmentation methods operating on 3D point clouds. We evaluated our method on three large scale datasets with four baseline models. Experimental results show that the use of enriched features brings significant improvements to the semantic segmentation of indoor and outdoor scenes. Numéro de notice : A2019-271 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.010 Date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93089
in ISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 246 - 258[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)
[article]
Titre : Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining Type de document : Article/Communication Auteurs : Quy Thy Truong , Auteur ; Cyril de Runz, Auteur ; Guillaume Touya , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 1651 - 1682 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] cartographie collaborative
[Termes IGN] comportement
[Termes IGN] données localisées des bénévoles
[Termes IGN] exploration de données
[Termes IGN] graphe
[Termes IGN] OpenStreetMap
[Termes IGN] pondération
[Termes IGN] qualité des données
[Termes IGN] travail coopératifRésumé : (auteur) This paper aims to qualify the behaviour of contributors to OpenStreetMap (OSM), a volunteered geographic information (VGI) project, through a multigraph approach. The main purpose is to reproduce contributor’s interactions in a more comprehensive way. First, we define a multigraph that combines existing spatial collaboration networks from the literature with new graphs that illustrate collaboration based on specific aspects of the VGI modes of contribution through semantics, geometry and topology. Indeed, the ways that contributors interact with one another through editing, completion, or even consumption may provide additional information on each user’s operation mode and therefore, on the quality of the contributed data. Social collaborations drawn from indirect criteria – for example, comparisons between contributors’ activity areas – can also be contemplated under another network. Second, the resulting multigraph is analysed using data mining approaches to characterise individuals and identify behavioural groups. The implementation of a multiplex network based on an OSM data sample and an initial analysis make it possible to identify useful behaviours for data qualification. The initial results characterise some contributors as pioneers, moderators and truthful contributors, according to their special roles in the graphs. Mapping elements that include these contributors’ participation are likely to be reliable data. Numéro de notice : A2019-025 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1556395 Date de publication en ligne : 17/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1556395 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91958
in International journal of geographical information science IJGIS > vol 33 n° 7 - 8 (July - August 2019) . - pp 1651 - 1682[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2019072 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019071 RAB Revue Centre de documentation En réserve L003 Disponible Is deep learning the new agent for map generalization? / Guillaume Touya in International journal of cartography, vol 5 n° 2-3 (July - November 2019)
[article]
Titre : Is deep learning the new agent for map generalization? Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Xiang Zhang, Auteur ; Imran Lokhat , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Conférence : ICC 2019, 29th International Cartographic Conference ICA, Mapping everything for everyone 15/07/2019 20/07/2019 Tokyo Japon Open Access Proceedings of the ICA Article en page(s) : pp 142 - 157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] système multi-agents
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) The automation of map generalization has been keeping researchers in cartography busy for years. Particularly great progress was made in the late 90s with the use of the multi-agent paradigm. Although the current use of automatic processes in some national mapping agencies is a great achievement, there are still many unsolved issues and research seems to stagnate in the recent years. With the success of deep learning in many fields of science, including geographic information science, this paper poses the controversial question of the title: is deep learning the new agent, i.e. the technique that will make generalization research bridge the gap to fully automated generalization processes? The paper neither responds a clear yes nor a clear no but discusses what issues could be tackled with deep learning and what the promising perspectives. Some preliminary experiments with building generalization or data enrichments are presented to support the discussion. Numéro de notice : A2019-235 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1613071 Date de publication en ligne : 09/05/2019 En ligne : https://doi.org/10.1080/23729333.2019.1613071 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92932
in International journal of cartography > vol 5 n° 2-3 (July - November 2019) . - pp 142 - 157[article]Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach / Jian Zhao in Marine geodesy, vol 42 n° 4 (July 2019)
[article]
Titre : Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach Type de document : Article/Communication Auteurs : Jian Zhao, Auteur ; Yanguo Fan, Auteur ; Yuxiang Mu, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] détection d'anomalie
[Termes IGN] données altimétriques
[Termes IGN] données Jason
[Termes IGN] données Topex-Poseidon
[Termes IGN] image ERS-SAR
[Termes IGN] méthode des moindres carrés
[Termes IGN] montée du niveau de la mer
[Termes IGN] Pacifique nord
[Termes IGN] prévision
[Termes IGN] réseau neuronal artificiel
[Termes IGN] série temporelleRésumé : (auteur) Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2 cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. Numéro de notice : A2019-281 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2019.1626306 Date de publication en ligne : 12/06/2019 En ligne : https://doi.org/10.1080/01490419.2019.1626306 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93115
in Marine geodesy > vol 42 n° 4 (July 2019) . - pp 344 - 366[article]Using direct transformation approach as an alternative technique to fuse global digital elevation models with GPS/levelling measurements in Egypt / Hossam Talaat Elshambaky in Journal of applied geodesy, vol 13 n° 3 (July 2019)
[article]
Titre : Using direct transformation approach as an alternative technique to fuse global digital elevation models with GPS/levelling measurements in Egypt Type de document : Article/Communication Auteurs : Hossam Talaat Elshambaky, Auteur Année de publication : 2019 Article en page(s) : pp 159 - 177 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Nivellement
[Termes IGN] collocation par moindres carrés
[Termes IGN] Egypte
[Termes IGN] formule de Molodensky
[Termes IGN] fusion de données
[Termes IGN] méthode fiable
[Termes IGN] MNS GTOPO30
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste margeRésumé : (auteur) Open global digital elevation models (GDEMs) represent a free and important source of information that is available to any country. Fusion processing between global and national digital elevation models is neither easy nor inexpensive. Hence, an alternative solution to fuse a GDEM (GTOPO30 or SRTM 1) with national GPS/levelling measurements is adopted. Herein, a transformation process between the GDEMs and national GPS/levelling measurements is applied using parametric and non-parametric equations. Two solutions are implemented before and after the filtration of raw data from outliers to assess the ability of the generated corrector surface model to absorb the effect of the outliers’ existence. In addition, a reliability analysis is conducted to select the most suitable transformation technique. We found that when both the fitting and prediction properties have equal priority, least-squares collocation integrated with a least-squares support vector machine inherited with a linear or polynomial kernel function exhibits the most accurate behavior. For the GTOPO30 model, before filtration of the raw data, there is an improvement in the mean and root mean square of errors by 39.31 % and 68.67 %, respectively. For the SRTM 1 model, the improvement in mean and root mean square values reached 86.88 % and 75.55 %, respectively. Subsequently, after the filtration process, these values became 3.48 % and 36.53 % for GTOPO30 and 85.18 % and 47.90 % for SRTM 1. Furthermore, it is found that using a suitable mathematical transformation technique can help increase the precision of classic GDEMs, such as GTOPO30, making them to be equal or more accurate than newer models, such as SRTM 1, which are supported by more advanced technologies. This can help overcome the limitation of shortage of technology or restricted data, particularly in developed countries. Henceforth, the proposed direct transformation technique represents an alternative faster and more economical way to utilize unfiltered measurements of GDEMs to estimate national digital elevations in areas with limited data. Numéro de notice : A2019-283 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0050 Date de publication en ligne : 05/03/2019 En ligne : https://doi.org/10.1515/jag-2018-0050 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93118
in Journal of applied geodesy > vol 13 n° 3 (July 2019) . - pp 159 - 177[article]Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)PermalinkVGI contributors’ awareness of geographic information quality and its effect on data quality: a case study from Japan / Jun Yamashita in International journal of cartography, vol 5 n° 2-3 (July - November 2019)PermalinkA cognitive framework for road detection from high-resolution satellite images / Naveen Chandra in Geocarto international, vol 34 n° 8 ([15/06/2019])PermalinkComprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. 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