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Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery / Thomas A. Lake in Remote sensing in ecology and conservation, vol 8 n° 6 (December 2022)
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Titre : Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery Type de document : Article/Communication Auteurs : Thomas A. Lake, Auteur ; Ryan D. Briscoe Runquist, Auteur ; David A. Moeller, Auteur Année de publication : 2022 Article en page(s) : pp 875 - 889 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] espèce exotique envahissante
[Termes IGN] image Worldview
[Termes IGN] PlanetScope
[Termes IGN] série temporelleRésumé : (auteur) Effective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge (Euphorbia virgata), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview-2 and Planetscope satellites. Worldview-2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short-term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview-2 model. Across models, most false-positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid-season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red-edge and near-infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large-scale, remote and data-sparse areas. Numéro de notice : A2023-033 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.288 En ligne : https://doi.org/10.1002/rse2.288 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102295
in Remote sensing in ecology and conservation > vol 8 n° 6 (December 2022) . - pp 875 - 889[article]Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])
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Titre : Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images Type de document : Article/Communication Auteurs : Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur Année de publication : 2022 Article en page(s) : pp 5433 - 5452 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition d'images
[Termes IGN] apprentissage profond
[Termes IGN] approche participative
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur (variable spectrale)
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] étiquette
[Termes IGN] image multibande
[Termes IGN] OpenStreetMap
[Termes IGN] pixel
[Termes IGN] plateforme collaborative
[Termes IGN] texture d'image
[Termes IGN] WorldviewRésumé : (auteur) In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved. Numéro de notice : A2022-702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1917006 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1917006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101561
in Geocarto international > vol 37 n° 18 [01/09/2022] . - pp 5433 - 5452[article]Landsat, le programme fête ses cinquante ans / Laurent Polidori in Géomètre, n° 2205 (septembre 2022)
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Titre : Landsat, le programme fête ses cinquante ans Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 19-19 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Technologies spatiales
[Termes IGN] Landsat
[Termes IGN] programme spatial
[Termes IGN] satellite d'observation de la Terre
[Termes IGN] télédétection spatialeRésumé : (Auteur) Le programme de la Nasa propose un demi-siècle d’images de télédétection, les premières pouvant être considérées comme un « état zéro» environnemental de la planète. Numéro de notice : A2022-670 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101492
in Géomètre > n° 2205 (septembre 2022) . - pp 19-19[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2022091 SL Revue Centre de documentation Revues en salle Disponible Validation of a corner reflector installation at Côte d’Azur multi-technique geodetic observatory / Xavier Collilieux in Advances in space research, vol 70 n° 2 (15 July 2022)
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Titre : Validation of a corner reflector installation at Côte d’Azur multi-technique geodetic observatory Type de document : Article/Communication Auteurs : Xavier Collilieux , Auteur ; Clément Courde, Auteur ; Bénédicte Fruneau
, Auteur ; Mourad Aimar, Auteur ; Guillaume Schmidt, Auteur ; Isabelle Delprat, Auteur ; Marie-Amélie Defresne, Auteur ; Damien Pesce, Auteur ; Fabien Bergerault, Auteur ; Guy Wöppelmann
, Auteur
Année de publication : 2022 Projets : Université de Paris / Clerici, Christine Article en page(s) : pp 360 - 370 Note générale : bibliographie
This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. It was supported by the Programme National GRAM INSAROME of CNRS/INSU with INP and IN2P3 co-funded by CNES but also by BQR-OCA.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] coin réflecteur
[Termes IGN] constellation Sentinel
[Termes IGN] Global Geodetic Observing System
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] observatoire astronomiqueRésumé : (auteur) We present the procedure we followed to design an artificial corner reflector (CR) at the Calern site of Côte d’Azur Observatory (France). Although still few in number, such reflectors are an integral part of the Global Geodetic Observing System (GGOS) infrastructure. They can be used as a stable radar target in SAR images to connect local InSAR deformation maps to the global Terrestrial Reference Frame and for SAR absolute determination. During a test phase, the orientation of the CR was changed in order to be aligned toward all possible orbits of Sentinel-1A/1B satellites. On the different SAR images, the CR exhibits a high backscattering signal, and provides a Signal-to-Clutter Ratio larger than 26 dB. Since December 2018, the CR is specifically oriented toward the relative orbit 88. It is clearly detected as a PS in our InSAR analyses and as expected, the standard deviation of displacement measured on the CR is lower than on surrounding PS. A first local survey was performed to locate precisely this CR with respect to the existing geodetic instruments and annual campaigns have been carried out since then to insure its stability over time. Numéro de notice : A2022-337 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2022.04.050 Date de publication en ligne : 29/04/2022 En ligne : https://doi.org/10.1016/j.asr.2022.04.050 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100694
in Advances in space research > vol 70 n° 2 (15 July 2022) . - pp 360 - 370[article]
[article]
Titre : Littoraux sous double surveillance Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 23 - 23 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Litto3D
[Termes IGN] marée océanique
[Termes IGN] montée du niveau de la mer
[Termes IGN] satellite d'observation de la mer
[Termes IGN] satellite d'observation de la Terre
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (Auteur) Concentré d’enjeux écologiques et sociaux, le littoral est sous la surveillance permanente des satellites. Mais cet objet complexe et changeant se dérobe parfois à l’observation. Numéro de notice : A2022-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101295
in Géomètre > n° 2204 (juillet-août 2022) . - pp 23 - 23[article]Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 063-2022071 SL Revue Centre de documentation Revues en salle En circulation
Exclu du prêtPermalinkEfficient variance component estimation for large-scale least-squares problems in satellite geodesy / Yufeng Nie in Journal of geodesy, vol 96 n° 2 (February 2022)
PermalinkPermalinkPermalinkPermalinkComparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)
PermalinkPermalinkChina’s high-resolution optical remote sensing satellites and their mapping applications / Deren Li in Geo-spatial Information Science, vol 24 n° 1 (March 2021)
PermalinkPermalinkInclusion of GPS clock estimates for satellites Sentinel-3A/3B in DORIS geodetic solutions / Petr Štěpánek in Journal of geodesy, vol 94 n° 12 (December 2020)
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