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On the suitability of a unified GIS-BIM-HBIM framework for cataloguing and assessing vulnerability in Historic Urban Landscapes: a critical review / Rafael Ramirez Eudave in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 10 (October 2021) . - pp 2047 - 2077 Titre : On the suitability of a unified GIS-BIM-HBIM framework for cataloguing and assessing vulnerability in Historic Urban Landscapes: a critical review Type de document : Article/Communication Auteurs : Rafael Ramirez Eudave, Auteur ; Tiago Miguel Ferreira, Auteur Année de publication : 2021 Article en page(s) : pp 2047 - 2077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] centre urbain [Termes IGN] CityGML [Termes IGN] codage [Termes IGN] format Industry foudation classes IFC [Termes IGN] géoréférencement [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] monument historique [Termes IGN] prévention des risques [Termes IGN] système d'information géographique [Termes IGN] vulnérabilité Résumé : (auteur) The use of digital representations of physical objects allows to simulate phenomena and predict behaviours. The representativeness of a model is based on the congruency between the code, the survey and the modelling strategies. In terms of physical space, two important approaches are the Geographical Information Systems (GIS) and Building Information Modelling (BIM). GIS approach is intended to code environmental information based on geographical references. BIM approach is intended to code buildings in terms of their components, generating parametric descriptions. For historic buildings, BIM extends to the so-called Historical BIM models (HBIM). Together, these strategies allow coding territorial-scale entities, such as historical centres. An application for these models would be the simulation of risk situations, leading to vulnerability analysis. Despite the existence of GIS-BIM-HBIM integration examples, most of their procedures are designed to fit with a specific case study, which questions the suitability of a general coding framework. This paper presents a vision of state-of-the-art technologies and strategies for coding, surveying and model historical centres, with emphases on the analysis of urban vulnerability and risk. Finally, we propose a general comprehensive framework on the convergences of GIS-BIM-HBIM technologies and successful practices. Numéro de notice : A2021-657 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1844208 Date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1844208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98393 [article]

Unsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 8 (April-2 2021) . - n° 1602 Titre : Unsupervised multi-level feature extraction for improvement of hyperspectral classification Type de document : Article/Communication Auteurs : Qiaoqiao Sun, Auteur ; Xuefeng Liu, Auteur ; Salah Bourennane, Auteur Année de publication : 2021 Article en page(s) : n° 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] codage [Termes IGN] convolution (signal) [Termes IGN] déconvolution [Termes IGN] échantillonnage d'image [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] observation multiniveaux Résumé : (auteur) Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features. Numéro de notice : A2021-380 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081602 Date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97628 [article]

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 490 - 512 Titre : Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Type de document : Article/Communication Auteurs : Xiongfeng Yan, Auteur ; Tinghua Ai, Auteur ; Min Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 490 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] codage [Termes IGN] données vectorielles [Termes IGN] graphe [Termes IGN] mesure géométrique [Termes IGN] modélisation du bâti [Termes IGN] représentation cognitive [Termes IGN] représentation spatiale Résumé : (auteur) The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching. Numéro de notice : A2021-166 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768260 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97100 [article]

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Programming for Computations - Python / Svein Linge (2020)  

Public Titre : Programming for Computations - Python Type de document : Guide/Manuel Auteurs : Svein Linge, Éditeur scientifique ; Hans Petter Langtangen, Éditeur scientifique Editeur : Springer Nature Année de publication : 2020 Importance : 332 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-16877-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques [Termes IGN] codage [Termes IGN] équation différentielle [Termes IGN] programmation informatique [Termes IGN] Python (langage de programmation) Index. décimale : 26.04 Langages informatiques Résumé : (éditeur) This second edition of the book presents computer programming as a key method for solving mathematical problems and represents a major revision: all code is now written in Python version 3.6 (the first edition was based on Python version 2.7). The first two chapters of the previous edition have been extended and split up into five new chapters, thus expanding the introduction to programming from 50 to 150 pages. Throughout, explanations are now more complete, previous examples have been modified, and new sections, examples, and exercises have been added. Also, errors and typos have been corrected. The book was inspired by the Springer book TCSE 6, A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. Note de contenu : 1- The First Few Steps 2- A Few More Steps 3- Loops and Branching 4- Functions and the Writing of Code 5- Some More Python Essentials 6- Computing Integrals and Testing Code 7- Solving Nonlinear Algebraic Equations 8- Solving Ordinary Differential Equations 9- Solving Partial Differential Equations Numéro de notice : 28461 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-030-16877-3 En ligne : https://doi.org/10.1007/978-3-030-16877-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99072

Recent advances in geographic information system for Earth sciences / Yosoon Choi (2020)  

Public Titre : Recent advances in geographic information system for Earth sciences Type de document : Monographie Auteurs : Yosoon Choi, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 264 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-490-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] bassin hydrographique [Termes IGN] cartographie thématique [Termes IGN] codage [Termes IGN] cognition [Termes IGN] développement durable [Termes IGN] effondrement de terrain [Termes IGN] interface homme-machine [Termes IGN] modèle dynamique [Termes IGN] modèle numérique de surface [Termes IGN] oculométrie [Termes IGN] outil d'aide à la décision [Termes IGN] planification urbaine [Termes IGN] QGIS [Termes IGN] réseau de transport [Termes IGN] réseau social [Termes IGN] utilisation du sol Résumé : (éditeur) Geographic information systems (GISs) have played a vital role in Earth sciences by providing a powerful means of observing the world and various tools for solving complex problems. The scientific community has used GISs to reveal fascinating details about the Earth and other planets. This book on recent advances in GIS for Earth sciences includes 12 publications from esteemed research groups worldwide. The research and review papers in this book belong to the following broad categories: Earth science informatics (geoinformatics), mining, hydrology, natural hazards, and society. Note de contenu : 1- Recent advances in geographic information system for earth sciences 2- An efficient parallel algorithm for polygons overlay analysis 3- Vector map random encryption algorithm based on multi-scale simplification and Gaussian distribution 4- Evaluation of effective cognition for the QGIS processing modeler 5- Geo-sensor framework and composition toolbox for efficient deployment of multiple spatial context service platforms in sensor networks 6- Review of GIS-based applications for mining: Planning, operation, and environmental management 7- A tightly coupled GIS and spatiotemporal modeling for methane emission simulation in the underground coal mine system 8- Evaluation of reliable digital elevation model resolution for TOPMODEL in two mountainous watersheds, South Korea 9- Spatiotemporal changes of urban rainstorm-related micro-blogging activities in response to rainstorms: A case study in Beijing, China 10- Rainfall induced landslide studies in Indian Himalayan region: A critical review 11- GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques 12- Spatiotemporal dynamics and obstacles of the multi-functionality of land use in Xiangxi, China 13- Analyzing spatial community pattern of network traffic flow and its variations across time based on taxi GPS trajectories Numéro de notice : 28439 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-490-9 En ligne : https://doi.org/10.3390/books978-3-03936-490-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98876

Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)  

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Introduction to Deep Learning / Sandro Skansi (2018)  

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Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Adaptive spectral–spatial compression of hyperspectral image with sparse representation / Wei Fu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

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Retina-inspired image and video coding / Effrosyni Doutsi (2017)  

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Sparsity, redundancy and robustness in artificial neural networks for learning and memory / Philippe Tigréat (2017)  

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Discriminative-dictionary-learning-based multilevel point-cluster features for ALS point-cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

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Sparse output coding for scalable visual recognition / Bin Zhao in International journal of computer vision, vol 119 n° 1 (August 2016)  

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A multilevel point-cluster-based discriminative feature for ALS point cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

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Toward a generalizable image representation for large-scale change detection : application to generic damage analysis / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

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