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Unlocking the potential of cloud computing and Earth observation

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GEE introduces Bhutan’s government agencies to the possibilities of enhanced data analysis and visualization

Unlocking the potential of cloud computing and Earth observation

Advances in cloud computing technology and internet penetration are making Earth observation information accessible to a wider demographic. Google Earth Engine (GEE) – Google’s cloud-based platform for Earth science data and analysis – stores, organizes, and provides access to a wide variety of satellite images and geospatial datasets. It also offers global-scale environmental data analysis capabilities. The GEE platform is being widely used for scientific analysis and visualization of Earth observation and geospatial datasets.

Recognizing the platform’s high-performance computing capabilities for processing and analysing large datasets, the National Center for Hydrology and Meteorology (NCHM) in Bhutan collaborated with ICIMOD’s Regional Database System (RDS) Initiative to organize a five-day training workshop on GEE in June 2019.

The training brought together 20 professionals from various government agencies in the country. It provided an overview of the GEE platform and multiple datasets hosted on the platform and included hands-on exercises on GEE JavaScript application programming interface for viewing, processing, and analysing Earth observation and geospatial datasets. It also showcased different science applications such as the resource accounting tool and wheat mapping application being developed at ICIMOD that make use of the platform’s scalable cloud computing architecture and suite of datasets.

ICIMOD conducted the first training on the GEE platform in Nepal in May 2017 under its SERVIR Hindu Kush Himalaya Initiative in collaboration with the GEE outreach team. It has since carried out multiple trainings in Nepal and Bangladesh.

The training showcased different science applications being developed at ICIMOD that make use of the GEE platform’s scalable cloud computing architecture and suite of datasets.

Chapter 4

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