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Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)
[article]
Titre : Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Xinyue Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1317 - 1342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] distorsion d'image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] hauteur du bâti
[Termes IGN] image Streetview
[Termes IGN] lever tachéométrique
[Termes IGN] modèle numérique de surface
[Termes IGN] porteRésumé : (auteur) Street view imagery such as Google Street View is widely used in people’s daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors’ elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas. Numéro de notice : A2022-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1981334 Date de publication en ligne : 06/10/2021 En ligne : https://doi.org/10.1080/13658816.2021.1981334 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100970
in International journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1317 - 1342[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022071 SL Revue Centre de documentation Revues en salle Disponible Accuracy evaluation for automated optical indoor positioning using a camera phone / V. Händler in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement, vol 137 n° 2 (01/03/2012)
[article]
Titre : Accuracy evaluation for automated optical indoor positioning using a camera phone Type de document : Article/Communication Auteurs : V. Händler, Auteur ; V. Willert, Auteur Année de publication : 2012 Article en page(s) : pp 114 - 122 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse comparative
[Termes IGN] classification
[Termes IGN] estimation de précision
[Termes IGN] GNSS assisté pour la navigation
[Termes IGN] image terrestre
[Termes IGN] porte
[Termes IGN] positionnement en intérieur
[Termes IGN] relèvement
[Termes IGN] système optique
[Termes IGN] téléphone intelligentRésumé : (Auteur) In this paper, we focus on the accuracy of optical indoor positioning and the design of an automated and mobile positioning system based on pictures taken by a cell phone camera. We restrict ourselves to automated relative pose estimation given only one image including the projection of an object with four reference points with known image and world coordinates. To infer the relative pose from the image coordinates of the reference points, we first have to detect and classify an object, afterwards localize the image coordinates, and finally apply spatial resection. We show, that if an object is correctly classified, then the quality of the positioning heavily depends on the accuracy of the localization of the image coordinates and on the choice of the spatial resection algorithm. To this end, we compare three different spatial resection algorithms and present two combinations of object classification and image coordinate localization techniques using doors as known objects. Statistical evaluations are provided to judge the different classification methods in terms of robustness and to present the accuracy of the image coordinate localization techniques. Numéro de notice : A2012-267 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31713
in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement > vol 137 n° 2 (01/03/2012) . - pp 114 - 122[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 199-2012021 RSREV Revue Centre de documentation Revues en salle Disponible