Descripteur
Documents disponibles dans cette catégorie (9015)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Analysis of cycling network evolution in OpenStreetMap through a data quality prism / Raphaël Bres (2023)
Titre : Analysis of cycling network evolution in OpenStreetMap through a data quality prism Type de document : Article/Communication Auteurs : Raphaël Bres, Auteur ; Veronika Peralta, Auteur ; Arnaud Le Guilcher , Auteur ; Thomas Devogele , Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Cyril de Runz, Auteur Editeur : Göttingen : Copernicus publications Année de publication : 2023 Collection : AGILE GIScience Series num. 4 Conférence : AGILE 2023, 26th international AGILE Conference on Geographic Information Science, Spatial data for design 13/06/2023 16/06/2023 Delft Pays-Bas OA Proceedings Importance : n° 3 ; 9 p. Note générale : bibliographie
voir aussi le rapport de reproductibilité : https://doi.org/10.17605/OSF.IO/9KP7ULangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] bicyclette
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] mobilité territoriale
[Termes IGN] mobilité urbaine
[Termes IGN] modèle de simulation
[Termes IGN] OpenStreetMap
[Termes IGN] qualité des données
[Termes IGN] voie cyclableRésumé : (auteur) Cycling practice has been constantly increasing for several years and the COVID crisis has just accelerated the process. Indeed, more and more municipalities have developed new cycle paths to facilitate cycling. Considering this increasing interest for cycling, it makes sense to study how this recent evolution is reflected in the underlying representation of the cycling network in the geographic databases. Main studies analysing the evolution of the road network focus on the motor vehicle network in the major cities of the world. These studies do not seem applicable to cycling network specially to some low population density areas or even to smaller cities. This paper analyses the changes in the cycling network through OSM data from a data freshness perspective. These changes can be either updates from changes in the real-world network or upgrades to the network. To these end, we propose a method using a Monte Carlo simulation (MCS) to analyse the frequency of changes in cycling routes in several areas with different population density, all in the Loire Valley region in France. We also define the cycling network, which is a very complex concept and we explain how it is represented in OSM data and suffers from different data quality issues. Results show that the number of changes across time are similar in areas having a similar population density, while being lower in low population density areas. These phenomena is higher in the cycling network compared to other networks. Numéro de notice : C2023-011 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : Vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-4-3-2023 Date de publication en ligne : 06/06/2023 En ligne : https://doi.org/10.5194/agile-giss-4-3-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103308
Titre : Artificial intelligence oceanography Type de document : Monographie Auteurs : Xiaofeng Li, Éditeur scientifique ; Fan Wang, Éditeur scientifique Editeur : Springer Nature Année de publication : 2023 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-19637-5-9 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algue
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cyclone
[Termes IGN] détection d'objet
[Termes IGN] iceberg
[Termes IGN] intelligence artificielle
[Termes IGN] océanographie
[Termes IGN] température de surface de la merRésumé : (éditeur) This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing. Note de contenu : 1- Artificial Intelligence Foundation of smart ocean
2- Forecasting tropical instability waves based on artificial intelligence
3- Sea surface height anomaly prediction based on artificial intelligence
4- Satellite data-driven internal solitary wave forecast based on machine learning techniques
5- AI-based subsurface thermohaline structure retrieval from remote sensing observations
6- Ocean heat content retrieval from remote sensing data based on machine learning
7- Detecting tropical cyclogenesis using broad learning system from satellite passive microwave observations
8- Tropical cyclone monitoring based on geostationary satellite imagery
9- Reconstruction of pCO2 data in the Southern ocean based on feedforward neural network
10- Detection and analysis of mesoscale eddies based on deep learning
11- Deep convolutional neural networks-based coastal inundation mapping from SAR imagery: with one application case for Bangladesh, a UN-defined least developed country
12- Sea ice detection from SAR images based on deep fully convolutional networks
13- Detection and analysis of marine green algae based on artificial intelligence
14- Automatic waterline extraction of large-scale tidal flats from SAR images based on deep convolutional neural networks
15- Extracting ship’s size from SAR images by deep learning
16- Benthic organism detection, quantification and seamount biology detection based on deep learningNuméro de notice : 24105 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.1007/978-981-19-6375-9 En ligne : https://link.springer.com/book/10.1007/978-981-19-6375-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103058
Titre : CDPS: Constrained DTW-Preserving Shapelets Type de document : Article/Communication Auteurs : Hussein El Amouri, Auteur ; Thomas Lampert, Auteur ; Pierre Gançarski, Auteur ; Clément Mallet , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2023 Collection : Lecture notes in Computer Science Sous-collection : Lecture Notes in Artificial Intelligence num. 13713 Projets : HIATUS / Giordano, Sébastien Conférence : ECML PKDD 2022, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 19/09/2022 23/09/2022 Grenoble France Proceedings Springer Projets : HERELLES / Gançarski, Pierre Importance : pp 21 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de données
[Termes IGN] analyse de groupement
[Termes IGN] classification
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] distance euclidienne
[Termes IGN] jeu de données localisées
[Termes IGN] série temporelle
[Termes IGN] traitement de données localisées
[Termes IGN] transformationRésumé : (auteur) The analysis of time series for clustering and classification is becoming ever more popular because of the increasingly ubiquitous nature of IoT, satellite constellations, and handheld and smart-wearable devices, etc. The presence of phase shift, differences in sample duration, and/or compression and dilation of a signal means that Euclidean distance is unsuitable in many cases. As such, several similarity measures specific to time-series have been proposed, Dynamic Time Warping (DTW) being the most popular. Nevertheless, DTW does not respect the axioms of a metric and therefore Learning DTW-Preserving Shapelets (LDPS) have been developed to regain these properties by using the concept of shapelet transform. LDPS learns an unsupervised representation that models DTW distances using Euclidean distance in shapelet space. This article proposes constrained DTW-preserving shapelets (CDPS), in which a limited amount of user knowledge is available in the form of must link and cannot link constraints, to guide the representation such that it better captures the user’s interpretation of the data rather than the algorithm’s bias. Subsequently, any unconstrained algorithm can be applied, e.g. K-means clustering, k-NN classification, etc, to obtain a result that fulfils the constraints (without explicit knowledge of them). Furthermore, this representation is generalisable to out-of-sample data, overcoming the limitations of standard transductive constrained-clustering algorithms. CLDPS is shown to outperform the state-of-the-art constrained-clustering algorithms on multiple time-series datasets. Numéro de notice : C2022-052 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-031-26387-3_2 Date de publication en ligne : 17/03/2023 En ligne : https://doi.org/10.1007/978-3-031-26387-3_2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103157 Children’s walking to urban services: an analysis of pedestrian access to social infrastructures and its relationship with land use / Wonjun No in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)
[article]
Titre : Children’s walking to urban services: an analysis of pedestrian access to social infrastructures and its relationship with land use Type de document : Article/Communication Auteurs : Wonjun No, Auteur ; Junyong Choi, Auteur ; Youngchul Kim, Auteur Année de publication : 2023 Article en page(s) : pp 189 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse spatiale
[Termes IGN] enfant
[Termes IGN] matrice
[Termes IGN] milieu urbain
[Termes IGN] navigation pédestre
[Termes IGN] origine - destination
[Termes IGN] Séoul
[Termes IGN] service public
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du solRésumé : (auteur) The conceptual framework of child-friendly cities guarantees children’s equal access to public urban services. Despite the widespread application of geographical information systems (GISs) and pedestrian network analysis, studies have yet to analyze children’s comprehensive pedestrian access to urban services in a large-scale city. This study demonstrates GIS-based approaches to measuring children’s pedestrian access to urban services using a pedestrian path layer and the spatial layers of social infrastructure locations in Seoul, South Korea. We show the spatial inequities in children’s access to urban services, which depend on the locational characteristics of social infrastructures and the urban development patterns around children. We analyze how children’s access to social infrastructures is differentiated by land use composition. Our statistical analysis finds that low-rise residential areas, consisting of impermeable street patterns, increase children’s walking distance and restrict children from accessing urban services within their walkable area. In addition, there is potential for key infrastructures such as schools and local community centers to promote pedestrian access to urban services for children. Considering pedestrian access at the street level will help pinpoint vulnerable areas with children who have less access overall and maximize the users served within the service areas of infrastructures. Numéro de notice : A2023-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2104455 Date de publication en ligne : 27/07/2022 En ligne : https://doi.org/10.1080/13658816.2022.2104455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102312
in International journal of geographical information science IJGIS > vol 37 n° 1 (January 2023) . - pp 189 - 214[article]A CNN based approach for the point-light photometric stereo problem / Fotios Logothetis in International journal of computer vision, vol 131 n° 1 (January 2023)
[article]
Titre : A CNN based approach for the point-light photometric stereo problem Type de document : Article/Communication Auteurs : Fotios Logothetis, Auteur ; Roberto Mecca, Auteur ; Ignas Budvytis, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 101 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] éclairement lumineux
[Termes IGN] effet de profondeur cinétique
[Termes IGN] intensité lumineuse
[Termes IGN] itération
[Termes IGN] reconstruction 3D
[Termes IGN] réflectivité
[Termes IGN] stéréoscopie
[Termes IGN] vue perspectiveRésumé : (auteur) Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world ’dataset for near-fieLd point light soUrCe photomEtric Stereo’ of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page. Numéro de notice : A2023-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01689-3 Date de publication en ligne : 07/10/2022 En ligne : https://doi.org/10.1007/s11263-022-01689-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102364
in International journal of computer vision > vol 131 n° 1 (January 2023) . - pp 101 - 120[article]A comparative assessment of the statistical methods based on urban population density estimation / Merve Yılmaz in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkDecadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkDecision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkEstablishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP / Pengfei Xia in GPS solutions, vol 27 n° 1 (January 2023)PermalinkEstimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkEstimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density / Luyen K. Bui in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 7 (January 2023)PermalinkField optical clocks and sensitivity to mass anomalies for geoscience applications / Guillaume Lion (2023)PermalinkForest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkGeneration of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data / Ji Won Suh in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkGeographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model / Zhenyang Du in Journal of Marine Systems, vol 237 (January 2023)PermalinkGeographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol inconnu (2023)PermalinkA geometry-aware attention network for semantic segmentation of MLS point clouds / Jie Wan in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)PermalinkGeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates / Valerio Marsocci (2023)PermalinkGeospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)PermalinkA hexagon-based method for polygon generalization using morphological operators / Lu Wang in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)PermalinkA hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)PermalinkA hierarchical multiview registration framework of TLS point clouds based on loop constraint / Hao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)PermalinkHow to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data / Rongfang Lyu in Sustainable Cities and Society, vol 88 (January 2023)PermalinkImprovement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier / Mahmoud Mohamed in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkImproving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar / Andrew W. Whelan in Remote sensing of environment, vol 284 (January 2023)Permalink