While we work together to transform the way the world produces, consumes and thinks about food, a group of concerned actors has partnered to co-lead a series of Independent Dialogues ahead of the upcoming 2021 United Nations Food Systems Summit. The Dialogues will promote the centrality of land rights in building sustainable food systems.
By: Faiz Zainudin
Date: 4 December 2016
Source: Freemalaysiatoday
When the US housing bubble burst in late 2008, it dragged major banks into liquidation and destabilised financial systems worldwide. A long, era-defining recession ensued, ushering bank bailouts, currency crises and austerity measures. Meanwhile, China’s skyrocketing industrial production was shifting global economic power.
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.
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new online advanced webinar series: Crop Mapping using Synthetic Aperture Radar (SAR) and Optical Remote Sensing. This three-part training, presented in English and Spanish, is open to the public and builds on previous ARSET agricultural trainings. Here we present more advanced radar remote sensing techniques using polarimetry and a canopy structure dynamic model to monitor crop growth. The training will also cover methods that use machine learning methods to classify crop type using a time series of Sentinel-1 & Sentinel-2 imagery. This series will include practical exercises using the Sentinel Application Platform (SNAP) and Python code written in Python Jupyter Notebooks, a web-based interactive development environment for scientific computing and machine learning.
