Výsledky vyhledávání - "Pierre Lepetit"

Using Deep Learning for Restoration of Precipitation Echoes in Radar Data

Autor: Cécile Mallet, Yvon Lemaître, Laurent Barthès, Nicolas Viltard, Lucie Rottner, Camille Ly, Pierre Lepetit

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2022, 60, pp.5100914. ⟨10.1109/TGRS.2021.3052582⟩

International audience; Raw data issued from meteorological radars are often corrupted by unwanted signals generically called clutter. Hills, tall buildings, atmospheric turbulence, birds, and insects yield patterns that complicate the interpretation

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2442bf521839f9dc3df51a7b54ad87bc
https://hal-insu.archives-ouvertes.fr/insu-03151106

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RETRIEVING RAIN RATES FROM SPACE BORNE MICROWAVE SENSORS USING U-NETS

Autor: Cécile Mallet, Pierre Lepetit, Nicolas Viltard, Audrey Martini, Laurent Barthès

Publikováno v: ACM International Conference Proceedings Series
Climate Informatics 2020. 10th International Conference
Climate Informatics 2020. 10th International Conference, Sep 2020, Oxford, United Kingdom
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International audience; Despite a lot of progress over the last decades, rain retrieval from spaceborne measurement has been a challenge since the first launch of a passive microwave radiometers on one of the NOAA Defense Meteorological satellites in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86e1e5400c0071a30886f513a84ff23a
https://hal-insu.archives-ouvertes.fr/insu-02894942

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Learning to compare visibility on webcam images

Autor: Nicolas Viltard, Cécile Mallet, Pierre Lepetit, Laurent Barthès

Publikováno v: Sep 2020, 2020
Climate Informatics 2020. 10th International Conference on Climate Informatics
Climate Informatics 2020. 10th International Conference on Climate Informatics, Sep 2020, Oxford, United Kingdom
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CI

International audience; From the beginning of the 2000’s, cameras are considered as an interesting source of opportunistic meteorological data. This short study deals with the comparison of meteorological visibility between images.A new dataset has

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b6e0e62ed310273ce9b3a1639c4dbcb
https://hal.archives-ouvertes.fr/hal-02926386/document

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Deep Learning for image based weather estimation: a focus on the snow

Autor: Laurent Barthès, Pierre Lepetit, Cécile Mallet

The road traffic is highly sensitive to weather conditions. Accumulation of snow on the road can cause important safety problems. But road conditions monitoring is as hard as critical: in mid-latitude countries, on the one hand, the spatial variabili

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6f0cc149dc8cd61e780962c30529a108
https://doi.org/10.5194/egusphere-egu2020-19802

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Deep Learning pour le traitement des Images Radar

Autor: Pierre Lepetit, Laurent Barthès, Camille Ly, cécile mallet, Nicolas Viltard, Yvon Lemaître, Valérie Vogt

Publikováno v: Journées R & D-Intelligence Artificielle-Centre international de Conférences de Météo France
Journées R & D-Intelligence Artificielle-Centre international de Conférences de Météo France, Jun 2019, Toulouse, France
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International audience; La donnée radar est essentielle pour localiser les précipitations et en mesurer l’intensité. Au début de la chaîne de traitement, ces données contiennent en mélange des échos de précipitations et des signaux parasit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::cfde068a88b629bc6e1f37975dbe61e1
https://hal-insu.archives-ouvertes.fr/insu-02359480

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Survey of Wintering Eurasian woodcock in Western Europe

Publikováno v: Proceedings of the Eleventh American Woodcock Symposium.

The Eurasian woodcock (Scolopax rusticola) is a mostly migratory wader (Charadriiformes) that is broadly distributed across the Palearctic. This species winters in or migrates through all European countries and is an important quarry species in many

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b755e8a53a8a77002bd0a90e9993cedd
https://doi.org/10.24926/aws.0131

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Radar data cleansing with deep learning

Autor: Pierre Lepetit, Laurent Barthès, cécile mallet

Publikováno v: Journée ‘extraction d’attributs et apprentissage pour l’analyse des images de télédétection’
Journée ‘extraction d’attributs et apprentissage pour l’analyse des images de télédétection’, Oct 2018, Paris, France
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International audience; Quality of radar data is critical for climatology, analysis and forecast. Actual algorithms present some drawbacks. For example, outer edges of rainy areas are often mistaken. Typing and cleansing of radar echoes is an ongoing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3cee084985accb2d16f65d2ef206add0
https://insu.hal.science/insu-01940723

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Filtrage des données radar par deep learning

Autor: Pierre Lepetit, Laurent Barthès, cécile mallet, yvon Lemaître

Publikováno v: Radars : Technologies, Méthodologies et Applications R-TMA-2018
Radars : Technologies, Méthodologies et Applications R-TMA-2018, Dec 2018, Guyancourt, France
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International audience; Depuis 2016, les données radars du réseau Aramis (Météo-France) sont accessibles. Ce sont des données brutes : un nettoyage des signaux parasites (échos fixes, ”interférences”) doit être effectué. Pour traiter le

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f045f89cae86b3ae64f93d65d57e2310
https://hal-insu.archives-ouvertes.fr/insu-02083284

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