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Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours / Amir Hossein Safaie in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)
[article]
Titre : Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours Type de document : Article/Communication Auteurs : Amir Hossein Safaie, Auteur ; Heidar Rastiveis, Auteur ; Alireza Shams, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 19 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre remarquable
[Termes IGN] arbre urbain
[Termes IGN] détection d'arbres
[Termes IGN] détection de contours
[Termes IGN] diagramme de Voronoï
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] sécurité routière
[Termes IGN] semis de points
[Termes IGN] transformation de HoughRésumé : (auteur) Trees are important road-side objects, and their geometric information plays an essential role in road studies and safety analyses. This paper proposes an efficient method for the automated creation of a road-side tree inventory using Mobile Terrestrial Lidar System (MTLS) point clouds. In the proposed method ground points are filtered through preprocessing to reduce processing time. Next, tree trunks are detected by performing a Hough Transform (HT) algorithm on several generated raster images from the point clouds. By initiating an approximate area of a tree’s foliage through a Voronoi Tessellation (VT) algorithm, the accurate boundary of the foliage is identified by applying Active Contour (AC) models. By extracting the points within this foliage boundary the geometric characteristics of each tree are obtained. This method was evaluated with two sample point clouds from different MTLS systems, and the algorithm correctly extracted all of the trees from both datasets. Additionally, comparing the calculated parameters with manually observed measures, the accuracy of the obtained geometric parameters were promising. Numéro de notice : A2021-206 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.026 Date de publication en ligne : 14/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.026 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97183
in ISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 19 - 34[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021041 SL Revue Centre de documentation Revues en salle Disponible 081-2021043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks / Bozhao Li in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
[article]
Titre : A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks Type de document : Article/Communication Auteurs : Bozhao Li, Auteur ; Zhongliang Cai, Auteur ; Mengjun Kang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 717 - 740 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] appariement de cartes
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] Pékin (Chine)
[Termes IGN] réseau routier
[Termes IGN] taux d'échantillonnage
[Termes IGN] trafic routier
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] zone urbaineRésumé : (auteur) Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks. Numéro de notice : A2021-269 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2020.1825721 Date de publication en ligne : 20/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1825721 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97326
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 717 - 740[article]Evaluating the effectiveness of different cartographic design variants for influencing route choice / Stefan Fuest in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)
[article]
Titre : Evaluating the effectiveness of different cartographic design variants for influencing route choice Type de document : Article/Communication Auteurs : Stefan Fuest, Auteur ; Susanne Grüner, Auteur ; Mark Vollrath, Auteur ; Monika Sester, Auteur Année de publication : 2021 Article en page(s) : pp 169 - 185 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique
[Termes IGN] comportement
[Termes IGN] conception cartographique
[Termes IGN] convivialité
[Termes IGN] itinéraire
[Termes IGN] représentation des détails topographiques
[Termes IGN] symbole graphique
[Termes IGN] trafic routier
[Termes IGN] visualisation cartographiqueRésumé : (Auteur) This paper addresses the suitability of different cartographic design variants for visually communicating recommended routes. We performed a user study, investigating the potential of six different design variants (color hue, distortion, length distortion, size, spacing, and symbols) for influencing route choice using cartographic visualization methods while recommending a longer, but less congested route. The visualizations for all design variants have been prepared in three different levels of intensity of modification (weak, medium, and strong). Although the input data (traffic density) is the same for all representation methods, variations are each visualized using different cartographic design principles. Our results showed that in general, for the majority of routing scenarios, the participants’ route choice has been significantly influenced toward choosing the recommended route – indicating that the modification of route visualizations does actually lead to a different route choice behavior. Results further revealed that for most variants, willingness to choose the recommended route increases with higher intensity of modification. While some of the design variants like symbols or length distortion have been found effective for recommending routes at all levels of intensity, others like size and spacing have not been found suitable. A comparison between route choices and estimated route characteristics suggested a close relationship between willingness to choose the recommended route and the characteristics participants associate with the representation. In particular, route visualizations that create an impression of faster, more convenient, or more fluent travel experience are more likely to influence route choice behavior. Numéro de notice : A2021-181 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1855251 Date de publication en ligne : 06/01/2021 En ligne : https://doi.org/10.1080/15230406.2020.1855251 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97068
in Cartography and Geographic Information Science > vol 48 n° 2 (March 2021) . - pp 169 - 185[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2021021 RAB Revue Centre de documentation En réserve L003 Disponible Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs / Roholah Yazdan in Geomatica, vol 75 n° 1 (Mars 2021)
[article]
Titre : Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs Type de document : Article/Communication Auteurs : Roholah Yazdan, Auteur ; Masood Varshosaz, Auteur ; Saied Pirasteh, Auteur ; Fabio Remondino, Auteur Année de publication : 2021 Article en page(s) : pp 28 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] contrainte géométrique
[Termes IGN] espace colorimétrique
[Termes IGN] programmation par contraintes
[Termes IGN] signalisation routièreRésumé : (auteur) Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided. Numéro de notice : A2021-608 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1139/geomat-2020-0010 En ligne : https://doi.org/10.1139/geomat-2020-0010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98322
in Geomatica > vol 75 n° 1 (Mars 2021) . - pp 28 - 50[article]Deep traffic light detection by overlaying synthetic context on arbitrary natural images / Jean Pablo Vieira de Mello in Computers and graphics, vol 94 n° 1 (February 2021)
[article]
Titre : Deep traffic light detection by overlaying synthetic context on arbitrary natural images Type de document : Article/Communication Auteurs : Jean Pablo Vieira de Mello, Auteur ; Lucas Tabelini, Auteur ; Rodrigo F. Berriel, Auteur Année de publication : 2021 Article en page(s) : pp 76 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] échantillonnage d'image
[Termes IGN] feu de circulation
[Termes IGN] image à haute résolution
[Termes IGN] navigation autonome
[Termes IGN] signalisation routière
[Termes IGN] trafic routierRésumé : (auteur) Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights. However, acquiring and annotating real data can be extremely costly in terms of time and effort. In this context, we propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds that are not related to the traffic domain. Thus, a large amount of training data can be generated without annotation efforts. Furthermore, it also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state. Experiments show that it is possible to achieve results comparable to those obtained with real training data from the problem domain, yielding an average mAP and an average F1-score which are each nearly 4 p.p. higher than the respective metrics obtained with a real-world reference model. Numéro de notice : A2021-151 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cag.2020.09.012 Date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1016/j.cag.2020.09.012 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97027
in Computers and graphics > vol 94 n° 1 (February 2021) . - pp 76 - 86[article]PermalinkImproving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkInferencing hourly traffic volume using data-driven machine learning and graph theory / Zhiyan Yi in Computers, Environment and Urban Systems, vol 85 (January 2021)PermalinkMachine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles / Tatiana Babicheva (2021)PermalinkPermalinkRecueil des contributions, Colloque international Tous (im)mobiles, tous cartographes ? Approches cartographiques des mobilités, des circulations, des flux et des déplacements : Méthodes, outils, représentations, pratiques et usages / Françoise Bahoken (2021)PermalinkRoute intersection reduction with connected autonomous vehicles / Sadegh Motallebi in Geoinformatica, vol 25 n° 1 (January 2021)PermalinkPermalinkEmpirical assessment of road network resilience in natural hazards using crowdsourced traffic data / Yi Qiang in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)PermalinkUsing multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)Permalink