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Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)  

Public Titre : Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments Type de document : Article/Communication Auteurs : Yuhao Jiang, Auteur ; María-Jesús Lobo , Auteur ; Sidonie Christophe , Auteur ; Christophe Jouffrais, Auteur Editeur : Göttingen : Copernicus publications Année de publication : 2023 Collection : AGILE GIScience Series num. 4 Conférence : AGILE 2023, 26th international AGILE Conference on Geographic Information Science, Spatial data for design 13/06/2023 16/06/2023 Delft Pays-Bas OA Proceedings Importance : n° 29 ; 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] carrefour [Termes IGN] carte sur mesure [Termes IGN] carte tactile [Termes IGN] chaîne de traitement [Termes IGN] OpenStreetMap [Termes IGN] personne malvoyante Index. décimale : 39.00 Cartographie - généralités - Cartologie Résumé : (auteur) Street intersections are very challenging for people with visual impairments. Manually produced tactile maps are an important support in teaching and assisting independent journeys as they can be customized to serve the visually impaired audience with diverse tactile reading and mobility skills in different use scenarios. But the manual map production involves a huge workload that makes the maps less accessible. This paper explores the possibility of semi-automatically producing customizable tactile maps for street intersections. It presents a parameterized semi-automated pipeline based on OSM data that allows the maps to be customized in size, map features, geometry processing choices, and symbolizations. It produces street intersection maps in two scales of three sizes, with different levels of details and styles. Numéro de notice : C2023-013 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-4-29-2023 Date de publication en ligne : 06/06/2023 En ligne : https://doi.org/10.5194/agile-giss-4-29-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103307

Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 735 - 754 Titre : Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction Type de document : Article/Communication Auteurs : Jincai Huang, Auteur ; Yunfei Zhang, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 735 - 754 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Vedettes matières IGN] Géomatique [Termes IGN] base de données routières [Termes IGN] carrefour [Termes IGN] carte routière [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] données routières [Termes IGN] extraction de traits caractéristiques [Termes IGN] information sémantique [Termes IGN] intégration de données [Termes IGN] navigation automobile [Termes IGN] vitesse [Termes IGN] Wuhan (Chine) Résumé : (auteur) The road map is a fundamental part of a spatial data infrastructure (SDI), and is widely applied in navigation, smart transportation, and mobile location services. Recently, with the ubiquity of positioning devices, crowdsourced trajectories have become a significant data resource for road map construction and updating. However, existing trajectory-based methods mainly place emphasis on extracting road geometry features and may ignore continuous updating of road semantic information. Hence, we propose a divide-and-conquer method to construct a spatial-semantic road map by incorporating multiple data sources (e.g., crowdsourced trajectories and geo-tagged data). The proposed method divides road map construction into two sub-tasks, road structure reconstruction and road attributes inference. The road structure reconstruction process starts to partition raw trajectory data into different cliques of roadways and road intersections, and then extracts various targeted road structures by analyzing the turning modes in different trajectory cliques. The road attributes inference process aims to infer three pieces of crucial semantic information about road speeds, turning rules, and road names from crowdsourced trajectories and geo-tagged data. The case studies in Wuhan were examined to illustrate that the proposed method can construct a routable road map with enhanced geometric structures and rich semantic information, providing a beneficial data solution for car navigation and SDI update. Numéro de notice : A2022-364 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12879 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1111/tgis.12879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100583 [article]

Schematizing crossroads from abstract textual descriptions / Jean-Marie Favreau (2022)  

Public Titre : Schematizing crossroads from abstract textual descriptions Type de document : Article/Communication Auteurs : Jean-Marie Favreau, Auteur ; Guillaume Touya , Auteur ; Jérémy Kalsron, Auteur Editeur : Bonn : Université de Bonn Année de publication : 2022 Projets : ACTIVmap / Favreau, Jean-Marie Conférence : CompCarto 2022, 1st workshop on Computational Cartography 19/05/2022 20/05/2022 Bonn Allemagne programme Importance : 3 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carrefour [Termes IGN] carte tactile [Termes IGN] cartogramme [Termes IGN] exploration de texte [Vedettes matières IGN] Cartologie Résumé : (auteur) [début] The use of cartographic representations among people with visual impairments (PVI) is often limited by the lack of available materials. However, two uses have been identified: diagrams made with sticks magnetised to a metal plate (Figure 1) are used by Orientation and Mobility instructors as a discussion aid around complex areas (typically intersections), and more accurate maps made by transcribing adapters are sometimes produced for regular use. While classical variations of the generalisation and stylisation approaches allow for the production of fairly accurate maps [JLCJ21], for example from OpenStreetMap data (figure 2), there are currently no known approaches to producing a more schematic representation, in the manner of the locomotion instructors’ magnets. Numéro de notice : C2022-013 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Date de publication en ligne : 24/05/2022 En ligne : https://hal.science/hal-03677334/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100747

Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° 1 (January 2022) . - pp 32 - 49 Titre : Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images Type de document : Article/Communication Auteurs : Xiao Li, Auteur ; Huan Ning, Auteur ; Xiao Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 32 - 49 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] carrefour [Termes IGN] cartographie urbaine [Termes IGN] couche thématique [Termes IGN] exploration d'images [Termes IGN] feu de circulation [Termes IGN] image Streetview [Termes IGN] Mapillary [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] signalisation routière Résumé : (auteur) Auditing and mapping traffic infrastructure is a crucial task in urban management. For example, signalized intersections play an essential role in transportation management; however, effectively identifying these intersections remains unsolved. Traditionally, signalized intersection data are manually collected through field audits or checking street view images (SVIs), which is time-consuming and labor-intensive. This study proposes an effective protocol to identify signalized intersections using road networks and SVIs. First, we propose a six-step geoprocessing model to generate an intersection feature layer from road networks. Second, we utilize up to three nearest SVIs to capture streetscapes at each intersection. Then, a deep learning-based image segmentation model is adopted to recognize traffic light-related pixels from each SVI. Last, we design a post-processing step to generate new features characterizing SVIs’ segmentation results at each intersection and build a decision tree model to determine the traffic control type. Results demonstrate that the proposed protocol can effectively identify signalized intersections with an overall accuracy of 97.05%. It also proves the effectiveness of SVIs for auditing urban infrastructures. This study can directly benefit transportation agencies by providing a ready-to-use smart audit and mapping solution for large-scale identification and mapping of signalized intersections. Numéro de notice : A2022-017 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1080/15230406.2021.1992299 Date de publication en ligne : 16/11/2021 En ligne : https://doi.org/10.1080/15230406.2021.1992299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99148 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2022011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Road-network-based fast geolocalization / Yongfei Li in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 6065 - 6076 Titre : Road-network-based fast geolocalization Type de document : Article/Communication Auteurs : Yongfei Li, Auteur ; Dongfang Yang, Auteur ; Shisheng Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6065 - 6076 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carrefour [Termes IGN] carte routière [Termes IGN] cohérence géométrique [Termes IGN] géolocalisation [Termes IGN] image aérienne [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] superposition d'images [Termes IGN] transformation homographique [Termes IGN] zone urbaine Résumé : (auteur) In this article, a road-network-based geolocalization method is proposed. We match roads in the onboard images to the reference road vector map, and realize successful localization over areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under the homography transformation and solved under the hypothesize-and-test framework. To tackle the point cloud registration problem, a global projective-invariant feature is proposed, which consists of two road intersections augmented with their tangents. In addition, we propose the necessary conditions for the features to match. This can reduce the candidate matching features, thus accelerating the search to a great extent. These matching candidates are first “filtered” with the model consistency check in parameter space and then tested with similarity metrics to identify the correct transformation. The experiments show that our method can localize an aerial image over an area larger than 1000 km 2 within several seconds on a single CPU. Our code can be found at: https://github.com/FlyAlCode/RCLGeolocalization-2.0 . Numéro de notice : A2021-532 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3011034 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3011034 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97989 [article]

Le carrefour dont vous êtes le héros / Jérémy Kalsron (2021) 

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Le carrefour dont vous êtes le héros : description de carrefours pour les personnes déficientes visuelles / Jérémy Kalsron (2021)    

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Mapping road crossings for visually impaired people / Yuhao Jiang (2021)  

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

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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)  

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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)  

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

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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)  

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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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