Centre de documentation
scientifique

Robotics, AI, and humanity / Joachim Von Braun (2020)  

Public Titre : Robotics, AI, and humanity : Science, ethics, and policy Type de document : Monographie Auteurs : Joachim Von Braun, Éditeur scientifique ; Margaret S. Archer, Éditeur scientifique ; Gregory M. Reichberg, Éditeur scientifique ; et al., Auteur Editeur : Springer Nature Année de publication : 2020 ISBN/ISSN/EAN : 978-3-030-54173-6 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] cerveau [Termes IGN] classification à base de connaissances [Termes IGN] cognition [Termes IGN] conscience [Termes IGN] émotion [Termes IGN] éthique [Termes IGN] finance [Termes IGN] formation [Termes IGN] navigation autonome [Termes IGN] robotique Résumé : (éditeur) This open access book examines recent advances in how artificial intelligence (AI) and robotics have elicited widespread debate over their benefits and drawbacks for humanity. The emergent technologies have for instance implications within medicine and health care, employment, transport, manufacturing, agriculture, and armed conflict. While there has been considerable attention devoted to robotics/AI applications in each of these domains, a fuller picture of their connections and the possible consequences for our shared humanity seems needed. This volume covers multidisciplinary research, examines current research frontiers in AI/robotics and likely impacts on societal well-being, human – robot relationships, as well as the opportunities and risks for sustainable development and peace. The attendant ethical and religious dimensions of these technologies are addressed and implications for regulatory policies on the use and future development of AI/robotics technologies are elaborated. Note de contenu : I- Foundational issues in AI and robotics II- AI and robotics changing the future of society: Work, farming, services, and poverty III- Robotics, AI, and militarized conflict IV- AI/robot–human interactions: Regulatory and ethical implications Numéro de notice : 28612 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-030-54173-6 En ligne : https://doi.org/10.1007/978-3-030-54173-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99478

A higher order conditional random field model for simultaneous classification of land cover and land use / Lena Albert in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 63 - 80 Titre : A higher order conditional random field model for simultaneous classification of land cover and land use Type de document : Article/Communication Auteurs : Lena Albert, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2017 Article en page(s) : pp 63 - 80 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] champ aléatoire conditionnel [Termes IGN] classification à base de connaissances [Termes IGN] classification automatique [Termes IGN] classification pixellaire [Termes IGN] image aérienne [Termes IGN] inférence [Termes IGN] occupation du sol [Termes IGN] prise en compte du contexte [Termes IGN] relation sémantique [Termes IGN] utilisation du sol Résumé : (Auteur) We propose a new approach for the simultaneous classification of land cover and land use considering spatial as well as semantic context. We apply a Conditional Random Fields (CRF) consisting of a land cover and a land use layer. In the land cover layer of the CRF, the nodes represent superpixels; in the land use layer, the nodes correspond to objects from a geospatial database. Intralayer edges of the CRF model spatial dependencies between neighbouring image sites. All spatially overlapping sites in both layers are connected by interlayer edges, which leads to higher order cliques modelling the semantic relation between all land cover and land use sites in the clique. A generic formulation of the higher order potential is proposed. In order to enable efficient inference in the two-layer higher order CRF, we propose an iterative inference procedure in which the two classification tasks mutually influence each other. We integrate contextual relations between land cover and land use in the classification process by using contextual features describing the complex dependencies of all nodes in a higher order clique. These features are incorporated in a discriminative classifier, which approximates the higher order potentials during the inference procedure. The approach is designed for input data based on aerial images. Experiments are carried out on two test sites to evaluate the performance of the proposed method. The experiments show that the classification results are improved compared to the results of a non-contextual classifier. For land cover classification, the result is much more homogeneous and the delineation of land cover segments is improved. For the land use classification, an improvement is mainly achieved for land use objects showing non-typical characteristics or similarities to other land use classes. Furthermore, we have shown that the size of the superpixels has an influence on the level of detail of the classification result, but also on the degree of smoothing induced by the segmentation method, which is especially beneficial for land cover classes covering large, homogeneous areas. Numéro de notice : A2017-510 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86456 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017083	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A novel MKL model of integrating LiDAR data and MSI for urban area classification / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5312 - 5326 Titre : A novel MKL model of integrating LiDAR data and MSI for urban area classification Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Qingwang Wang, Auteur ; Xiuping Jia, Auteur ; Jón Alti, Auteur Année de publication : 2015 Article en page(s) : pp 5312 - 5326 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] classificateur [Termes IGN] classification à base de connaissances [Termes IGN] classification automatique [Termes IGN] données lidar [Termes IGN] image multibande [Termes IGN] image spectrale [Termes IGN] milieu urbain Résumé : (Auteur) A novel multiple-kernel learning (MKL) model is proposed for urban classification to integrate heterogeneous features (HF-MKL) from two data sources, i.e., spectral images and LiDAR data. The features include spectral, spatial, and elevation attributes of urban objects from the two data sources. With these heterogeneous features (HFs), the new MKL model is designed to carry out feature fusion that is embedded in classification. First, Gaussian kernels with different bandwidths are used to measure the similarity of samples on each feature at different scales. Then, these multiscale kernels with different features are integrated using a linear combination. In the combination, the weights of the kernels with different features are determined by finding a projection based on the maximum variance. This way, the discriminative ability of the HFs is exploited at different scales and is also integrated to generate an optimal combined kernel. Finally, the optimization of the conventional support vector machine with this kernel is performed to construct a more effective classifier. Experiments are conducted on two real data sets, and the experimental results show that the HF-MKL model achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms. Numéro de notice : A2015-752 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421051 Date de publication en ligne : 07/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421051 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78742 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

An intelligent spatial proximity system using neurofuzzy classifiers and contextual information / F. Barouni in Geomatica, vol 69 n° 3 (september 2015)  

Public [article]  inGeomatica > vol 69 n° 3 (september 2015) . - pp 285 - 296 Titre : An intelligent spatial proximity system using neurofuzzy classifiers and contextual information Type de document : Article/Communication Auteurs : F. Barouni, Auteur ; Bernard Moulin, Auteur Année de publication : 2015 Article en page(s) : pp 285 - 296 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification à base de connaissances [Termes IGN] classification floue [Termes IGN] implémentation (informatique) [Termes IGN] relation spatiale Résumé : (auteur) Dans cet article, nous proposons une approche novatrice pour discuter de la proximité spatiale. L'approche est basée sur l'information contextuelle et utilise une classification neuro-floue pour traiter l'aspect d'incertitude de la proximité. Les systèmes neuro-flous consistent en une combinaison de réseaux neuronaux et de systèmes flous et incorporent les avantages des deux techniques. Même si les systèmes flous sont concentrés sur la représentation des connaissances, ils ne permettent pas l'estimation des fonctions d'appartenance. Inversement, les réseaux neuronaux utilisent de puissantes techniques d'apprentissage, mais ne sont pas capables d'expliquer comment les résultats sont obtenus. Les systèmes neuro-flous bénéficient des deux techniques en utilisant des données d'apprentissage pour générer les fonctions d'appartenance et en faisant appel à des règles floues pour représenter les connaissances spécialisées. En outre, l'information contextuelle est collectée à partir d'une base de connaissances. La classification neuro-floue est utilisée pour calculer les paramètres des fonctions d'appartenance des quantificateurs flous de relations spatiales. La solution complète que nous proposons est intégrée à un SIG, ce qui l'améliore grâce au raisonnement de proximité. Notre approche est utilisée dans le domaine des télécommunications et surtout dans les systèmes de surveillance par fibre optique. Dans de tels systèmes, un utilisateur doit qualifier la distance entre les événements rapportés par des capteurs et les objets voisins de l'environnement pour former des modèles spatiotemporels. Ces modèles sont définis pour aider les utilisateurs à prendre des décisions telles que l'optimisation de l'affectation des équipes d'urgence. Numéro de notice : A2015-666 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5623/cig2015-303 En ligne : https://doi.org/10.5623/cig2015-303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78275 [article]

Semisupervised transfer component analysis for domain adaptation in remote sensing image classification / Giona Matasci in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3550 - 3564 Titre : Semisupervised transfer component analysis for domain adaptation in remote sensing image classification Type de document : Article/Communication Auteurs : Giona Matasci, Auteur ; Michele Volpi, Auteur ; Mikhail Kanevski, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 3550 - 3564 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] classification à base de connaissances [Termes IGN] classification automatique [Termes IGN] découverte de connaissances [Termes IGN] extraction automatique [Termes IGN] méthode fondée sur le noyau [Termes IGN] occupation du sol Résumé : (Auteur) In this paper, we study the problem of feature extraction for knowledge transfer between multiple remotely sensed images in the context of land-cover classification. Several factors such as illumination, atmospheric, and ground conditions cause radiometric differences between images of similar scenes acquired on different geographical areas or over the same scene but at different time instants. Accordingly, a change in the probability distributions of the classes is observed. The purpose of this work is to statistically align in the feature space an image of interest that still has to be classified (the target image) to another image whose ground truth is already available (the source image). Following a specifically designed feature extraction step applied to both images, we show that classifiers trained on the source image can successfully predict the classes of the target image despite the shift that has occurred. In this context, we analyze a recently proposed domain adaptation method aiming at reducing the distance between domains, Transfer Component Analysis, and assess the potential of its unsupervised and semisupervised implementations. In particular, with a dedicated study of its key additional objectives, namely the alignment of the projection with the labels and the preservation of the local data structures, we demonstrate the advantages of Semisupervised Transfer Component Analysis. We compare this approach with other both linear and kernel-based feature extraction techniques. Experiments on multi- and hyperspectral acquisitions show remarkable cross- image classification performances for the considered strategy, thus confirming its suitability when applied to remotely sensed images. Numéro de notice : A2015-319 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2377785 En ligne : https://doi.org/10.1109/TGRS.2014.2377785 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76570 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015071	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  

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Object-based building change detection from a single multispectral image and pre-existing geospatial information / Georgia Doxani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  

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A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images / Zahra Ziaei in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)  

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Annual crop type classification of the US great plains for 2000 to 20011 / Daniel M. Howard in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)  

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Multi-agent recognition system based on object based image analysis using WorldView-2 / Fatemeh Tabib Mahmoudi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)  

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Spatial resolution imagery requirements for identifying structure damage in a hurricane disaster: A cognitive approach / S. Battersby in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)  

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Road network extraction in suburban areas / A. Grote in Photogrammetric record, vol 27 n° 137 (March - May 2012)  

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Real-time object detection with sub-pixel accuracy using the level set method / F. Burkert in Photogrammetric record, vol 26 n° 134 (June - August 2011)  

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Rule-based classification of a very high resolution image in an urban environment using multispectral segmentation by cartographic data / M. Bouziani in IEEE Transactions on geoscience and remote sensing, vol 48 n° 8 (August 2010)  

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Cognition-based extraction and modelling of topographic eminences / G. Sinha in Cartographica, vol 45 n° 2 (June 2010)  

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