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Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1117 - 1136 Titre : Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data Type de document : Article/Communication Auteurs : Shivangi Srivastava, Auteur ; John E. Vargas-Muñoz, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2020 Article en page(s) : pp 1117 - 1136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] base de données urbaines [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données localisées des bénévoles [Termes IGN] données localisées libres [Termes IGN] Ile-de-France [Termes IGN] image Streetview [Termes IGN] image terrestre [Termes IGN] information géographique [Termes IGN] méthode heuristique [Termes IGN] OpenStreetMap [Termes IGN] réseau social Résumé : (auteur) We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization. Numéro de notice : A2020-269 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542698 Date de publication en ligne : 18/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542698 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95041 [article]

Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])  

Public [article]  inGeocarto international > vol 35 n° 6 [01/05/2020] . - pp 623 - 640 Titre : Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics Type de document : Article/Communication Auteurs : E. Banzhaf, Auteur ; H. Kollai, Auteur ; A. Kindler, Auteur Année de publication : 2020 Article en page(s) : pp 623 - 640 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse d'image orientée objet [Termes IGN] base de données orientée objet [Termes IGN] base de données urbaines [Termes IGN] bati [Termes IGN] bien-être collectif [Termes IGN] cartographie urbaine [Termes IGN] développement durable [Termes IGN] données lidar [Termes IGN] écosystème urbain [Termes IGN] gestion urbaine [Termes IGN] orthophotographie [Termes IGN] population urbaine [Termes IGN] santé [Termes IGN] télédétection Résumé : (auteur) Mapping urban structures is a vital prerequisite for urban planners to enhance their database for a liveable city dedicated to sustainable development. Therefore, it is significant to measure urban grey and green structures at the scale of local districts to understand the urban structure and residential needs for urban ecosystem services. For a detailed analysis we exploit digital orthophotos (DOP), LiDAR data, and vital statistics. We use remote sensing techniques to create an Object-based Image Analysis (OBIA) that differentiates grey and green structures with high precision and at refined scale. This spatial information is linked with allocated population and health-related indicators to identify built-up types with highest population densities and local districts with deficits in the provision of different green structures. Our results show the share of built-up structures and the contribution of green structures to urban ecosystem services, human health and well-being at local district level. Numéro de notice : A2020-202 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1524514 Date de publication en ligne : 23/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1524514 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94877 [article]

Influence of sample size on automatic positional accuracy assessment methods for urban areas / Francisco Javier Ariza-López in ISPRS International journal of geo-information, vol 7 n° 6 (June 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 6 (June 2018) Titre : Influence of sample size on automatic positional accuracy assessment methods for urban areas Type de document : Article/Communication Auteurs : Francisco Javier Ariza-López, Auteur ; Juan J. Ruiz-Lendínez, Auteur ; Manuel A. Ureña-Cámara, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données urbaines [Termes IGN] distance de Kolmogorov-Smirnov [Termes IGN] échantillon [Termes IGN] polygone [Termes IGN] précision de localisation [Termes IGN] précision planimétrique [Termes IGN] qualité des données [Termes IGN] zone urbaine Résumé : (Auteur) In recent years, new approaches aimed to increase the automation level of positional accuracy assessment processes for spatial data have been developed. However, in such cases, an aspect as significant as sample size has not yet been addressed. In this paper, we study the influence of sample size when estimating the planimetric positional accuracy of urban databases by means of an automatic assessment using polygon-based methodology. Our study is based on a simulation process, which extracts pairs of homologous polygons from the assessed and reference data sources and applies two buffer-based methods. The parameter used for determining the different sizes (which range from 5 km up to 100 km) has been the length of the polygons’ perimeter, and for each sample size 1000 simulations were run. After completing the simulation process, the comparisons between the estimated distribution functions for each sample and population distribution function were carried out by means of the Kolmogorov–Smirnov test. Results show a significant reduction in the variability of estimations when sample size increased from 5 km to 100 km. Numéro de notice : A2018-346 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7060200 Date de publication en ligne : 28/05/2018 En ligne : https://doi.org/10.3390/ijgi7060200 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90570 [article]

Urban 3D segmentation and modelling from street view images and LiDAR point clouds / Pouria Babahajiani in Machine Vision and Applications, sans n° ([01/06/2017])  

Public [article]  inMachine Vision and Applications > sans n° [01/06/2017] Titre : Urban 3D segmentation and modelling from street view images and LiDAR point clouds Type de document : Article/Communication Auteurs : Pouria Babahajiani, Auteur ; Lixin Fan, Auteur ; Joni-Kristian Kämäräinen, Auteur ; Moncef Gabbouj, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] base de données urbaines [Termes IGN] cartographie urbaine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] façade [Termes IGN] image terrestre [Termes IGN] milieu urbain [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) 3D urban maps with semantic labels and metric information are not only essential for the next generation robots such autonomous vehicles and city drones, but also help to visualize and augment local environment in mobile user applications. The machine vision challenge is to generate accurate urban maps from existing data with minimal manual annotation. In this work, we propose a novel methodology that takes GPS registered LiDAR (Light Detection And Ranging) point clouds and street view images as inputs and creates semantic labels for the 3D points clouds using a hybrid of rule-based parsing and learning-based labelling that combine point cloud and photometric features. The rule-based parsing boosts segmentation of simple and large structures such as street surfaces and building facades that span almost 75% of the point cloud data. For more complex structures, such as cars, trees and pedestrians, we adopt boosted decision trees that exploit both structure (LiDAR) and photometric (street view) features. We provide qualitative examples of our methodology in 3D visualization where we construct parametric graphical models from labelled data and in 2D image segmentation where 3D labels are back projected to the street view images. In quantitative evaluation we report classification accuracy and computing times and compare results to competing methods with three popular databases: NAVTEQ True, Paris-Rue-Madame and TLS (terrestrial laser scanned) Velodyne. Numéro de notice : A2017-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00138-017-0845-3 En ligne : https://doi.org/10.1007/s00138-017-0845-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85269 [article]

SinoGrids: a practice for open urban data in China / Xinyue Ye in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

Public [article]  inCartography and Geographic Information Science > vol 43 n° 5 (November 2016) . - pp 379 - 392 Titre : SinoGrids: a practice for open urban data in China Type de document : Article/Communication Auteurs : Xinyue Ye, Auteur ; Qunying Huang, Auteur ; Wenwen Li, Auteur Année de publication : 2016 Article en page(s) : pp 379 - 392 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] base de données urbaines [Termes IGN] Chine [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] données ouvertes [Termes IGN] interface utilisateur [Termes IGN] partage de données localisées [Termes IGN] planification urbaine [Termes IGN] ville intelligente Résumé : (Auteur) In the past decade, an explosion of data has taken place in Chinese cities due to widespread use of mobile Internet devices, Web 2.0 applications, and the development of the “Wired City.” With advances in data storage and high-performance computing, big/open urban data have opened up important avenues for urban studies, planning practice, and commercial consultancy. Urban researchers and planners are eager to make use of these abundant, sophisticated, and dynamic data to deepen their understanding on urban form and functions. However, in practice, access to such urban data is limited in China due to institutional constraints on data distribution and data holders’ hesitation to share data. And this hampers urban analytics. To draw reliable conclusions about the workings of complex urban systems, efficient and effective interoperation of multisource urban datasets is needed. Also, dealing with the heterogeneity between datasets is an equally critical challenge, especially for urban planners and government officers. They would derive value from data analytics, but have little data processing experience. To address these issues, we initiated SinoGrids (Plan Xu Xiake), a crowdsourcing platform that standardizes (or “downscales”) microscale urban data in China to facilitate its sharing and interoperation. To assess the performance evaluation of SinoGrids, we propose field-testing with actual urban data and their potential users. Digital desert, a son project of SinoGrids is also included. Numéro de notice : A2016-689 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1129914 En ligne : https://doi.org/10.1080/15230406.2015.1129914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82017 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2016051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

La ville à l’échelle de l’Europe : apports du couplage et de l’expertise de bases de données issues de l’imagerie satellitale / Anne Bretagnolle in Revue internationale de géomatique, vol 26 n° 1 (janvier - mars 2016)  

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Inverse procedural Street Modelling: from interactive to automatic reconstruction / Rémi Cura (2016)  

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Efficient visualization of urban simulation data using modern GPUs / Aleksandr Zagarskikh in Procedia Computer Science, vol 51 (2015)  

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Methodological framework for shrinking cities case study research: northwest region of Bosnia and Herzegovina / Tijana M. Vujičić, in Geodetski vestnik, vol 59 n° 3 (September - November 2015)  

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Urbanization of the United States over two centuries: an approach based on a long-term database (1790–2010) / Anne Bretagnolle in International journal of geographical information science IJGIS, vol 29 n° 5 (May 2015)  

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Cartes et légendes d'Afrique / Françoise de Blomac in DécryptaGéo le mag, n° 164 (février 2015)

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Vers une sémiologie graphique 3D appliquée à l'urbanisme / Florian Pelloie (2014)

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The rise of OpenStreetMap / E. Van Rees in Geoinformatics, vol 15 n° 4 (01/06/2012)

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Scénario de mise en place du SIG urbain de la ville de Fès (Maroc) / Abdelkader El Garouani in Géomatique expert, n° 85 (01/03/2012)

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Obtenir des cartes urbaines par intégration de données multisources au sein d’un SIG : application à la ville de Fès / Rachid A. Barry (01/09/2011)

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