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Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 3 (March 2021) . - n° 114 Titre : Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions Type de document : Article/Communication Auteurs : Amit Batar, Auteur ; Teiji Watanabe, Auteur Année de publication : 2021 Article en page(s) : n° 114 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse bivariée [Termes IGN] analyse de sensibilité [Termes IGN] bassin hydrographique [Termes IGN] cartographie des risques [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie locale [Termes IGN] Google Earth [Termes IGN] Himalaya [Termes IGN] Inde [Termes IGN] inventaire [Termes IGN] système d'information géographique [Termes IGN] théorème de Bayes Résumé : (auteur) The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection. Numéro de notice : A2021-233 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10030114 Date de publication en ligne : 27/02/2021 En ligne : https://doi.org/10.3390/ijgi10030114 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97228 [article]

Bayesian transfer learning for object detection in optical remote sensing images / Changsheng Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7705 - 7719 Titre : Bayesian transfer learning for object detection in optical remote sensing images Type de document : Article/Communication Auteurs : Changsheng Zhou, Auteur ; Jiangshe Zhang, Auteur ; Junmin Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7705 - 7719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] chaîne de traitement [Termes IGN] détection d'objet [Termes IGN] distribution de Fisher [Termes IGN] jeu de données localisées [Termes IGN] théorème de Bayes Résumé : (auteur) In the literature of object detection in optical remote sensing images, a popular pipeline is first modifying an off-the-shelf deep neural network, then initializing the modified network by pretrained weights on a source data set, and finally fine-tuning the network on a target data set. The procedure works well in practice but might not make full use of underlying knowledge implied by pretrained weights. In this article, we propose a novel method, referred to as Fisher regularization, for efficient knowledge transferring. Based on Bayes’ theorem, the method stores underlying knowledge into a Fisher information matrix and fine-tunes parameters based on the knowledge. The proposed method would not introduce extra parameters and is less sensitive to hyperparameters than classical weight decay. Experiments on NWPUVHR-10 and DOTA data sets show that the proposed method is effective and works well with different object detectors. Numéro de notice : A2020-679 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2983201 Date de publication en ligne : 14/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2983201 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96182 [article]

Bayesian inference on complicated data / Niansheng Tang (2020)  

Public Titre : Bayesian inference on complicated data Type de document : Monographie Auteurs : Niansheng Tang, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 118 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-83962-704-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] algorithme de Gibbs [Termes IGN] échantillonnage [Termes IGN] estimation bayesienne [Termes IGN] filtrage bayésien [Termes IGN] inférence statistique [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] théorème de Bayes Résumé : (éditeur) Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers. Note de contenu : 1- On the impact of the choice of the prior in Bayesian statistics 2- A brief tour of Bayesian sampling methods 3- A review on the exact Monte Carlo simulation 4- Bayesian analysis for random effects models 5- Bayesian inference of Gene regulatory network 6- Patient Bayesian inference: Cloud-based healthcare data analysis using constraint-based adaptive boost algorithm 7- The Bayesian posterior estimators under six loss functions for unrestricted and restricted parameter spaces Numéro de notice : 28590 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.83214 En ligne : https://doi.org/10.5772/intechopen.83214 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97937

Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography / Camille Chapdelaine (2019)  

Public Titre : Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography Type de document : Thèse/HDR Auteurs : Camille Chapdelaine, Auteur ; Charles Soussen, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 185 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de Doctorat de l’Université Paris - Saclay préparée à l'Université Paris-Sud, Sciences et Technologies de l’Information et de la Communication (STIC), Traitement du signal et des images Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] artefact [Termes IGN] capteur-projecteur [Termes IGN] faisceau [Termes IGN] inférence [Termes IGN] itération [Termes IGN] processeur graphique [Termes IGN] rayon X [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'image [Termes IGN] segmentation [Termes IGN] spectroscopie [Termes IGN] théorème de Bayes [Termes IGN] tomographie [Termes IGN] volume (grandeur) Index. décimale : THESE Thèses et HDR Résumé : (auteur) In industry, 3D X-ray Computed Tomography aims at virtually imaging a volume in order to inspect its interior. The virtual volume is obtained thanks to a reconstruction algorithm based on projections of X-rays sent through the industrial part to inspect. In order to compensate uncertainties in the projections such as scattering or beam-hardening, which are cause of many artifacts in conventional filtered backprojection methods, iterative reconstruction methods bring further information by enforcing a prior model on the volume to reconstruct, and actually enhance the reconstruction quality. In this context, this thesis proposes new iterative reconstruction methods for the inspection of aeronautical parts made by SAFRAN group. In order to alleviate the computational cost due to repeated projection and backprojection operations which model the acquisition process, iterative reconstruction methods can take benefit from the use of high-parallel computing on Graphical Processor Unit (GPU). In this thesis, the implementation on GPU of several pairs of projector and backprojector is detailed. In particular, a new GPU implementation of the matched Separable Footprint pair is proposed. Since many of SAFRAN's industrial parts are piecewise-constant volumes, a Gauss-Markov-Potts prior model is introduced, from which a joint reconstruction and segmentation algorithm is derived. This algorithm is based on a Bayesian approach which enables to explain the role of each parameter. The actual polychromacy of X-rays, which is responsible for scattering and beam-hardening, is taken into account by proposing an error-splitting forward model. Combined with Gauss-Markov-Potts prior on the volume, this new forward model is experimentally shown to bring more accuracy and robustness. At last, the estimation of the uncertainties on the reconstruction is investigated by variational Bayesian approach. In order to have a reasonable computation time, it is highlighted that the use of a matched pair of projector and backprojector is necessary. Note de contenu : 1- X-ray computed tomography : an inverse problem 2- Reconstruction methods in X-ray computed tomography 3- Projection and backprojection operators 4- Gauss-Markov-Potts prior model for joint reconstruction and segmentation 5- Error-splitting forward model and its application with Gauss-Markov-Potts prior 6- Towards the estimation of the uncertainties on the reconstruction by Variational Bayesian Approach 7- Conclusion and perspectives Numéro de notice : 25702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l’Information et de la Communication (STIC) : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Safran nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02110033 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94827

Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco / Hassane Rahali in Geocarto international, vol 34 n° 1 ([01/01/2019])  

Public [article]  inGeocarto international > vol 34 n° 1 [01/01/2019] . - pp 43 - 77 Titre : Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco Type de document : Article/Communication Auteurs : Hassane Rahali, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse des risques [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie locale [Termes IGN] incertitude géométrique [Termes IGN] lithologie [Termes IGN] Maroc [Termes IGN] méthode de Monte-Carlo [Termes IGN] méthode fiable [Termes IGN] modèle de simulation [Termes IGN] processus stochastique [Termes IGN] régression logistique [Termes IGN] théorème de Bayes [Termes IGN] zone à risque Résumé : (auteur) This paper aims at providing an answer as to whether generalization obtained with data-driven modelling can be used to gauge the plausibility of the physically based (PB) model’s prediction. Two statistical models namely; Weight of Evidence (WofE) and Logistic Regression (LR), and a PB model using the infinite slope assumptions were evaluated and compared with respect to their abilities to predict susceptible areas to shallow landslides at the 1:10.000 urban scale. Threshold-dependent performance metrics showed that the three methods produced statistically comparable results in terms of success and prediction rates. However, with the Area Under the receiver operator Curve (AUC), statistical models are more accurate (88.7 and 84.6% for LR and WofE, respectively) than the PB model (only 69.8%). Nevertheless, in such data-sparse situation, the usual approaches for validation, i.e. comparing observed with predicted data, are insufficient, formal uncertainty analysis (UA) is a means for evaluating the validity and reliability of the model. We then refitted the PB model using a stochastic modification of the infinite slope stability model input scheme using Monte Carlo (MC) method backed with sensitivity analysis (SA). For statistical models, we used an informal Student t-test for estimating the certainty of the predicted probability (PP) at each location. Both modelling outputs independently show a high validity; and whereas the level of confidence in LR and WofE models remained the same after performance re-evaluation, the accuracy of the PB model showed an improvement (AUC = 72%). This result is reasonable and provides a further validation of PB model. So, in urban slope analysis, where PB diagnostic is necessary, statistical and PB modelling may play equally supportive roles in landslide hazard assessment. Numéro de notice : A2019-219 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1357767 Date de publication en ligne : 10/08/2017 En ligne : https://doi.org/10.1080/10106049.2017.1357767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92737 [article]

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