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Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning / J.F. Roberts in Computers & geosciences, vol 167 (October 2022)
Titre : Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning Type de document : Article/Communication Auteurs : J.F. Roberts, Auteur ; R. Mwangi, Auteur ; F. Mukabi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] carte thématique
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] image Sentinel-MSI
[Termes IGN] informatique en nuage
[Termes IGN] Kenya
[Termes IGN] langage de programmation
[Termes IGN] observation de la Terre
[Termes IGN] Python (langage de programmation)
[Termes IGN] surveillance forestièreRésumé : (auteur) Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically. This paper introduces the open-access Python 3 package Pyeo - “Python for Earth Observation”. Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences. Numéro de notice : A2022-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105192 Date de publication en ligne : 09/07/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101575
in Computers & geosciences > vol 167 (October 2022) . - n° 105192[article]
Titre : A multi-source spatio-temporal data cube for large-scale geospatial analysis Type de document : Article/Communication Auteurs : Fan Gao, Auteur ; Peng Yue, Auteur ; Zhipeng Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1853 - 1884 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] cube espace-temps
[Termes IGN] cyberinfrastructure
[Termes IGN] données spatiotemporelles
[Termes IGN] Géocube
[Termes IGN] hypercube
[Termes IGN] informatique en nuage
[Termes IGN] intelligence artificielle
[Termes IGN] observation de la TerreRésumé : (auteur) Data management and analysis are challenging with big Earth observation (EO) data. Expanding upon the rising promises of data cubes for analysis-ready big EO data, we propose a new geospatial infrastructure layered over a data cube to facilitate big EO data management and analysis. Compared to previous work on data cubes, the proposed infrastructure, GeoCube, extends the capacity of data cubes to multi-source big vector and raster data. GeoCube is developed in terms of three major efforts: formalize cube dimensions for multi-source geospatial data, process geospatial data query along these dimensions, and organize cube data for high-performance geoprocessing. This strategy improves EO data cube management and keeps connections with the business intelligence cube, which provides supplementary information for EO data cube processing. The paper highlights the major efforts and key research contributions to online analytical processing for dimension formalization, distributed cube objects for tiles, and artificial intelligence enabled prediction of computational intensity for data cube processing. Case studies with data from Landsat, Gaofen, and OpenStreetMap demonstrate the capabilities and applicability of the proposed infrastructure. Numéro de notice : A2022-643 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2087222 Date de publication en ligne : 14/06/2022 En ligne : https://doi.org/10.1080/13658816.2022.2087222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101458
in International journal of geographical information science IJGIS > vol 36 n° 9 (September 2022) . - pp 1853 - 1884[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2022091 SL Revue Centre de documentation Revues en salle Disponible
Titre : Mapping global flying aircraft activities using Landsat 8 and cloud computing Type de document : Article/Communication Auteurs : Fen Zhao, Auteur ; Lang Xia, Auteur ; Arve Kylling, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aéronef
[Termes IGN] analyse spatio-temporelle
[Termes IGN] aviation civile
[Termes IGN] carte thématique
[Termes IGN] climat
[Termes IGN] détection d'objet
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] informatique en nuage
[Termes IGN] navigation aérienne
[Termes IGN] trafic aérienRésumé : (auteur) Satellite-based remote sensing might provide a potential way for monitoring the global flight activities and their environment impacts, while the remote sensing community pays less attention on it. In this study, we presented a flying aircraft detection algorithm which effectively handles the noise on Landsat 8 OLI cirrus band caused by energetic particles in the South Atlantic Anomaly region, and a new framework based on cloud infrastructure was proposed to map global flying aircraft activities from 2013 to 2020 using Landsat 8 Operational Land Imager (OLI) data. Validation was performed for 254 scenes recorded for various cloudy and surface conditions and vapor contents. The overall percentages of false alarms and omissions for these validation images were 5.37% and 7.80%, respectively. Limited to the resolution of Landsat data, cloud, the size and flight altitude of the aircraft, 42.99% flying aircraft were undetected compared with the FlightRadar24. Instead of using the Google Earth Engine, we employed more flexible cloud computing techniques, Google Cloud Storage and Google Calculation Engine, to construct our framework for the larger volume data. A total of 1.94 million Landsat images were analyzed to obtain the activities maps, and the results showed that globally flying aircraft increased by 25.85% from 2014 to 2019 (the year 2013 was excluded for the low coverage of Landsat scenes), with an annual rate of 4.31%. In 2020, flying aircraft were reduced by 40% compared with 2019 due to the influence of COVID-19 and traveling restrictions, and Europe was the most severely affected by COVID-19, with an 84.59% decline of flying aircraft in April 2020. This study provides a unique long-term supplement to monitor aviation activities and their climate impact. Numéro de notice : A2022-090 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.12.003 Date de publication en ligne : 15/12/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.12.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99506
in ISPRS Journal of photogrammetry and remote sensing > vol 184 (February 2022) . - pp 19 - 30[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2022021 SL Revue Centre de documentation Revues en salle Disponible 081-2022023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : A web GIS-based integration of 3D digital models with linked open data for cultural heritage exploration Type de document : Article/Communication Auteurs : Ikrom Nishanbaev, Auteur ; Erik Champion, Auteur ; David A. McMeekin, Auteur Année de publication : 2021 Article en page(s) : n° 684 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] Australie
[Termes IGN] carte interactive
[Termes IGN] édition en libre accès
[Termes IGN] hétérogénéité
[Termes IGN] informatique en nuage
[Termes IGN] interface de programmation
[Termes IGN] interopérabilité
[Termes IGN] métadonnées
[Termes IGN] modélisation 3D
[Termes IGN] patrimoine culturel
[Termes IGN] QGIS
[Termes IGN] SPARQL
[Termes IGN] visualisation 3D
[Termes IGN] web des données
[Termes IGN] WebSIGRésumé : (auteur) In recent years, considerable efforts have been made by cultural heritage institutions across the globe to digitise cultural heritage sites, artifacts, historical maps, etc. for digital preservation and online representation. On the other hand, ample research projects and studies have been published that demonstrate the great capabilities of web-geographic information systems (web-GIS) for the dissemination and online representation of cultural heritage data. However, cultural heritage data and the associated metadata produced by many cultural heritage institutions are heterogeneous. To make this heterogeneous data more interoperable and structured, an ever-growing number of cultural heritage institutions are adopting linked data principles. Although the cultural heritage domain has already started implementing linked open data concepts to the cultural heritage data, there are not many research articles that present an easy-to-implement, free, and open-source-based web-GIS architecture that integrates 3D digital cultural heritage models with cloud computing and linked open data. Furthermore, the integration of web-GIS technologies with 3D web-based visualisation and linked open data may offer new dimensions of interaction and exploration of digital cultural heritage. To demonstrate the high potential of integration of these technologies, this study presents a novel cloud architecture that attempts to enhance digital cultural heritage exploration by integrating 3D digital cultural heritage models with linked open data from DBpedia and GeoNames platforms using web-GIS technologies. More specifically, a digital interactive map, 3D digital cultural heritage models, and linked open data from DBpedia and GeoNames platforms were integrated into a cloud-based web-GIS architecture. Thus, the users of the architecture can easily interact with the digital map, visualise 3D digital cultural heritage models, and explore linked open data from GeoNames and DBpedia platforms, which offer additional information and context related to the selected cultural heritage site as well as external web resources. The architecture was validated by applying it to specific case studies of Australian cultural heritage and seeking expert feedback on the system, its benefits, and scope for improvement in the near future. Numéro de notice : A2021-802 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10100684 Date de publication en ligne : 11/10/2021 En ligne : https://doi.org/10.3390/ijgi10100684 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98854
in ISPRS International journal of geo-information > vol 10 n° 10 (October 2021) . - n° 684[article]
Titre : Applications of Internet of Things Type de document : Monographie Auteurs : Chi-Hua Chen, Éditeur scientifique ; Kuen-Rong Lo, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 162 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-0365-1193-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] géopositionnement
[Termes IGN] informatique en nuage
[Termes IGN] internet des objets
[Termes IGN] service fondé sur la position
[Termes IGN] système de transport intelligent
[Termes IGN] téléphonie mobile
[Termes IGN] trafic routier
[Termes IGN] vitesseRésumé : (auteur) This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al. Note de contenu : 1- Vehicle positioning and speed estimation based on cellular network signals for urban roads
2- A method for traffic congestion clustering judgment based on grey relational analysis
3- Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road
anomaly detection
4- A high-efficiency method of mobile positioning based on commercial vehicle operation data
5- Efficient location privacy-preserving k-anonymity method based on the credible chain
6- Proximity-based asynchronous messaging platform for location-based Internet of
Things service
7- Detection of electronic anklet wearers’ groupings throughout telematics monitoring
8- Camera coverage estimation based on multistage grid subdivisionNuméro de notice : 28650 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-1193-1 En ligne : https://doi.org/10.3390/books978-3-0365-1193-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99766
