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Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree / José Antonio Martin-Jimenez in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)
[article]
Titre : Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree Type de document : Article/Communication Auteurs : José Antonio Martin-Jimenez, Auteur ; Santiago Zazo, Auteur ; José Juan Arranz Justel, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 334 - 346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] accident de la route
[Termes IGN] arbre de décision
[Termes IGN] cohérence géométrique
[Termes IGN] données GNSS
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Espagne
[Termes IGN] extraction du réseau routier
[Termes IGN] indice de risque
[Termes IGN] lidar mobile
[Termes IGN] raisonnement
[Termes IGN] sécurité routière
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] triangulation de DelaunayRésumé : (auteur) Safe roads are a necessity for any society because of the high social costs of traffic accidents. This challenge is addressed by a novel methodology that allows us to evaluate road safety from Mobile LiDAR System data, taking advantage of the road alignment due to its influence on the accident rate. Automation is obtained through an inductive reasoning process based on a decision tree that provides a potential risk assessment. To achieve this, a 3D point cloud is classified by an iterative and incremental algorithm based on a 2.5D and 3D Delaunay triangulation, which apply different algorithms sequentially. Next, an automatic extraction process of road horizontal alignment parameters is developed to obtain geometric consistency indexes, based on a joint triple stability criterion. Likewise, this work aims to provide a powerful and effective preventive and/or predictive tool for road safety inspections. The proposed methodology was implemented on three stretches of Spanish roads, each with different traffic conditions that represent the most common road types. The developed methodology was successfully validated through as-built road projects, which were considered as “ground truth.” Numéro de notice : A2018-541 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.10.004 Date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91565
in ISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 334 - 346[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018131 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018133 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018132 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Scene classification based on multiscale convolutional neural network / Yanfei Liu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)
[article]
Titre : Scene classification based on multiscale convolutional neural network Type de document : Article/Communication Auteurs : Yanfei Liu, Auteur ; Yanfei Zhong, Auteur ; Qianqing Qin, Auteur Année de publication : 2018 Article en page(s) : pp 7109 - 7121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image multidimensionnelle
[Termes IGN] image satellite
[Termes IGN] mesure de similitude
[Termes IGN] modèle orienté objetRésumé : (auteur) With the large amount of high-spatial resolution images now available, scene classification aimed at obtaining high-level semantic concepts has drawn great attention. The convolutional neural networks (CNNs), which are typical deep learning methods, have widely been studied to automatically learn features for the images for scene classification. However, scene classification based on CNNs is still difficult due to the scale variation of the objects in remote sensing imagery. In this paper, a multiscale CNN (MCNN) framework is proposed to solve the problem. In MCNN, a network structure containing dual branches of a fixed-scale net (F-net) and a varied-scale net (V-net) is constructed and the parameters are shared by the F-net and V-net. The images and their rescaled images are fed into the F-net and V-net, respectively, allowing us to simultaneously train the shared network weights on multiscale images. Furthermore, to ensure that the features extracted from MCNN are scale invariant, a similarity measure layer is added to MCNN, which forces the two feature vectors extracted from the image and its corresponding rescaled image to be as close as possible in the training phase. To demonstrate the effectiveness of the proposed method, we compared the results obtained using three widely used remote sensing data sets: the UC Merced data set, the aerial image data set, and the google data set of SIRI-WHU. The results confirm that the proposed method performs significantly better than the other state-of-the-art scene classification methods. Numéro de notice : A2018-556 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2848473 Date de publication en ligne : 26/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2848473 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91660
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7109 - 7121[article]Super-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)
[article]
Titre : Super-resolution of Sentinel-2 images : Learning a globally applicable deep neural network Type de document : Article/Communication Auteurs : Charis Lanaras, Auteur ; José Bioucas-Dias, Auteur ; Silvano Galliani, Auteur ; Emmanuel P. Baltsavias, Auteur ; Konrad Schindler, Auteur Année de publication : 2018 Article en page(s) : pp 305 - 319 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] bande spectrale
[Termes IGN] échantillonnage de données
[Termes IGN] erreur moyenne quadratique
[Termes IGN] image à basse résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] pas d'échantillonnage au sol
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance – GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40 20 m, respectively 360 60 m GSD. In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images. We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining. In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics. It also delivers visually convincing results at the full 10 m GSD. Numéro de notice : A2018-540 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.018 Date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91554
in ISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 305 - 319[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018131 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018133 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018132 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Fictive motion extraction and classification / Ekaterina Egorova in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)
[article]
Titre : Fictive motion extraction and classification Type de document : Article/Communication Auteurs : Ekaterina Egorova, Auteur ; Ludovic Moncla , Auteur ; Mauro Gaio, Auteur ; Christophe Claramunt, Auteur ; Ross S. Purves, Auteur Année de publication : 2018 Article en page(s) : pp 2247 - 2271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Alpes
[Termes IGN] base de règles
[Termes IGN] corpus
[Termes IGN] extraction automatique
[Termes IGN] traitement du langage naturelRésumé : (Auteur) Fictive motion (e.g. ‘The highway runs along the coast’) is a pervasive phenomenon in language that can imply both a static and a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclopaedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse. Numéro de notice : A2018-524 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1498503 Date de publication en ligne : 30/07/2018 En ligne : https://doi.org/10.1080/13658816.2018.1498503 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91349
in International journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2247 - 2271[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018061 RAB Revue Centre de documentation En réserve L003 Disponible Formal representation of qualitative direction / Christian Freksa in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)
[article]
Titre : Formal representation of qualitative direction Type de document : Article/Communication Auteurs : Christian Freksa, Auteur ; Jasper van de Ven, Auteur ; Diedrich Wolter, Auteur Année de publication : 2018 Article en page(s) : pp 2514 - 2534 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] direction
[Termes IGN] raisonnement spatial
[Termes IGN] représentation cartographique
[Termes IGN] représentation spatiale
[Termes IGN] traitement de données localiséesRésumé : (Auteur) This paper reviews formal approaches to representing spatial knowledge about qualitative direction. Unlike geometric direction information, qualitative information does not employ numerical values but relies on comparison. The qualitative approach is often regarded as suitable for capturing commonsense concepts and thus is relevant to human-centered interfaces for spatial information systems. To establish a context for the work on qualitative direction, we preset a brief history of the development of qualitative temporal and spatial representations from different scientific perspectives. We identify main focal areas of these representations of spatial direction and propose a taxonomy. In the light of more than three decades of fruitful research, we obtain a map of formal representations that reveal interrelationships between different research strands in the field. Numéro de notice : A2018-528 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1420794 Date de publication en ligne : 25/01/2018 En ligne : https://doi.org/10.1080/13658816.2017.1420794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91366
in International journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2514 - 2534[article]Réservation
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