Nos dias 23,24 e 25 de agosto do presente ano, levar-se-á a cabo o TERCEIRO CONGRESSO IBEROAMERICANO DE SOLO URBANO na cidade de Curitiba, Brasil, com o tema “O solo na nova agenda urbana”, organizado de maneira conjunta pelo Colégio Mexiquence AC, a Universidade Federal do Paraná, a Universidade Pontifícia Católica do Paraná e a Universidade Positivo.

A Comissão Organizadora da IV Reunião Nordestina de Ciência do Solo já definiu a data do evento, que ocorrerá entre os dias 27 a 30/11/2017 na cidade de Teresina, PI. Segundo o Coordenador do evento, o Dr.

Given the uncertainties generated after the peace agreements in Colombia, we believe that understanding and studying the most relevant transformation agents and potential socio-ecological pathways into the future is a key step to develop pro-active management strategies.

Evento: 15º Congresso de Cadastro Multifinalitário e Gestão Territorial e 3º Encontro de Professores de Cadastro Territorial

Lema/Tema: "Popularização cadastral sob o modelo multifinalitário"

Data: De 7 a 9 de novembro de 2022

Local: Florianópolis, Santa Catarina - Brasil (Híbrido)

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.

NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Large Scale Applications of Machine Learning using Remote Sensing for Building Agriculture Solutions. Remote sensing data is becoming crucial to solve some of the most important environmental problems, especially pertaining to agricultural applications and food security. Participants will become familiar with data format and quality considerations, tools, and techniques to process remote sensing imagery at large scale from publicly available satellite sources, using cloud tools such as AWS S3, Databricks, and Parquet. Additionally, participants will learn how to analyze and train machine learning models for classification using this large source of data to solve environmental problems with a focus on agriculture.