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Auteur Sagi Dalyot |
Documents disponibles écrits par cet auteur (4)
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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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Titre : Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians Type de document : Article/Communication Auteurs : Achituv Cohen, Auteur ; Sagi Dalyot, Auteur Année de publication : 2020 Article en page(s) : pp 1264-1279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données localisées des bénévoles
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion des itinéraires
[Termes IGN] handicap
[Termes IGN] itinéraire piétionnier
[Termes IGN] modèle de simulation
[Termes IGN] navigation pédestre
[Termes IGN] OpenStreetMap
[Termes IGN] personne non-voyante
[Termes IGN] point d'intérêt
[Termes IGN] trafic routierRésumé : (Auteur) Navigation and orientation while walking in urban spaces pose serious challenges for blind pedestrians, sometimes even on a daily basis. Research shows the practicability of computerized weighted network route planning algorithms based on OpenStreetMap mapping data for calculating customized routes for blind pedestrians. While data about pedestrians and vehicle traffic flow at different times throughout the day influence the route choices of blind pedestrians, such data do not exist in OpenStreetMap. Quantifying the correlation between spatial structure and traffic flow could be used to fill this gap. As such, we investigated machine‐learning methods to develop a computerized model for predicting pedestrian traffic flow levels, with the objective of enriching the OpenStreetMap database. This article presents prediction results by implementing six machine‐learning algorithms based on parameters relating to the geometrical and topological configuration of streets in OpenStreetMap, as well as points‐of‐interest such as public transportation and shops. The Random Forest algorithm produced the best results, whereby 95% of the testing data were successfully predicted. These results indicate that machine‐learning algorithms can accurately generate necessary temporal data, which when combined with the available crowdsourced open mapping data could augment the reliability of route planning algorithms for blind pedestrians. Numéro de notice : A2020-700 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12674 Date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1111/tgis.12674 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96210
in Transactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1264-1279[article]Extracting spatial patterns in bicycle routes from crowdsourced data / Jody Sultan in Transactions in GIS, vol 21 n° 6 (December 2017)
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Titre : Extracting spatial patterns in bicycle routes from crowdsourced data Type de document : Article/Communication Auteurs : Jody Sultan, Auteur ; Gev Ben‐Haim, Auteur ; Jan‐Henrik Haunert, Auteur ; Sagi Dalyot, Auteur Année de publication : 2017 Article en page(s) : pp 1321 - 1340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Amsterdam (Pays-Bas)
[Termes IGN] cycliste
[Termes IGN] données localisées des bénévoles
[Termes IGN] extraction de modèle
[Termes IGN] trace GPS
[Termes IGN] trajet (mobilité)Résumé : (auteur) Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning. Numéro de notice : A2017-838 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12280 Date de publication en ligne : 06/06/2017 En ligne : https://doi.org/10.1111/tgis.12280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89374
in Transactions in GIS > vol 21 n° 6 (December 2017) . - pp 1321 - 1340[article]Towards the production of digital terrain models from volunteered GPS trajectories / I. Massad in Survey review, vol 47 n° 344 (September 2015)
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Titre : Towards the production of digital terrain models from volunteered GPS trajectories Type de document : Article/Communication Auteurs : I. Massad, Auteur ; Sagi Dalyot, Auteur Année de publication : 2015 Conférence : FIG 2014, Commission 3 Annual Workshop, Geospatial Crowdsourcing and VGI: Establishment of SDI & SIM 04/11/2014 07/11/2014 Bologne Italie Article en page(s) : pp 325 - 332 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées 2D
[Termes IGN] données localisées des bénévoles
[Termes IGN] modèle numérique de terrain
[Termes IGN] OpenStreetMap
[Termes IGN] positionnement par GPS
[Termes IGN] précision des données
[Termes IGN] production participative
[Termes IGN] qualité des données
[Termes IGN] web mapping
[Termes IGN] WikimapiaRésumé : (auteur) There currently exists a wide variety of online resources providing mapping infrastructures and geographic information. Most web-based map services, such as Google Maps, Yahoo! Maps and Bing Maps, are mostly based on data that is collected by authoritative mapping agencies. Alternatively, some relatively new web-map services, such as OpenStreetMap (OSM) and Wikimapia, are mostly based on volunteered data collected by the public (e.g. crowdsourced mapping). Although such volunteered-based map services platforms show an increasing planimetric (2D) accuracy, completeness and update-rate of their mapping infrastructure, surprisingly enough, there is a lack of comparable data and accuracy measures in respect to the third dimension, i.e. height; more specifically, the topographic representation that is based on the volunteered collected data. Most of these web services still rely on existing open-source authoritative topographic infrastructures, and not on data collected by the volunteers. Moreover, topographic information that is open to the public and is free to use, e.g. advanced spaceborne thermal emission and reflection radiometer and shuttle radar topography mission, is regularly available with relatively low height accuracy (not better than 5 m) and low planimetric resolution (over 30 m). Volunteered data, on the other hand, collected by individuals that are situated ‘all over’ the globe can offer with new capabilities and data characteristics having potentially higher qualities. This research proposes to examine the feasibility of using crowdsourced volunteered geographic information working paradigm for the task of producing a reliable digital terrain model (DTM) infrastructure for general use. This is achieved by collecting GPS observations that are available from VG data sources, while applying a 2D Kalman filter-based algorithm, aimed at reducing noise and ambiguities. This paper presents this methodology, with preliminary analysis results achieved by this implementation, showing the feasibility of this working methodology, having good results and accuracy of the DTM generated. Numéro de notice : A2015-916 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1179/1752270615Y.0000000010 En ligne : https://doi.org/10.1179/1752270615Y.0000000010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79704
in Survey review > vol 47 n° 344 (September 2015) . - pp 325 - 332[article]Landslide morphology analysis model based on LiDAR and topographic dataset comparison / Sagi Dalyot in SaLIS Surveying and land information science, vol 68 n° 3 (September 2008)
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Titre : Landslide morphology analysis model based on LiDAR and topographic dataset comparison Type de document : Article/Communication Auteurs : Sagi Dalyot, Auteur ; E. Keinan, Auteur ; Y. Doytsher, Auteur Année de publication : 2008 Article en page(s) : pp 155 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] croissance urbaine
[Termes IGN] données lidar
[Termes IGN] données localisées numériques
[Termes IGN] effondrement de terrain
[Termes IGN] géomorphologie locale
[Termes IGN] modèle numérique de surface
[Termes IGN] surveillance géologique
[Termes IGN] zone urbaine denseRésumé : (Auteur) The natural trend of megacities is to expand from their centers to outlying areas in order to accommodate rapid population growth. Urban areas situated near steep, mountainous terrain or water sources may suffer from landslides, which are one of the most common natural disasters. This paper outlines a new approach in which airborne laser altimetry is used to automatically identify and model landslide occurrences within the acquired data. Dense and accurate light detection and ranging (LiDAR) topographic observations are modeled and compared to an existing digital elevation model (DEM) of the area acquired at an earlier stage. The standard direct superimposition comparison mechanism, which ignores the datasets' inherent interrelations, usually results in an incorrect modeling outcome. A new modeling concept is proposed that initially monitors the inherent global incongruities by georeferencing the two datasets. These incongruities are then utilized in a novel geospatial matching procedure that makes it possible to achieve a precise and correct local modeling mechanism. The hierarchical comparison concept presented accurate results in automatic identification of landslide phenomena, making it possible to precisely assess their morphology. Copyright SaLIS Numéro de notice : A2008-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83666
in SaLIS Surveying and land information science > vol 68 n° 3 (September 2008) . - pp 155 - 170[article]Exemplaires(1)
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