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

Public [article]  inGeoinformatica [en ligne] > vol 25 n° 1 (January 2021) . - pp 99 - 125 Titre : Route intersection reduction with connected autonomous vehicles Type de document : Article/Communication Auteurs : Sadegh Motallebi, Auteur ; Hairuo Xie, Auteur ; Egemen Tanin, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 99 - 125 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] calcul d'itinéraire [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] chemin le plus court (algorithme) [Termes descripteurs IGN] gestion de trafic [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] trafic routier Résumé : (Auteur) A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated globally. We prototype a traffic management system for optimizing traffic with connected autonomous vehicles. The system allocates routes to the vehicles based on streaming traffic data. We develop two route assignment algorithms for the system. The algorithms can help to mitigate traffic congestions by reducing intersecting routes. Extensive experiments are conducted to compare the proposed algorithms and two state-of-the-art route assignment algorithms with both synthetic and real road networks in a simulated traffic management system. The experimental results show that the proposed algorithms outperform the competitors in terms of the travel time of the vehicles. Numéro de notice : A2021-093 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00420-z date de publication en ligne : 23/08/2020 En ligne : https://doi.org/10.1007/s10707-020-00420-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96933 [article]

Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 947 - 968 Titre : Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks Type de document : Article/Communication Auteurs : Mahmoud Saeedimoghaddam, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2020 Article en page(s) : pp 947 - 968 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] carte ancienne [Termes descripteurs IGN] carte numérisée [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] numérisation automatique [Termes descripteurs IGN] représentation cartographique [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them. Numéro de notice : A2020-205 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1696968 date de publication en ligne : 28/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1696968 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94882 [article]

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Method for an automatic alignment of imagery and vector data applied to cadastral information in Poland / Juan J. Ruiz-Lendínez in Survey review, vol 51 n° 365 (March 2019)  

Public [article]  inSurvey review > vol 51 n° 365 (March 2019) . - pp 123 - 134 Titre : Method for an automatic alignment of imagery and vector data applied to cadastral information in Poland Type de document : Article/Communication Auteurs : Juan J. Ruiz-Lendínez, Auteur ; B. Maćkiewicz, Auteur ; P. Motek, Auteur ; T. Stryjakiewicz, Auteur Année de publication : 2019 Article en page(s) : pp 123 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] conflation [Termes descripteurs IGN] données cadastrales [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] limite cadastrale [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] Pologne [Termes descripteurs IGN] Poznan [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] texture d'image Résumé : (Auteur) Nowadays, an important problem in combining vector data and imagery is that they rarely align. This problem can become particularly acute in the case of cadastral systems. In this study, and as part of the partnership between the Universities of Jaén and Adam Mickiewicz (Poznań), we provide a methodological proposal to assess the conflation procedures between cadastral vector data and imagery, improving the alignment between both data sets. To do this, we use an automatic alignment algorithm which detects road intersections from both data sets as control points by using image texture characterisation. With this method, we first train the system on the imagery to learn the road texture distribution, then we can obtain its segmentation according to its texture, and finally the system locates road intersection points. The last step is to align vector data and imagery by using different techniques. This algorithm is based on an earlier one, detailed in [Ruiz, J.J., Rubio, T.J., and Ureña, M.A., 2011b. Automatic extraction of road intersections from images in conflation processes based on texture characterization. Survey review, 43 (321), 212–225.]. However, in the updated version we have solved the problem of not-well-defined intersection points, resulting in a substantial increase in the number of intersection points employed for the final adjustment to align both products and in a reduction of the computation time. On the other hand, the positional uncertainty assessment of parcel boundary lines both before and after applying our alignment procedure between them is provided. With regard to the experimental results, in the case of Polish cadastral data this procedure allows for significant improvement in the alignment between imagery and cadastral parcels boundaries. Numéro de notice : A2019-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2017.1388959 date de publication en ligne : 20/10/2017 En ligne : https://doi.org/10.1080/00396265.2017.1388959 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92626 [article]

Motion priors based on goals hierarchies in pedestrian tracking applications / Francisco Madrigal in Machine Vision and Applications, vol 28 n° 3-4 (May 2017)  

Public [article]  inMachine Vision and Applications > vol 28 n° 3-4 (May 2017) . - pp 341 - 359 Titre : Motion priors based on goals hierarchies in pedestrian tracking applications Type de document : Article/Communication Auteurs : Francisco Madrigal, Auteur ; Jean-Bernard Hayet, Auteur Année de publication : 2017 Article en page(s) : pp 341 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] image vidéo [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] position [Termes descripteurs IGN] poursuite de cible [Termes descripteurs IGN] prévision [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] séquence d'images Résumé : (auteur) In this paper, the problem of automated scene understanding by tracking and predicting paths for multiple humans is tackled, with a new methodology using data from a single, fixed camera monitoring the environment. Our main idea is to build goal-oriented prior motion models that could drive both the tracking and path prediction algorithms, based on a coarse-to-fine modeling of the target goal. To implement this idea, we use a dataset of training video sequences with associated ground-truth trajectories and from which we extract hierarchically a set of key locations. These key locations may correspond to exit/entrance zones in the observed scene, or to crossroads where trajectories have often abrupt changes of direction. A simple heuristic allows us to make piecewise associations of the ground-truth trajectories to the key locations, and we use these data to learn one statistical motion model per key location, based on the variations of the trajectories in the training data and on a regularizing prior over the models spatial variations. We illustrate how to use these motion priors within an interacting multiple model scheme for target tracking and path prediction, and we finally evaluate this methodology with experiments on common datasets for tracking algorithms comparison. Numéro de notice : A2017-325 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-017-0832-8 date de publication en ligne : 15/03/2017 En ligne : http://doi.org/10.1007/s00138-017-0832-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85384 [article]

Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data / Luliang Tang 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 417 - 426 Titre : Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data Type de document : Article/Communication Auteurs : Luliang Tang, Auteur ; Zihan Kan, Auteur ; Xia Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 417 - 426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] coordonnées GPS [Termes descripteurs IGN] dimension temporelle [Termes descripteurs IGN] données massives [Termes descripteurs IGN] fréquence [Termes descripteurs IGN] itinéraire [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] taxi [Termes descripteurs IGN] temps de trajet [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] Wuhan (Chine) Résumé : (Auteur) Intersections are the critical parts where different traffic flows converge and change directions, forming “bottlenecks” and “clog points” in urban traffic. Intersection travel time is an important parameter for public route planning, traffic management, and engineering optimization. Based on low-frequency spatial-temporal Global Positioning System (GPS) trace data, this article presents a novel method for estimating intersection travel time. The proposed method first analyzes the different travel patterns of vehicles through an intersection, then determines the range of an intersection dynamically and reasonably, and obtains traffic flow speed and delay at the intersection under different travel patterns using a fuzzy fitting approach. Finally, the average intersection travel time is estimated from traffic flow speed and delay and intersection range in different travel patterns. Wuhan road network data and GPS trace data from taxicabs were tested in the experiments and the results show that the proposed method can improve the accuracy of travel time estimation at city intersections. Numéro de notice : A2016-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/15230406.2015.1130649 En ligne : https://doi.org/10.1080/15230406.2015.1130649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82029 [article]

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An automated system for image-to-vector georeferencing / Y. Li in Cartography and Geographic Information Science, vol 39 n° 4 (October 2012)  

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A road network selection process based on data enrichment and structure detection / Guillaume Touya in Transactions in GIS, vol 14 n° 5 (October 2010)  

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Affordance-based categorization of road network data using a grounded theory of channel networks / S. Scheider in International journal of geographical information science IJGIS, vol 24 n°7-8 (july 2010)  

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Generalization approaches for car navigation systems / A. Dogru (15/11/2009)

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Automatic and accurate extraction of road intersections from raster maps / Yao-Yi Chiang in Geoinformatica, vol 13 n° 2 (June 2009)

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3D information extraction from laser point clouds covering complex road junctions / Sander J. Oude Elberink in Photogrammetric record, vol 24 n° 125 (March - May 2009)

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Road junction extraction from high-resolution aerial imagery / M. Ravanbakhsh in Photogrammetric record, vol 23 n° 124 (December 2008 - February 2009)  

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Automatically and accurately conflating raster maps with orthoimagery / C.C. Chen in Geoinformatica, vol 12 n° 3 (September - November 2008)

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Etude sur la généralisation automatique des carrefours routiers complexes / Jérémy Renard (2007)

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A wavelet approach to road extraction from high spatial resolution remotely-sensed imagery / Qian Zhang in Geomatica, vol 58 n° 1 (March 2004)

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