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XXXVI Congresso Brasileiro de Ciência do Solo

30 Julho 2017 - 14 Agosto 2017
Belém - Pará- Brasil
Universidade Federal de Viçosa
Belém - Pará- Brasil
Brazil

O Congresso Brasileiro de Ciência do Solo (CBCS), chega a sua 36ª edição, cuja realização será no coração da Amazônia, Belém do Pará, pela Universidade Federal Rural da Amazônia e instituições parceiras, no período de 30 de julho a 4 de agosto de 2017.

International conference: Forestry science for sustainable development

29 Setembro 2022 - 30 Setembro 2022

To mark the thirtieth anniversary of the Faculty of Forestry, University of Banja Luka and 30 years of PFE "Šume Republike Srpske" ad Sokolac, with the support of the Food and Agriculture Organization of the United Nations and The International Union for Conservation of Nature , is organizing an international scientific conference FORS²D under the theme ""Perspectives of forestry and related se

International Union for Conservation of Nature

LDC 2022 : Les pressions mondiales sur les terres locales : Perspectives du Sud

07 Dezembro 2022 - 09 Dezembro 2022

Le thème de la sixième édition de l'ILDC en 2022 est "Global Pulls on Local Lands : Southern Perspectives". L'objectif est de poursuivre et d'élargir la portée des échanges Sud-Sud autour des conversations et des coopérations sur les terres, qui ont débuté lors du dernier épisode, tandis que l'accent des délibérations continuera d'être mis sur l'Inde.

NRMC

Fundamentals of Machine Learning for Earth Science

19 Abril 2023 - 03 Maio 2023

NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new online introductory webinar series: Fundamentals of Machine Learning for Earth Science. This three-part training, presented in English and Spanish, is open to the public and will provide attendees an overview of machine learning in regards to Earth Science, and how to apply these algorithms and techniques to remote sensing data in a meaningful way. Attendees will also be provided with end-to-end case study examples for generating a simple random forest model for land cover classification from optical remote sensing. We will also present additional case studies to apply the presented workflows using additional NASA data.