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NRand‐K : Minimizing the impact of location obfuscation in spatial analysis / Mayra Zurbaran in Transactions in GIS, vol 22 n° 5 (October 2018)
[article]
Titre : NRand‐K : Minimizing the impact of location obfuscation in spatial analysis Type de document : Article/Communication Auteurs : Mayra Zurbaran, Auteur ; Pedro Wightman, Auteur ; Maria Antonia Brovelli, Auteur ; Daniele Oxoli, Auteur Année de publication : 2018 Article en page(s) : pp 1257 - 1274 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] anonymisation
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] exploration de données géographiques
[Termes IGN] protection de la vie privéeRésumé : (Auteur) Location privacy, or geoprivacy, is critical to secure users’ privacy in context‐aware applications. Location‐based services pose privacy risks for users, due to the inferences that could be made about them from their location information and the potential misuse of this data by service providers or third‐party companies. A common solution is to apply masking or location obfuscation, which degrades location information quality while keeping a geographic coordinate structure. However, there is a trade‐off between privacy, quality of service, and quality of information, the last one being a valuable asset for companies. NRand is a location privacy mechanism with obfuscation capabilities and resistance against filtering attacks. In order to minimize the impact on location information quality, NRand‐K has been introduced. This algorithm is designed for use when releasing location information to third parties or as open data with privacy concerns. To assess the impact of location obfuscation on exploratory spatial data analysis (ESDA), a comparison is performed between obfuscated data with NRand, NRand‐K, and unaltered data. For the experiments, geolocated tweets collected during the Central Italy 2016 earthquake are used. Results show that NRand‐K reduces the impact on ESDA, where inferences were similar to those obtained with the unaltered data. Numéro de notice : A2018-573 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12462 Date de publication en ligne : 11/10/2018 En ligne : https://doi.org/10.1111/tgis.12462 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92298
in Transactions in GIS > vol 22 n° 5 (October 2018) . - pp 1257 - 1274[article]Augmented reality meets computer vision : efficient data generation for urban driving scenes / Hassan Abu Alhaija in International journal of computer vision, vol 126 n° 9 (September 2018)
[article]
Titre : Augmented reality meets computer vision : efficient data generation for urban driving scenes Type de document : Article/Communication Auteurs : Hassan Abu Alhaija, Auteur ; Siva Karthik Mustikovela, Auteur ; Lars Mescheder, Auteur ; Andreas Geiger, Auteur ; Carsten Rother, Auteur Année de publication : 2018 Article en page(s) : pp 961 - 972 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage automatique
[Termes IGN] détection d'objet
[Termes IGN] réalité augmentée
[Termes IGN] scène urbaine
[Termes IGN] vision par ordinateurRésumé : (Auteur) The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D models of the target object category. Leveraging our approach, we introduce a novel dataset of augmented urban driving scenes with 360 degree images that are used as environment maps to create realistic lighting and reflections on rendered objects. We analyze the significance of realistic object placement by comparing manual placement by humans to automatic methods based on semantic scene analysis. This allows us to create composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. Through an extensive set of experiments, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of the proposed approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenarios. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on the Cityscapes dataset. Our experiments demonstrate that the models trained on augmented imagery generalize better than those trained on fully synthetic data or models trained on limited amounts of annotated real data. Numéro de notice : A2018-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1070-x Date de publication en ligne : 07/03/2018 En ligne : https://doi.org/10.1007/s11263-018-1070-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90900
in International journal of computer vision > vol 126 n° 9 (September 2018) . - pp 961 - 972[article]Configurable 3D scene synthesis and 2D image rendering with per-pixel ground truth using stochastic grammars / Chenfanfu Jiang in International journal of computer vision, vol 126 n° 9 (September 2018)
[article]
Titre : Configurable 3D scene synthesis and 2D image rendering with per-pixel ground truth using stochastic grammars Type de document : Article/Communication Auteurs : Chenfanfu Jiang, Auteur ; Shuyao Qi, Auteur ; Yixin Zhu, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 920 - 941 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] architecture pipeline (processeur)
[Termes IGN] compréhension de l'image
[Termes IGN] image RVB
[Termes IGN] rendu réaliste
[Termes IGN] scène intérieure
[Termes IGN] segmentation sémantique
[Termes IGN] synthèse d'imageRésumé : (Auteur) We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms. In particular, we devise a learning-based pipeline of algorithms capable of automatically generating and rendering a potentially infinite variety of indoor scenes by using a stochastic grammar, represented as an attributed Spatial And-Or Graph, in conjunction with state-of-the-art physics-based rendering. Our pipeline is capable of synthesizing scene layouts with high diversity, and it is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. It renders photorealistic RGB images of the generated scenes while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity, and material information (detailed to object parts), as well as environments (e.g., illuminations and camera viewpoints). We demonstrate the value of our synthesized dataset, by improving performance in certain machine-learning-based scene understanding tasks—depth and surface normal prediction, semantic segmentation, reconstruction, etc.—and by providing benchmarks for and diagnostics of trained models by modifying object attributes and scene properties in a controllable manner. Numéro de notice : A2018-416 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1103-5 Date de publication en ligne : 30/06/2018 En ligne : https://doi.org/10.1007/s11263-018-1103-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90899
in International journal of computer vision > vol 126 n° 9 (September 2018) . - pp 920 - 941[article]Descriptive measures of point distributions summarized with respect to spatial scale in visualization / Yukio Sadahiro in Cartographica, vol 53 n° 3 (Fall 2018)
[article]
Titre : Descriptive measures of point distributions summarized with respect to spatial scale in visualization Type de document : Article/Communication Auteurs : Yukio Sadahiro, Auteur Année de publication : 2018 Article en page(s) : pp 185 - 202 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] carte de répartition par points
[Termes IGN] carte thématique
[Termes IGN] méthode fondée sur le noyau
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Visual exploration plays a critical role in point pattern analysis. It permits analysts to grasp a wide variety of spatial patterns in point distributions that are not necessarily detectable by mathematical and statistical methods. Since spatial patterns are scale-dependent, grid and kernel density maps are effective in analysis that can visualize point distributions at various scales from small to large. Visual exploration of these maps, however, takes a considerable amount of time even if the maps are generated automatically in GIS software. In addition, visual exploration inevitably becomes subjective and unstable when treating numerous maps simultaneously. It is not easy to evaluate and memorize spatial patterns in maps in a consistent and objective way. To resolve the problem, this article proposes new quantitative measures summarizing the characteristics of point distributions. The measures can be visualized as maps that help analysts to capture the overall spatial pattern of point distributions efficiently. Numerical experiments and applications to real data analysis are performed to test the validity of the proposed measures. The results reveal the effectiveness of the measures, as well as their shortcomings, to be resolved in future research. Numéro de notice : A2018-482 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart.53.3.2017-0023 Date de publication en ligne : 01/10/2018 En ligne : https://doi.org/10.3138/cart.53.3.2017-0023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91207
in Cartographica > vol 53 n° 3 (Fall 2018) . - pp 185 - 202[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2018031 SL Revue Centre de documentation Revues en salle Disponible Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)
[article]
Titre : Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; R. Prasad, Auteur ; D. K. Gupta, Auteur ; V. N. Mishra, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 942 - 956 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance végétale
[Termes IGN] cultures
[Termes IGN] données polarimétriques
[Termes IGN] estimation statistique
[Termes IGN] hiver
[Termes IGN] image Sentinel-SAR
[Termes IGN] Leaf Area Index
[Termes IGN] régression
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste marge
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m2 area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R2 = 0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R2 = 0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms. Numéro de notice : A2018-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1316781 Date de publication en ligne : 18/04/2017 En ligne : https://doi.org/10.1080/10106049.2017.1316781 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90551
in Geocarto international > vol 33 n° 9 (September 2018) . - pp 942 - 956[article]Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)PermalinkImage-based synthesis for deep 3D human pose estimation / Grégory Rogez in International journal of computer vision, vol 126 n° 9 (September 2018)PermalinkIntegrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation of urban emergency shelters / Jia Yu in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkSpatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)Permalink3-D deep learning approach for remote sensing image classification / Amina Ben Hamida in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)PermalinkAdaptive correlation filters with long-term and short-term memory for object tracking / Chao Ma in International journal of computer vision, vol 126 n° 8 (August 2018)PermalinkA deep learning approach to DTM extraction from imagery using rule-based training labels / Caroline M. Gevaert in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkA deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)PermalinkImproving the quality of cartographic colour reproduction using the self-organizing map method / Mingguang Wu in Cartographic journal (the), Vol 55 n° 3 (August 2018)PermalinkSpectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields / Elham Kordi Ghasrodashti in Geocarto international, vol 33 n° 8 (August 2018)Permalink