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3D micro-mapping : Towards assessing the quality of crowdsourcing to support 3D point cloud analysis / Benjamin Herfort in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)
[article]
Titre : 3D micro-mapping : Towards assessing the quality of crowdsourcing to support 3D point cloud analysis Type de document : Article/Communication Auteurs : Benjamin Herfort, Auteur ; Bernhard Höfle, Auteur ; Carolin Klonner, Auteur Année de publication : 2018 Article en page(s) : pp 73 - 83 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] arbre (flore)
[Termes IGN] cartographie collaborative
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données localisées des bénévoles
[Termes IGN] évaluation des données
[Termes IGN] production participative
[Termes IGN] qualité des données
[Termes IGN] semis de points
[Termes IGN] villeRésumé : (Auteur) In this paper, we propose a method to crowdsource the task of complex three-dimensional information extraction from 3D point clouds. We design web-based 3D micro tasks tailored to assess segmented LiDAR point clouds of urban trees and investigate the quality of the approach in an empirical user study. Our results for three different experiments with increasing complexity indicate that a single crowdsourcing task can be solved in a very short time of less than five seconds on average. Furthermore, the results of our empirical case study reveal that the accuracy, sensitivity and precision of 3D crowdsourcing are high for most information extraction problems. For our first experiment (binary classification with single answer) we obtain an accuracy of 91%, a sensitivity of 95% and a precision of 92%. For the more complex tasks of the second Experiment 2 (multiple answer classification) the accuracy ranges from 65% to 99% depending on the label class. Regarding the third experiment – the determination of the crown base height of individual trees – our study highlights that crowdsourcing can be a tool to obtain values with even higher accuracy in comparison to an automated computer-based approach. Finally, we found out that the accuracy of the crowdsourced results for all experiments is hardly influenced by characteristics of the input point cloud data and of the users. Importantly, the results’ accuracy can be estimated using agreement among volunteers as an intrinsic indicator, which makes a broad application of 3D micro-mapping very promising. Numéro de notice : A2018-078 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89440
in ISPRS Journal of photogrammetry and remote sensing > vol 137 (March 2018) . - pp 73 - 83[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018033 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018032 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt A new model for cadastral surveying using crowdsourcing / K. Apostolopoulos in Survey review, vol 50 n° 359 (March 2018)
[article]
Titre : A new model for cadastral surveying using crowdsourcing Type de document : Article/Communication Auteurs : K. Apostolopoulos, Auteur ; M. Geli, Auteur ; P. Petrelli, Auteur ; Chryssy Potsiou, Auteur ; C. Ioannidis, Auteur Année de publication : 2018 Article en page(s) : pp 122 - 133 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] ArcGIS
[Termes IGN] banlieue
[Termes IGN] cadastre étranger
[Termes IGN] données localisées des bénévoles
[Termes IGN] Grèce
[Termes IGN] jeu
[Termes IGN] périphérie urbaine
[Termes IGN] production participative
[Termes IGN] zone rurale
[Termes IGN] zone urbaineRésumé : (auteur) A ‘fit-for-purpose’ approach is developed, tested and presented for cadastral surveys through increased owners’ participation using new technology and m-government services. Three case studies are reported, for urban, suburban and rural areas, with a combined use of two mobile applications: a commercial software package (ESRI’s Collector for ArcGIS) and an opensource self-developed application named BoundGeometry. The parameters of time, quality and accuracy are assessed and the identified difficulties are classified. It is concluded that the method is applicable both in developed and developing countries, and each time adjustable to the available infrastructure. Numéro de notice : A2018-179 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1080/00396265.2016.1253522 Date de publication en ligne : 15/11/2016 En ligne : https://doi.org/10.1080/00396265.2016.1253522 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89823
in Survey review > vol 50 n° 359 (March 2018) . - pp 122 - 133[article]Recognition of building group patterns in topographic maps based on graph partitioning and random forest / Xianjin He in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)
[article]
Titre : Recognition of building group patterns in topographic maps based on graph partitioning and random forest Type de document : Article/Communication Auteurs : Xianjin He, Auteur ; Xinchang Zhang, Auteur ; Qinchuan Xin, Auteur Année de publication : 2018 Article en page(s) : pp 26 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] graphe
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] partitionnement
[Termes IGN] reconnaissance de formes
[Termes IGN] ville
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Recognition of building group patterns (i.e., the arrangement and form exhibited by a collection of buildings at a given mapping scale) is important to the understanding and modeling of geographic space and is hence essential to a wide range of downstream applications such as map generalization. Most of the existing methods develop rigid rules based on the topographic relationships between building pairs to identify building group patterns and thus their applications are often limited. This study proposes a method to identify a variety of building group patterns that allow for map generalization. The method first identifies building group patterns from potential building clusters based on a machine-learning algorithm and further partitions the building clusters with no recognized patterns based on the graph partitioning method. The proposed method is applied to the datasets of three cities that are representative of the complex urban environment in Southern China. Assessment of the results based on the reference data suggests that the proposed method is able to recognize both regular (e.g., the collinear, curvilinear, and rectangular patterns) and irregular (e.g., the L-shaped, H-shaped, and high-density patterns) building group patterns well, given that the correctness values are consistently nearly 90% and the completeness values are all above 91% for three study areas. The proposed method shows promises in automated recognition of building group patterns that allows for map generalization. Numéro de notice : A2018-073 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89433
in ISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 26 - 40[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018023 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018022 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Using mobility data as proxy for measuring urban vitality / Patrizia Sulis in Journal of Spatial Information Science (JoSIS), n° 16 ([01/02/2018])
[article]
Titre : Using mobility data as proxy for measuring urban vitality Type de document : Article/Communication Auteurs : Patrizia Sulis, Auteur ; Ed Manley, Auteur ; Chen Zhong, Auteur ; Michael Batty, Auteur Année de publication : 2018 Article en page(s) : pp 137 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] données localisées
[Termes IGN] données massives
[Termes IGN] dynamique spatiale
[Termes IGN] mobilité humaine
[Termes IGN] villeRésumé : (auteur) In this paper, we propose a computational approach to Jane Jacobs' concept of diversity and vitality, analyzing new forms of spatial data to obtain quantitative measurements of urban qualities frequently employed to evaluate places. We use smart card data collected from public transport to calculate a diversity value for each research unit. Diversity is composed of three dynamic attributes: intensity, variability, and consistency, each measuring different temporal variations of mobility flows. We then apply a regression model to establish the relationship between diversity and vitality, using Twitter data as a proxy for human activity in urban space. Final results (also validated using data sourced from OpenStreetMap) unveil which are the most vibrant areas in London. Numéro de notice : A2018-684 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.5311/JOSIS.2018.16.38 En ligne : https://josis.org/index.php/josis/article/view/92 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98694
in Journal of Spatial Information Science (JoSIS) > n° 16 [01/02/2018] . - pp 137 - 162[article]Detection and localization of traffic signals with GPS floating car data and Random Forest / Yann Méneroux (2018)
Titre : Detection and localization of traffic signals with GPS floating car data and Random Forest Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Hiroshi Kanasugi, Auteur ; Guillaume Saint Pierre, Auteur ; Arnaud Le Guilcher , Auteur ; Sébastien Mustière , Auteur ; Ryosuke Shibasaki, Auteur ; Yugo Kato, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2018 Collection : LIPIcs Leibniz International Proceedings in Informatics, ISSN 1868-8969 num. 114 Projets : 1-Pas de projet / Conférence : GIScience 2018, 10th International Conference on Geographic Information Science 28/08/2018 31/08/2018 Melbourne Australie Open Access Proceedings Importance : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage automatique
[Termes IGN] base de données routières
[Termes IGN] carte routière
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distribution spatiale
[Termes IGN] guidage de véhicules
[Termes IGN] inférence
[Termes IGN] Japon
[Termes IGN] trace GPS
[Termes IGN] trafic routier
[Termes IGN] traitement de données localisées
[Termes IGN] villeRésumé : (auteur) As Floating Car Data are becoming increasingly available, in recent years many research works focused on leveraging them to infer road map geometry, topology and attributes. In this paper, we present an algorithm, relying on supervised learning to detect and localize traffic signals based on the spatial distribution of vehicle stop points. Our main contribution is to provide a single framework to address both problems. The proposed method has been experimented with a one-month dataset of real-world GPS traces, collected on the road network of Mitaka (Japan). The results show that this method provides accurate results in terms of localization and performs advantageously compared to the OpenStreetMap database in exhaustivity. Among many potential applications, the output predictions may be used as a prior map and/or combined with other sources of data to guide autonomous vehicles. Numéro de notice : C2018-051 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.4230/LIPIcs.GISCIENCE.2018.11 Date de publication en ligne : 30/07/2018 En ligne : http://drops.dagstuhl.de/opus/volltexte/2018/9339/ Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91335 Documents numériques
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