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.
Secure land tenure in rural landscapes is widely recognized as an essential foundation for achieving a range of economic development goals. However, forest areas in low and middle-income countries face particular challenges in strengthening the security of land and resource tenure. Forest peoples are often among the poorest and most politically marginalized communities in their national contexts, and their tenure systems are often based on customary, collective rights that have insufficient formal legal protection.
Secure land tenure in rural landscapes is widely recognized as an essential foundation for achieving a range of economic development goals. However, forest areas in low and middle-income countries face particular challenges in strengthening the security of land and resource tenure. Forest peoples are often among the poorest and most politically marginalized communities in their national contexts, and their tenure systems are often based on customary, collective rights that have insufficient formal legal protection.
Secure land tenure in rural landscapes is widely recognized as an essential foundation for achieving a range of economic development goals. However, forest areas in low and middle-income countries face particular challenges in strengthening the security of land and resource tenure. Forest peoples are often among the poorest and most politically marginalized communities in their national contexts, and their tenure systems are often based on customary, collective rights that have insufficient formal legal protection.
