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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 174 (April 2021) . - pp 19 - 34 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 descripteurs IGN] arbre remarquable [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] sécurité routière [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] tessellation [Termes descripteurs IGN] transformation de Hough Résumé : (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 : 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 [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)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 2 (March 2021) . - pp 169 - 185 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 descripteurs IGN] comportement [Termes descripteurs IGN] conception cartographique [Termes descripteurs IGN] convivialité [Termes descripteurs IGN] itinéraire [Termes descripteurs IGN] représentation des détails topographiques [Termes descripteurs IGN] symbole graphique [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] visualisation cartographique Résumé : (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 [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)  

Public [article]  inComputers and graphics > vol 94 n° 1 (February 2021) . - pp 76 - 86 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 descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] feu de circulation [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] navigation autonome [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] trafic routier Résumé : (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 [article]

Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / R. Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 18 - 35 Titre : Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation Type de document : Article/Communication Auteurs : R. Yazdan, Auteur ; M. Varshosaz, Auteur Année de publication : 2021 Article en page(s) : pp 18 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] corrélation à l'aide de traits caractéristiques [Termes descripteurs IGN] corrélation croisée normalisée [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] reconnaissance d'objets [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] SURF (algorithme) [Termes descripteurs IGN] Téhéran [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Automatic detection and recognition of traffic signs have many applications. However, some problems can affect the accuracy of the existing algorithms, such as changes in environmental light conditions, shadows, the presence of objects of the same colour, significant changes in scale and rotation, as well as obstacles in front of the traffic signs. To overcome these difficulties, a reference image database is usually used that includes different modes of appearing the traffic signs in the images. In order to overcome the effects of scale and rotation, in this paper a new method is presented in which only one reference image is needed for each sign to recognise the traffic sign in an image. In the proposed method, imaging is done in stereo. Using the captured image pair, a virtual image is generated which is then used to recognise the sign. As a result, the recognition is carried out with a minimum number of reference images. Experiments show that the proposed algorithm significantly improves recognition results. The traffic signs are recognised with 93.1% accuracy that enjoys a 4.9% improvement over traditional methods. Numéro de notice : A2021-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.003 date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96304 [article]

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Inferencing hourly traffic volume using data-driven machine learning and graph theory / Zhiyan Yi in Computers, Environment and Urban Systems, vol 85 (January 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 85 (January 2021) . - n° 101548 Titre : Inferencing hourly traffic volume using data-driven machine learning and graph theory Type de document : Article/Communication Auteurs : Zhiyan Yi, Auteur ; Xiaoyue Cathy Liu, Auteur ; Nikola Markovic, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101548 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] Extreme Gradient Machine [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] planification [Termes descripteurs IGN] théorie des graphes [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] Utah (Etas-Unis) Résumé : (auteur) Traffic volume is a critical piece of information in many applications, such as transportation long-range planning and traffic operation analysis. Effectively capturing traffic volumes on a network scale is beneficial to Transportation Systems Management & Operations (TSM&O). Yet it is impractical to install sensors to cover a large road network. To address this issue, spatial prediction techniques are widely performed to estimate traffic volumes at sites without sensors. In retrospect, most relevant studies resort to machine learning methods and treat each prediction location independently during the training process, ignoring the potential spatial dependency among them. This paper presents an innovative spatial prediction method of hourly traffic volume on a network scale. To achieve this, we applied a state-of-the-art tree ensemble model - extreme gradient boosting tree (XGBoost) - to handle the large-scale features and hourly traffic volume samples, due to the model's powerful scalability. Moreover, spatial dependency among road segments is taken into account in the proposed model using graph theory. Specifically, we created a traffic network graph leveraging probe trajectory data, and implemented a graph-based approach - breadth first search (BFS) - to search neighboring sites in this graph for computing spatial dependency. The proposed spatial dependency feature is subsequently incorporated as a new feature fed into XGBoost. The proposed model is tested on the road network in the state of Utah. Numerical results not only indicate high computational efficiency of the proposed model, but also demonstrate significant improvement in prediction accuracy of hourly traffic volume comparing with the benchmarked models. Numéro de notice : A2021-004 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101548 date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101548 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96271 [article]

Route intersection reduction with connected autonomous vehicles / Sadegh Motallebi in Geoinformatica [en ligne], vol 25 n° 1 (January 2021)  

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Empirical 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)  

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Using 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)  

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Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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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)  

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Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Network-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

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Measuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

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Semi-automated framework for generating cycling lane centerlines on roads with roadside barriers from noisy MLS data / Yang Ma in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam / Khanh Giang Le in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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