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Titre : AI4GEO: a data intelligence platform for 3D geospatial mapping Type de document : Article/Communication Auteurs : Pierre-Marie Brunet, Auteur ; Pierre Lassalle, Auteur ; Simon Baillarin, Auteur ; Bruno Vallet , Auteur ; Arnaud Le Bris
Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. XLIII-B2-2021 Projets : AI4GEO / Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 817 - 823 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] chaîne de traitement
[Termes IGN] données localisées 3D
[Termes IGN] données massives
[Termes IGN] jeu de données localisées
[Termes IGN] plateforme logicielle
[Termes IGN] segmentation sémantique
[Termes IGN] traitement de données localiséesRésumé : (auteur) The availability of 3D Geospatial information is a key issue for many expanding sectors such as autonomous vehicles, business intelligence and urban planning. Its production is now possible thanks to the abundance of available data (Earth observation satellite constellations, insitu data, …) but manual interventions are still needed to guarantee a high level of quality, which prevents mass production. New artificial intelligence and big data technologies adapted to 3D imagery can help to remove these obstacles. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information and new added-value services. This paper will first introduce AI4GEO initiative, context and overall objectives. It will then present the current status of the project and in particular it will focus on the innovative platform put in place to handle big 3D datasets for analytics needs and it will present the first results of 3D semantic segmentations and associated perspectives. Numéro de notice : C2021-015 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2021-817-2021 date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-817-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98067 Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in International Journal of Health Geographics, vol inconnu (2021)
Titre : Geographically masking addresses to study COVID-19 clusters Type de document : Article/Communication Auteurs : Walid Houfaf-Khoufaf, Auteur ; Guillaume Touya Année de publication : 2021 Projets : 1-Pas de projet / Note générale : bibliographie
10.21203/rs.3.rs-128679/v1 DOI d'attenteLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] adresse postale
[Termes IGN] anonymisation
[Termes IGN] carte sanitaire
[Termes IGN] classification barycentrique
[Termes IGN] surveillance sanitaire
[Termes IGN] traitement de données localiséesRésumé : (auteur) The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic unfortunately generated a huge amount of data. But analysing this raw data, with for instance the address of the people who contracted COVID-19, raises some privacy issues, and geomasking is necessary to preserve both people privacy and the spatial accuracy required for analysis. This paper proposes dierent geomasking techniques adapted to this COVID-19 data. Methods: Different techniques are adapted from the literature, and tested on a synthetic dataset mimicking the COVID-19 spatio-temporal spreading in Paris and a more rural nearby region. Theses techniques are assessed in terms of k-anonymity and cluster preservation. Results: Three adapted geomasking techniques are proposed: aggregation, bimodal gaussian perturbation, and simulated crowding. All three can be useful in different use cases, but the bimodal gaussian perturbation is the overall best techniques, and the simulated crowding is the most promising one, provided some improvements are introduced to avoid points with a low k-anonymity. Conclusions: It is possible to use geomasking techniques on addresses of people who caught COVID-19, while preserving the important spatial patterns. Numéro de notice : A2021-065 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.21203/rs.3.rs-128679/v1 En ligne : https://doi.org/10.21203/rs.3.rs-128679/v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96857
in International Journal of Health Geographics > vol inconnu (2021)[article]
Titre : Volunteered geographic information systems: Technological design patterns Type de document : Article/Communication Auteurs : Jose Pablo Gómez‐Barrón, Auteur ; Miguel Angel Manso-Callejo, Auteur ; Ramón Alcarria, Auteur Année de publication : 2019 Article en page(s) : pp 976 - 1007 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] acquisition de données
[Termes IGN] approche participative
[Termes IGN] communauté virtuelle
[Termes IGN] données localisées des bénévoles
[Termes IGN] GeoWeb
[Termes IGN] Infrastructure de données
[Termes IGN] interface mobile
[Termes IGN] réseau social
[Termes IGN] système d'information géographique
[Termes IGN] traitement de données localiséesRésumé : (auteur) Volunteered geographic information (VGI) has been studied extensively within various contexts, however, generally focusing on isolated aspects of data. Therefore, we still need to advance the structured knowledge of the mechanisms that affect and support its creation. This article aims to contribute to the integrated study of VGI systems (VGIS) in order to enhance design processes that lead to a more systematic and efficient VGI management. We define VGIS, their main components, and general architectures. Then, we use the service blueprinting technique to conduct the case study of several systems, identifying system actions to find the processes and technologies forming the different functional components of VGIS: elements of the technological infrastructure that implement diverse processes to facilitate participation and data management. Integrated in the data collection component, we also propose a typology of crowdsourcing tasks that—coupled with the functional components—establishes a foundation for VGIS research and design. We characterize these components, arguing that they are necessary in the development of VGIS, however, there is still a need for a more detailed study of the flow of processes and subprocesses and how they work under the criteria of different types of projects to continue facilitating their design. Numéro de notice : A2019-550 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12544 date de publication en ligne : 13/06/2019 En ligne : https://doi.org/10.1111/tgis.12544 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94200
in Transactions in GIS > Vol 23 n° 5 (October 2019) . - pp 976 - 1007[article]
Titre : Cognitive aspects of human-computer interaction for GIS Type de document : Monographie Auteurs : Dieter Fritsch, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 196 p. ISBN/ISSN/EAN : 978-3-03921-569-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] Androïd
[Termes IGN] base de données statistiques
[Termes IGN] carte d'intérieur
[Termes IGN] classification par forêts aléatoires
[Termes IGN] cognition
[Termes IGN] état de l'art
[Termes IGN] géovisualisation
[Termes IGN] informatique en nuage
[Termes IGN] interface homme-machine
[Termes IGN] IOS
[Termes IGN] monde virtuel
[Termes IGN] oculométrie
[Termes IGN] représentation mentale spatiale
[Termes IGN] traitement de données localisées
[Termes IGN] visualisation de donnéesRésumé : (éditeur) The book is dealing with recent progress in human–computer interaction (HCI) related to geographic information science (GIS). The Editorial starts with an overview about the evolution of the Internet and first HCI concepts and stimulates recent HCI developments using 3D and 4D apps, running on all mobile devices with OS Android, iOS, Linus, and Windows. Eight research articles present the state-of-the-art in HCI–GIS-related issues, starting with gender and age differences in using indoor maps via the estimation of building heights from space to an efficient visualization method for polygonal data with dynamic simplification. The review article deals with progress and challenges on entity alignment of geographic knowledge bases. Note de contenu : Editorial “Cognitive aspects of human-computer interaction for GIS”
1- Gender and age differences in using indoor maps for wayfinding in real environments
2- Collaborative Immersive Virtual Environments for Education in Geography
3- Evaluation of User Performance in Interactive and Static 3D Maps
4- Determining optimal video length for the estimation of building height through radial displacement measurement from space
5- 4D time density of trajectories: Discovering spatiotemporal patterns in movement data
6- Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop
7- Estimating the performance of random forest versus multiple regression for predicting prices of the apartments
8- An efficient visualization method for polygonal data with dynamic simplification
9- Progress and challenges on entity alignment of geographic knowledge basesNuméro de notice : 25883 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03921-569-0 En ligne : https://doi.org/10.3390/books978-3-03921-569-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95748
Titre : Google Earth Engine applications Type de document : Monographie Auteurs : Lalit Kumar, Éditeur scientifique ; Onisimo Mutanga, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 420 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03897-885-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique
[Termes IGN] base de données d'images
[Termes IGN] Google Earth Engine
[Termes IGN] image 3D
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] information géographique numérique
[Termes IGN] informatique en nuage
[Termes IGN] moteur de recherche
[Termes IGN] surveillance écologique
[Termes IGN] système d'information environnementale
[Termes IGN] traitement de données localiséesRésumé : (éditeur) In a rapidly changing world, there is an ever-increasing need to monitor the Earth's resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth's surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales. Note de contenu : 1- Google Earth Engine applications since inception: usage, trends, and potential
2- Global estimation of biophysical variables from Google Earth Engine platform
3- An operational before-after-control-impact (BACI) designed platform for vegetation monitoring at planetary scale
4- Mapping vegetation and land use types in Fanjingshan national nature reserve using Google Earth Engine
5- A dynamic Landsat derived Normalized Difference Vegetation Index (NDVI) product for the conterminous United States
6- High spatial resolution visual band imagery outperforms medium resolution spectral imagery for ecosystem assessment in the semi-arid Brazilian Sert˜ao
7- Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016
8- Towards global-scale seagrass mapping and monitoring using Sentinel-2 on Google Earth Engine: The case study of the Aegean and Ionian Seas
9- BULC-U: Sharpening resolution and improving accuracy of land-use/land-cover classifications in Google Earth Engine
10- Monitoring the impact of land cover change on surface urban heat island through Google
Earth Engine: Proposal of a global methodology, first applications and problems
11- Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data
12- The first wetland inventory map of Newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform
13- A cloud-based multi-temporal ensemble classifier to map smallholder farming systems
14- Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine
15- SnowCloudHydro — A new framework for forecasting streamflow in snowy, data-scarce regions
16- Flood prevention and emergency response system powered by Google Earth Engine
17- Leveraging the Google Earth Engine for drought assessment using global soil moisture data
18- Multitemporal cloud masking in the Google Earth Engine
19- Historical and operational monitoring of surface sediments in the lower Mekong basin using Landsat and Google Earth Engine cloud computing
20- Mapping mining areas in the Brazilian Amazon using MSI/Sentinel-2 imagery (2017)
21- Estimating satellite-derived bathymetry (SDB) with the Google Earth Engine and Sentinel-2
22- Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potentialNuméro de notice : 25887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-885-5 En ligne : https://doi.org/10.3390/books978-3-03897-885-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95788 Machine learning techniques applied to geoscience information system and remote sensing / Saro Lee (2019)
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