This interdisciplinary program provides an overview of pressing issues related to agricultural investments, as well as an introduction to relevant practical skills.
The World Urban Forum (WUF) was established in 2001 by the United Nations to address one of the most pressing issues facing the world today: rapid urbanization and its impact on communities, cities, economies, climate change and policies. Convened by UN-Habitat, the Forum is a high level, open and inclusive platform for addressing the challenges of sustainable urbanization.
Cities around the world face numerous environmental hazards, such as extreme heat events, landslides, pollution, and flooding. Cities must monitor and address these hazards to reduce risks to, and enhance resilience of, their residents to climate change impacts.
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.
