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SIMuRG: System for Ionosphere Monitoring and Research from GNSS / Yury V. Yasyukevich in GPS solutions, Vol 24 n° 3 (July 2020)
[article]
Titre : SIMuRG: System for Ionosphere Monitoring and Research from GNSS Type de document : Article/Communication Auteurs : Yury V. Yasyukevich, Auteur ; Alexander V. Kiselev, Auteur ; Ilyav Zhivetiev, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] collecte de données
[Termes IGN] ionosphère
[Termes IGN] perturbation ionosphérique
[Termes IGN] récepteur GNSS
[Termes IGN] site web
[Termes IGN] surveillance
[Termes IGN] teneur totale en électronsRésumé : (auteur) Currently, more than 6000 operating GNSS receivers deliver observations to multiple servers. Ionospheric data are derived from these measurements providing outstanding space coverage and time resolution. There are about 200 million independent measurements daily. Researchers need sophisticated software tools to deal with such a large amount of data. We present recent advances and products from the System for Ionosphere Monitoring and Research from GNSS (SIMuRG). Currently, SIMuRG provides the total electron content (TEC) variations filtered within 2–10 min, 10–20 min, and 20–60 min, the Rate of the TEC Index, the Along Arc TEC Rate index, and the vertical TEC. SIMuRG is an online service at http://simurg.iszf.irk.ru. The system can be used free of charge and allows calculating both maps and series for arbitrary time intervals and geographic regions. All the data products are available in the form of data or figures. We discuss the system and its geophysics applications. Numéro de notice : A2020-327 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-00983-2 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1007/s10291-020-00983-2 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95208
in GPS solutions > Vol 24 n° 3 (July 2020)[article]GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)
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Titre : GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2020 Article en page(s) : pp 556 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] cartographie topographique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] collecte de données
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] géobalise
[Termes IGN] toponyme
[Termes IGN] United States Geological SurveyRésumé : (Auteur) Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain. Numéro de notice : A2020-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12633 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12633 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95307
in Transactions in GIS > Vol 24 n° 3 (June 2020) . - pp 556 - 572[article]Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)
[article]
Titre : Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods Type de document : Article/Communication Auteurs : Rocio Nahime Torres, Auteur Année de publication : 2020 Article en page(s) : pp 225 – 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] base de données altimétriques
[Termes IGN] classification floue
[Termes IGN] collecte de données
[Termes IGN] données localisées des bénévoles
[Termes IGN] figuré du terrain
[Termes IGN] méthode heuristique
[Termes IGN] modèle numérique de surface
[Termes IGN] montagne
[Termes IGN] OpenStreetMap
[Termes IGN] sommet (relief)
[Termes IGN] système d'information géographiqueRésumé : (auteur) Landform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets. Numéro de notice : A2020-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00295-2 Date de publication en ligne : 24/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00295-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95870
in Applied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246[article]
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
Titre : Mapping crisis : participation, datafication and humanitarianism in the age of digital mapping Type de document : Monographie Auteurs : Doug Specht, Éditeur scientifique Editeur : Londres : University of London Press Année de publication : 2020 Importance : 259 p. ISBN/ISSN/EAN : 978-1-912250-38-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] analyse spatiale
[Termes IGN] cartographie collaborative
[Termes IGN] changement climatique
[Termes IGN] collecte de données
[Termes IGN] gestion de crise
[Termes IGN] participation du public
[Termes IGN] représentation cartographique
[Termes IGN] science citoyenne
[Termes IGN] visualisation de donnéesRésumé : (Editeur) The digital age has thrown questions of representation, participation and humanitarianism back to the fore, as machine learning, algorithms and big data centres take over the process of mapping the subjugated and subaltern. Since the rise of Google Earth in 2005, there has been an explosion in the use of mapping tools to quantify and assess the needs of those in crisis, including those affected by climate change and the wider neo-liberal agenda. Yet, while there has been a huge upsurge in the data produced around these issues, the representation of people remains questionable. Some have argued that representation has diminished in humanitarian crises as people are increasingly reduced to data points. In turn, this data has become ever more difficult to analyse without vast computing power, leading to a dependency on the old colonial powers to refine the data collected from people in crisis, before selling it back to them. This book brings together critical perspectives on the role that mapping people, knowledges and data now plays in humanitarian work, both in cartographic terms and through data visualisations, and questions whether, as we map crises, it is the map itself that is in crisis. Note de contenu : Introduction: mapping in times of crisis / Doug Specht
1. Mapping as tacit représentations of the colonial gaze / Tamara Bellone, Salvatore Engel- Di Mauro, Francesco Fiermonte, Emiliana Armano and Linda Quiquivix
2. The failures of participatory mapping: a mediational perspective / Gregory Asmolov
3. Knowledge and spatial production between old and new representations: a conceptual and operative Framework / Maria Rosaria Prisco
4. Data colonialism, surveillance capitalism and drones / Faine Greenwood
5. The role of data collection, mapping and analysis in the reproduction of refugeeness and migration discourses: reflections from the Refugee Spaces project / Giovanna Astolfo, Ricardo Marten Caceres, Garyfalia Palaiologou, Camillo Boano and Ed Manley
6. Dying in the technosphere: an intersectional analysis of European migration maps / Monika Halkort
7. Now the totality maps us: mapping climate migration and surveilling movable borders in digital cartographies / Bogna M. Konior
8. The rise of the citizen data scientist / Aleš Završnik and Pika Šarf
9. Modalities of united statelessness / Rupert AllanNuméro de notice : 26514 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.14296/920.9781912250387 En ligne : https://doi.org/10.14296/920.9781912250387 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97284 PermalinkAnalyse spatiotemporelle des tournées de livraison d’une entreprise de livraison à domicile / Khaled Belhassine in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)PermalinkLe réseau GPS permanent (RGP) de l'IGN / Sébastien Saur in Géomètre, n° 2168 (avril 2019)PermalinkA vélo au travers des Andes, pour OpenStreetMap / Anonyme in Géomatique expert, n° 126 (janvier - février 2019)PermalinkWebscraping, bigdata et analyse spatiale de données immobilières : réponse à un projet ESPON au sein de l'UMS RIATE / Marc Lieury (2019)PermalinkEntre perception de soi et construction du pouvoir d'agir : le pouvoir caché des cartes participatives / Stéphanie Bost in Cartes & Géomatique, n° 235-236 (mars - juin 2018)PermalinkResearches about the living condition in Ulaanbaatar with mapping developments based on a participatory approach / Paul Roux (2018)PermalinkPermalinkRepésenter le Border art et le mur de séparation israélo-palestinien / Clémence Lehec in Cartes & Géomatique, n° 225 (septembre 2015)PermalinkMéthode de cartographie de la consommation de sol agricole dans le Grand Genève / Marie-Laure Halle (2015)Permalink