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Titre : Data Acquisition and Processing in Cultural Heritage Type de document : Monographie Auteurs : Gabriele Bitelli, Auteur ; Fulvio Rinaudo, Auteur ; Diego Gonzalez-Aguilera, Auteur ; Pierre Grussenmeyer, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 276 p. ISBN/ISSN/EAN : 978-3-03921-741-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] acquisition d'images
[Termes IGN] archéologie aérienne
[Termes IGN] image captée par drone
[Termes IGN] image multitemporelle
[Termes IGN] patrimoine culturel
[Termes IGN] reconstruction 3D
[Termes IGN] restauration virtuelle
[Termes IGN] télémètre laser terrestreRésumé : (Editeur) Advances in the knowledge of the tangible components (position, size, shape) and intangible components (identity, habits) of an historic building or site involves fundamental and complex tasks in any project related to the conservation of cultural heritage (CH). In recent years, new geotechnologies have proven their usefulness and added value to the field of cultural heritage (CH) in the tasks of recording, modeling, conserving, and visualizing. In addition, current developments in building information modeling (HBIM), allow integration and simulation of different sources of information, generating a digital twin of any complex CH construction. As a result, experts in the area have increased the number of available sensors and methodologies. However, the quick evolution of geospatial technologies makes it necessary to revise their use, integration, and application in CH. This process is difficult to adopt, due to the new options which are opened for the study, analysis, management, and valorization of CH. Therefore, the aim of the present Special Issue is to cover the latest relevant topics, trends, and best practices in geospatial technologies and processing methodologies for CH sites and scenarios as well as to introduce the new tendencies. This book originates from the Special Issue “Data Acquisition and Processing in Cultural Heritage”, focusing primarily on data and sensor integration for CH; documentation/restoration in CH; heritage 3D documentation and modeling of complex CH sites; drone inspections in CH; software development in CH; and augmented reality in CH. It is hoped that this book will provide the advice and guidance required for any CH professional, making the best possible use of these sensors and methods in CH. Numéro de notice : 26499 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03921-741-0 En ligne : https://doi.org/10.3390/books978-3-03921-741-0 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97017
Titre : Distributed and parallel architectures for spatial data Type de document : Monographie Auteurs : Alberto Belussi, Éditeur scientifique ; Sara Migliorini, Éditeur scientifique ; Damiano Carra, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 170 p. ISBN/ISSN/EAN : 978-3-03936-751-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] base de données localisées
[Termes IGN] collecte de données
[Termes IGN] développement durable
[Termes IGN] données localisées
[Termes IGN] données massives
[Termes IGN] entrepôt de données localisées
[Termes IGN] géoportail
[Termes IGN] Hadoop
[Termes IGN] métadonnées
[Termes IGN] modèle numérique de surface
[Termes IGN] objet mobile
[Termes IGN] OLAP
[Termes IGN] OpenStreetMap
[Termes IGN] PostGIS
[Termes IGN] réseau social
[Termes IGN] SQL
[Termes IGN] système d'information géographique
[Termes IGN] téléphone intelligent
[Termes IGN] traitement parallèle
[Termes IGN] zone tamponRésumé : (Editeur) [Préface] In recent years, an increasing amount of spatial data has been collected by different types of devices, such as mobile phones, sensors, satellites, space telescope, and medical tools for analysis, or is generated by social networks, such as geotagged tweets. The processing of this huge amount of information, including spatial properties, which are frequently represented in heterogeneous ways, is a challenging task that has boosted research in the big data area in an attempt to investigate cases and propose new solutions for dealing with its peculiarities. In the literature, many different proposals and approaches for facing the problem have been proposed, addressing different goals and different types of users. However, most are obtained by customizing existing approaches which were originally developed for the processing of big data of the alphanumeric type, without any specific support for spatial or spatiotemporal properties. Thus, the proposed solutions can exploit the parallelism provided by these kinds of systems, but without taking into account, in a proficient way, the space and time dimensions that intrinsically characterize the analyzed datasets. As described in the literature, current solutions include: (i) the on-top approach, where an underlying system for traditional big datasets is used as a black box while spatial processing is added through the definition of user-defined functions that are specified on top of the underlying system; (ii) the from-scratch approach, where a completely new system is implemented for a specific application context; and (iii) the built-in approach, where an existing solution is extended by injecting spatial data functions into its core. This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data. Note de contenu : Preface
1- Distributed Processing of Location-Based Aggregate Queries Using MapReduce
2- Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
3- HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time
4- Mobility DataWarehouses
5- Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC
6- LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data
7- Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling
8- High-Performance Geospatial Big Data Processing System Based on MapReduceNuméro de notice : 25884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03936-751-1 En ligne : https://doi.org/10.3390/books978-3-03936-751-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95762 Disturbance effects on soil carbon and greenhouse gas emissions in forest ecosystems / Scott X. Chang (2020)
Titre : Disturbance effects on soil carbon and greenhouse gas emissions in forest ecosystems Type de document : Monographie Auteurs : Scott X. Chang, Éditeur scientifique ; Yanjiang Cai, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 232 p. ISBN/ISSN/EAN : 978-3-03928-667-6 Note générale : Bibliographie
This book is a printed edition of the Special Issue published in Forests.Langues : Anglais (eng) Descripteur : [Termes IGN] dioxyde de carbone
[Termes IGN] écosystème forestier
[Termes IGN] effet de serre
[Termes IGN] émission de gaz
[Termes IGN] gestion forestière
[Termes IGN] politique forestière
[Termes IGN] puits de carbone
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (Editeur) Forest ecosystems are often disturbed by agents such as harvesting, fire, wind, insects and diseases, and acid deposition, with differing intensities and frequencies. Such disturbances can markedly affect the amount, form, and stability of soil organic carbon in, and the emission of greenhouse gases, including CO2, CH4, and N2O from, forest ecosystems. It is vitally important that we improve our understanding of the impact of different disturbance regimes on forest soil carbon dynamics and greenhouse gas emissions to guide our future research, forest management practices, and policy development. This Special Issue provides an important update on the disturbance effects on soil carbon and greenhouse gas emissions in forest ecosystems in different climate regions. Numéro de notice : 26498 Affiliation des auteurs : non IGN Thématique : FORET Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03928-667-6 En ligne : https://doi.org/10.3390/books978-3-03928-667-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97000
Titre : Geo-spatial analysis in hydrology Type de document : Monographie Auteurs : Qiming Zhou, Éditeur scientifique ; Jianfeng Li, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 124 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-03936-981-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] barrage
[Termes IGN] bassin hydrographique
[Termes IGN] évapotranspiration
[Termes IGN] hydrographie
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] inondation
[Termes IGN] modèle hydrographique
[Termes IGN] modèle numérique de surface
[Termes IGN] occupation du sol
[Termes IGN] ruissellement
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du solRésumé : (auteur) Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale. Note de contenu : 1- Dynamic 3D simulation of flood risk based on the integration of spatio-temporal GIS and hydrodynamic models
2- Integrated hazard modeling for simulating torrential stream response to flash flood events
3- Consideration of level of confidence within multi-approach satellite-derived bathymetry
4- A method of watershed delineation for flat terrain using Sentinel-2A imagery and DEM: A case study of the Taihu basin
5- Monitoring the water quality of small water bodies using high-resolution remote sensing data
6- Estimation of crop water deficit in lower Bari doab, Pakistan using reflection-based crop coefficientNuméro de notice : 28625 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-981-2 En ligne : https://doi.org/10.3390/books978-3-03936-981-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99556
Titre : Intelligent Imaging and Analysis Type de document : Monographie Auteurs : DaeEun Kim, Éditeur scientifique ; Dosik Hwang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 492 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03921-921-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] estimation de pose
[Termes IGN] image 3D
[Termes IGN] image captée par drone
[Termes IGN] imagerie médicale
[Termes IGN] reconstruction d'image
[Termes IGN] segmentation d'image
[Termes IGN] texture d'image
[Termes IGN] vision par ordinateurRésumé : (éditeur) Imaging and analysis are widely involved in various research fields, including biomedical applications, medical imaging and diagnosis, computer vision, autonomous driving, and robot controls. Imaging and analysis are now facing big changes regarding intelligence, due to the breakthroughs of artificial intelligence techniques, including deep learning. Many difficulties in image generation, reconstruction, de-noising skills, artifact removal, segmentation, detection, and control tasks are being overcome with the help of advanced artificial intelligence approaches. This Special Issue focuses on the latest developments of learning-based intelligent imaging techniques and subsequent analyses, which include photographic imaging, medical imaging, detection, segmentation, medical diagnosis, computer vision, and vision-based robot control. These latest technological developments will be shared through this Special Issue for the various researchers who are involved with imaging itself, or are using image data and analysis for their own specific purposes. Note de contenu : 1- Special features on intelligent imaging and analysis
2- Intelligent evaluation of strabismus in videos based on an automated cover test
3- Application of a real-time visualization method of AUVs in underwater visual localization
4- Volumetric tooth wear measurement of scraper conveyor sprocket using shape from
focus-based method
5- A novel self-intersection penalty term for statistical body shape models and its applications in 3D pose estimation
6- A CNN model for human parsing based on capacity optimization
7- Fast 3D semantic mapping in road scenes †
8- Automated classification analysis of geological structures based on images data and deep learning model
9- Dark spot detection in SAR images of oil spill using segnet
10- A high-resolution texture mapping technique for 3D textured model
11- Image super-resolution algorithm based on dual-channel convolutional neural networks
12- No-reference automatic quality assessment for colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images using high-dynamic-range-derived features
13- A novel one-camera-five-mirror three-dimensional imaging method for reconstructing the cavitation bubble cluster in a water hydraulic valve
14- Deep residual network with sparse feedback for image restoration
15- An image segmentation method using an active contour model based on improved SPF
and LIF
16- Image segmentation approaches for weld pool monitoring during robotic arc welding
17- A novel discriminating and relative global spatial image representation with applications in CBIR
18- Double low-rank and sparse decomposition for surface defect segmentation of steel sheet
19- A UAV-based visual inspection method for rail surface defects
20- Feature-learning-based printed circuit board inspection via speeded-up robust features and random forest
21- Research progress of visual inspection technology of steel products
22- Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net
23- Semi-automatic segmentation of vertebral bodies in MR images of human lumbar spinesNuméro de notice : 28500 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03921-921-6 En ligne : https://doi.org/10.3390/books978-3-03921-921-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96897 PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink