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Attributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 1 (January 2022) . - pp 31 - 54 Titre : Attributing pedestrian networks with semantic information based on multi-source spatial data Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Kathleen Stewart, Auteur ; Mengyuan Fang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 31 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] données multisources [Termes IGN] étiquetage sémantique [Termes IGN] extraction de données [Termes IGN] itinéraire piétionnier [Termes IGN] navigation pédestre [Termes IGN] ondelette [Termes IGN] réseau routier [Termes IGN] utilisation du sol [Termes IGN] Wuhan (Chine) Résumé : (auteur) The lack of associating pedestrian networks, i.e. the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map. Numéro de notice : A2022-083 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1902530 En ligne : https://doi.org/10.1080/13658816.2021.1902530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99480 [article]

BasqueRoads: a benchmark for road network selection / Guillaume Touya (2022)  

Public Titre : BasqueRoads: a benchmark for road network selection Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Azelle Courtial , Auteur Editeur : Paris, ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 4 Projets : LostInZoom / Touya, Guillaume Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Note générale : bibliographie This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101003012). Langues : Anglais (eng) Descripteur : [Termes IGN] objet géographique [Termes IGN] réseau routier [Termes IGN] simplification de contour [Termes IGN] test de performance [Vedettes matières IGN] Généralisation Résumé : (auteur) [début] Road network selection is one of the major issues of map generalisation, as new papers are proposed every year since the first attempts of automation in the 1990’s (Thomson & Richardson, 1995). New methods are regularly proposed because selecting roads for maps at smaller scales is a complex problem. Roads are at the same time present in maps to enable car navigation tasks, and because they are structuring elements that reveal the nature of the landscape (urban, rural, mountainous…). So road selection is not only about retaining the most important roads of the network, but the preservation of topology and connectivity is essential, as well as the preservation, or the typification of road patterns (e.g. a ring road), and the preservation of local density differences (between urban and rural areas for instance). It is rare to see comparisons of road selection techniques in the literature, because of the lack of open source in map generalisation, but also because of the lack of a common dataset to benchmark these techniques; new propositions on road selection are most of the time tied to their own dataset and use case. This is why we think that this BasqueRoads dataset could be useful to advance on this topic of road network selection. Numéro de notice : C2021-066 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-4-5-2022 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.5194/ica-abs-4-5-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99536

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]

Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)  

Public [article]  inTransactions in GIS > vol 25 n° 6 (December 2021) . - pp 3025 - 3047 Titre : Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models Type de document : Article/Communication Auteurs : Tong Zhang, Auteur ; Jing Li, Auteur Année de publication : 2021 Article en page(s) : pp 3025 - 3047 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] Colorado (Etats-Unis) [Termes IGN] données spatiotemporelles [Termes IGN] épidémie [Termes IGN] maladie virale [Termes IGN] mobilité territoriale [Termes IGN] modèle de simulation [Termes IGN] modélisation spatio-temporelle [Termes IGN] outil d'aide à la décision [Termes IGN] quartier [Termes IGN] réseau de transport [Termes IGN] risque sanitaire [Termes IGN] surveillance sanitaire Résumé : (Auteur) In order to find useful intervention strategies for the novel coronavirus (COVID-19), it is vital to understand how the disease spreads. In this study, we address the modeling of COVID-19 spread across space and time, which facilitates understanding of the pandemic. We propose a hybrid data-driven learning approach to capture the mobility-related spreading mechanism of infectious diseases, utilizing multi-sourced mobility and attributed data. This study develops a visual analytic approach that identifies and depicts the strength of the transmission pathways of COVID-19 between areal units by integrating data-driven deep learning and compartmental epidemic models, thereby engaging stakeholders (e.g., public health officials, managers from transportation agencies) to make informed intervention decisions and enable public messaging. A case study in the state of Colorado, USA was performed to demonstrate the applicability of the proposed transmission modeling approach in understanding the spatio-temporal spread of COVID-19 at the neighborhood level. Transmission path maps are presented and analyzed, demonstrating their utility in evaluating the effects of mitigation measures. In addition, integrated embeddings also support daily prediction of infected cases and role analysis of each area unit during the transmission of the virus. Numéro de notice : A2021-932 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12803 Date de publication en ligne : 16/07/2021 En ligne : https://doi.org/10.1111/tgis.12803 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99447 [article]

Photogrammetric 3D mobile mapping of rail tracks / Philipp Glira in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 352 - 362 Titre : Photogrammetric 3D mobile mapping of rail tracks Type de document : Article/Communication Auteurs : Philipp Glira, Auteur ; K. ÖlsböckK., Auteur ; T. Kadiofsky, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 352 - 362 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Autriche [Termes IGN] axe médian [Termes IGN] compensation par faisceaux [Termes IGN] compensation par moindres carrés [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] modèle numérique de surface [Termes IGN] OpenStreetMap [Termes IGN] orthoimage [Termes IGN] point d'appui [Termes IGN] reconstruction 3D [Termes IGN] réseau ferroviaire [Termes IGN] semis de points [Termes IGN] voie ferrée Résumé : (auteur) Recent developments in the field of rail vehicles increased the demand for accurate and up-to-date 3D maps of rail track networks. Collision avoidance systems, semi-automated, or fully autonomous rail vehicles strongly benefit from such high quality maps. In this work, we present a fully automatic, photogrammetric method for the 3D reconstruction of rail track segments. More specifically, the center line of the rail track is reconstructed as a georeferenced and continuous 3D cubic spline. The main data inputs are collected while driving the rail vehicle along the segment: (a) images from a front-looking camera and (b) observations from a low-cost GNSS receiver. Optional data inputs can be used to increase the reconstruction accuracy, namely (c) an a priori rail track (e.g. from OpenStreetMap), (d) a digital height model (DHM), and (e) ground control points (GCPs). The rail track is estimated in post processing (offline) by a weighted least squares adjustment (LSA). The core of the LSA is the bundle adjustment of images. It is extended by additional geometric constraints which exploit the geometric relations between the rail track, the rail vehicle, and the camera trajectory. As a consequence, in contrast to many related methods, the rails need not to be visible in the images to map the rail track. We applied the method to reconstruct a 13 km long tram line in Vienna (Austria). We found that the local geometry of the track can be well reconstructed from the image sequence. However, if the low-cost GNSS receiver is used as single georeferencing source, the track shows a strong drift behavior. This drift can significantly be minimized over the entire track if the above mentioned optional data inputs are used. Numéro de notice : A2022-034 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2021.09.006 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.09.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99327 [article]

Semi-automatic extraction of rural roads under the constraint of combined geometric and texture features / Hai Tan in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)  

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Spatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)  

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Analyzing routes in Ottoman Greater Syria using historical GIS: The 1849 Saida map / Motti Zohar in Transactions in GIS, vol 25 n° 5 (October 2021)  

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Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)  

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A multiagent systems with Petri Net approach for simulation of urban traffic networks / Mauricio Flores Geronimo in Computers, Environment and Urban Systems, vol 89 (September 2021)  

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A cellular-automata model for assessing the sensitivity of the street network to natural terrain / Jeeno Soa George in Annals of GIS, vol 27 n° 3 (July 2021)  

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

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A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Fast weakly supervised detection of railway-related infrastructures in lidar acquisitions / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)  

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Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image / Genki Takahashi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)  

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