91 to 100 of 133 Results
Sautot, Lucile; Chraibi, Eric; Fize, Jacques; Lentschat, Martin; Cernesson, Flavie; Journaux, Ludovic; Peillet Sébastien, 2019, "Grand Débat National - Données enrichies", https://doi.org/10.18167/DVN1/ZRTW02, CIRAD Dataverse, V2
[FR] Ces données agrègent : - les contributions libres déposées sur la plateforme en ligne http://granddebat.fr relatives à la question de la transition écologique, - les annotations de ces contributions réalisées sur la plateforme en ligne https://grandeannotation.fr, - l'extrac... |
Rumiano, Florent; Miguel, Eve; Valls-Fox, Hugo; Chamaillé-Jammes, Simon; Caron, Alexandre; Tran, Annelise, 2020, "Monthly surface water maps, Hwange National Park, Zimbabwe, 2018", https://doi.org/10.18167/DVN1/KPSYME, CIRAD Dataverse, V1
Monthly surface water maps, Hwange National Park, Zimbabwe, derived from Sentinel-2 images (2018) TEMPO project: Remote sensing and Spatial modelling of animal mobility - Application to the study of wildlife/livestock contacts and risk of pathogen transmission http://tempo.cirad.... |
Tran, Annelise; Mangeas, Morgan; Demarchi, Marie; Roux, Emmanuel; Degenne, Pascal; Herbreteau, Vincent; Dehecq, Jean-Sébastien, 2019, "ALBORUN - Modèle de la dynamique de population des moustiques Aedes albopictus à la Réunion", https://doi.org/10.18167/DVN1/XF2I3L, CIRAD Dataverse, V3
[FR] ALBORUN est un modèle de la dynamique de population des moustiques Aedes albopictus, développé à l'Ile de la Réunion. Il a été développé avec le langage métier Ocelet, langage dédié à la modélisation de dynamiques spatiales doté d'un environnement de développement de modèles... |
Dupuy, Stéphane; Gaetano, Raffaele, 2019, "Reunion island - 2017, Land cover map (Pleiades)", https://doi.org/10.18167/DVN1/RTAEHK, CIRAD Dataverse, V3
As part of THEIA (the French Data and Services center for continental surfaces) CIRAD's TETIS research unit is developing an automated mapping method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing. The methodolog... |
Lebourgeois, Valentine; Dupuy, Stéphane; Vintrou, Élodie; Ameline, Maël; Butler, Suzanne; Bégué, Agnès, 2019, "Antsirabe, Madagascar - 2015, land cover map", https://doi.org/10.18167/DVN1/8T3UJE, CIRAD Dataverse, V2
This work was conducted before the launch of ESA Sentinel-2 mission, which images are particularly adapted to crop monitoring and characterization thanks to their high spatial (10 – 60m) and temporal (5 days) resolutions. We worked with Landsat-8 and Spot5 images to create a time... |
Laurence, Defrise; Andriamanga, Valérie; Rasoamalala, Eloise; Dupuy, Stéphane; Burnod, Perrine, 2019, "Antananarivo - Madagascar - 2017, Land use reference spatial database", https://doi.org/10.18167/DVN1/5TZOOW, CIRAD Dataverse, V1
This vector format database (ESRI shape format) is made up of field surveys carried out during the end of the 2017 rainy season (March to April) which corresponds to the peak of the growing season. Study area is Antananarivo city, capital of Madagascar. GPS points were collected... |
Dupuy, Stéphane, 2019, "Reunion Island - 2017, reference spatial database", https://doi.org/10.18167/DVN1/TOARDN, CIRAD Dataverse, V3
The reference spatial database for 2017 is composed of 6256 plots. We use it to calculate a land use map from satellite images.It is organized according to a nomenclature offering 3 levels of precision. We randomly selected 20% of the plots in each class to build a validation dat... |
Dupuy, Stéphane; Gaetano, Raffaele, 2019, "Reunion island - 2018, Land cover map (Pleiades) - 0.5m", https://doi.org/10.18167/DVN1/WKAJZO, CIRAD Dataverse, V1
CIRAD's TETIS research unit is developing an automated mapping method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing. The methodology uses jointly a Very High Spatial Resolution image (Spot6/7 or Pleiades) and on... |
Dupuy, Stéphane, 2019, "Reunion Island - 2018, learning spatial database", https://doi.org/10.18167/DVN1/8QJABD, CIRAD Dataverse, V1
The spatial learning database for 2018 contains 5620 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. The sources and techniques used to build the database by land use groups are described below: For a... |
15 oct. 2019
Londoño Villegas, Mónica María; Le Mezo, Lionel; Dupuy, Stéphane, 2019, "Base de données spatiale pour l'apprentissage - 2016-2017- La Réunion", https://doi.org/10.18167/DVN1/HM9QNL, CIRAD Dataverse, V1
La base de données pour la période 2016-2017, est constituée de 2650 parcelles. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'image satellites. Nous avons sélectionné de façon aléatoire 20% des parcelles de chaque classe pour constituer une base de donné... |
