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A graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1382-1401 Titre : A graph convolutional network model for evaluating potential congestion spots based on local urban built environments Type de document : Article/Communication Auteurs : Kun Qin, Auteur ; Yuanquan Xu, Auteur ; Chaogui Kang, Auteur ; Mei-Po Kwan, Auteur Année de publication : 2020 Article en page(s) : pp 1382-1401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] graphe [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] taxi [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] Wuhan (Chine) [Termes descripteurs IGN] zone urbaine dense Résumé : (Auteur) Automatically identifying potential congestion spots in cities has significant practical implications for efficient urban development and management. It requires the ability to examine the relationships between urban built environment features and traffic congestion situations. This article presents a novel and effective approach for achieving the task based on a machine‐learning technique and publicly available street‐view imagery and point‐of‐interest (POI) data. The proposed multiple‐graph‐based convolutional network architecture can: (a) extract essential urban built environment features from street‐view imagery and neighboring POIs; (b) model the spatial dependencies between traffic congestion on road networks via graph convolution; and (c) evaluate the risk level of road intersections to emerging congestion situations based on local built environment features. We apply the model to Wuhan in China, and predict the potential congestion spots across the city. The results confirm that the model prediction is highly consistent (about 85.5%) when compared to the ground‐truth data based on traffic indices derived from a big taxi GPS trajectory dataset. This research enhances the understanding of traffic congestion situations under various geographic, societal, and economic contexts based on easily accessible road, street‐view, and POI datasets at large spatiotemporal scales. Numéro de notice : A2020-702 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12641 date de publication en ligne : 04/06/2020 En ligne : https://doi.org/10.1111/tgis.12641 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96225 [article]

Local color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? / Istvan G. Lauko in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 222 - 236 Titre : Local color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? Type de document : Article/Communication Auteurs : Istvan G. Lauko, Auteur ; Adam Honts, Auteur ; Jacob Beihoff, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 222 - 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] couleur (variable spectrale) [Termes descripteurs IGN] densité de la végétation [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] image panoramique [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] indicateur environnemental [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Milwaukee [Termes descripteurs IGN] paysage urbain [Termes descripteurs IGN] rayonnement proche infrarouge Résumé : (auteur) Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems. These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy, but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover. Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View (GSV) images, made accessible by the Google Street View Image API. Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density, and it facilitates an analysis performed at the street-level. In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index. We deployed this image processing method and, using GSV images as a high-resolution GIS data source, we computed and mapped the green index of Milwaukee County, a 3,082 km2 urban/suburban county in Wisconsin. This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management, as well as for researchers investigating the correlation between environmental factors and human health outcomes. Numéro de notice : A2020-563 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1805367 date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1080/10095020.2020.1805367 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95880 [article]

Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 26 - 40 Titre : Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction Type de document : Article/Communication Auteurs : Qing Zhu, Auteur ; Zhendong Wang, Auteur ; Han Hu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 26 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] appariement de points [Termes descripteurs IGN] éclairage [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image terrestre [Termes descripteurs IGN] maillage [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] valeur aberrante Résumé : (auteur) Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. However, as the first step, the feature point matching between aerial and ground images is remarkably difficult, due to the large differences in viewpoint and illumination conditions. Previous studies based on geometry-aware image rectification have alleviated this problem, but the performance and convenience of this strategy are still limited by several flaws, e.g. quadratic image pairs, segregated extraction of descriptors and occlusions. To address these problems, we propose a novel approach: leveraging photogrammetric mesh models for aerial-ground image matching. The methods have linear time complexity with regard to the number of images. It explicitly handles low overlap using multi-view images. The proposed methods can be directly injected into off-the-shelf structure-from-motion (SFM) and multi-view stereo (MVS) solutions. First, aerial and ground images are reconstructed separately and initially co-registered through weak georeferencing data. Second, aerial models are rendered to the initial ground views, in which color, depth and normal images are obtained. Then, feature matching between synthesized and ground images are conducted through descriptor searching and geometry-constrained outlier removal. Finally, oriented 3D patches are formulated using the synthesized depth and normal images and the correspondences are propagated to the aerial views through patch-based matching. Experimental evaluations using five datasets reveal satisfactory performance of the proposed methods in aerial-ground image matching, which succeeds in all of the ten challenging pairs compared to only three for the second best. In addition, incorporation of existing SFM and MVS solutions enables more complete reconstruction results, with better internal stability. Numéro de notice : A2020-351 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.024 date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95234 [article]

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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 descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données urbaines [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données localisées libres [Termes descripteurs IGN] Ile-de-France [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] image terrestre [Termes descripteurs IGN] information géographique [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs 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]

Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 125 - 136 Titre : Geocoding of trees from street addresses and street-level images Type de document : Article/Communication Auteurs : Daniel Laumer, Auteur ; Nico Lang, Auteur ; Natalie Van Doorn, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse des correspondances [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] image panoramique [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] zone urbaine Résumé : (auteur) We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale. Numéro de notice : A2020-124 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.001 date de publication en ligne : 21/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94749 [article]

Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Mise en place d’une visionneuse de photographies immersives dans QGIS / Thibault Petiet (2020) 

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Semiautomatically register MMS LiDAR points and panoramic image sequence using road lamp and lane / Ningning Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 11 (November 2019)  

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Detecting and mapping traffic signs from Google Street View images using deep learning and GIS / Andrew Campbell in Computers, Environment and Urban Systems, vol 77 (september 2019)  

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The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests / Christopher Mulverhill in Annals of Forest Science [en ligne], Vol 76 n° 3 (September 2019)  

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Automatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)  

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Co‐registration of panoramic mobile mapping images and oblique aerial images / Phillipp Jende in Photogrammetric record, vol 34 n° 166 (June 2019)  

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More surface detail with one-two-pixel matching / Ewelina Rupnik (2019)    

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A vélo au travers des Andes, pour OpenStreetMap / Anonyme in Géomatique expert, n° 126 (janvier - février 2019)

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Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds / Xiang Gao in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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