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 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 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.
The study was commissioned to the KIT Royal Tropical Institute in July 2017 by the Land Dialogue, with financial support from the Dutch Government. The objective is to provide insight and guidance into the relevance of land governance as a possible priority theme t o be considered in the process of the International Responsible Business Conduct (IRBC) Agreements.
The study was commissioned to the KIT Royal Tropical Institute in July 2017 by the Land Dialogue, with financial support from the Dutch Government. The objective is to provide insight and guidance into the relevance of land governance as a possible priority theme t o be considered in the process of the International Responsible Business Conduct (IRBC) Agreements.
The study was commissioned to the KIT Royal Tropical Institute in July 2017 by the Land Dialogue, with financial support from the Dutch Government. The objective is to provide insight and guidance into the relevance of land governance as a possible priority theme t o be considered in the process of the International Responsible Business Conduct (IRBC) Agreements.
