An Introduction to Google Earth Engine

Google Earth Engine (GEE) is a cloud-based platform that offers multi-level geospatial data analysis at a planetary scale. Earth engine provides a data catalog that hosts most of the publicly available remote sensing data sets of Landsat, MODIS, Sentinel, etc., as well as different vector data sets at a regional and global scale that makes the power of GEE to work on large scale geospatial data analysis without hesitating to download data. The GEE Platform offers easy access to high-performance computing resources for processing the big geospatial data sets to global geospatial researchers and scientists That’s why Google Earth Engine is a popular place for researchers and scientists conducting geospatial research. GEE has already made significant contributions in the global research area by fostering and scaling up data processing, reducing the time, cost, and limitations of computational power.

Earth engine provides an Application Programming Interface (API) and an integrated web-based interactive development environment (IDE) in order to access its resources through the internet connection. Earth Engine is free for non-commercial educational, research, and nonprofit use. Users having an internet connection can sign up and log in to the GEE with their institutional accounts to access the user interface and other resources like tutorials, guides, training materials, documentation, etc.

The web-based client interface of GEE provides a JavaScript code editor to write, run, debug, and save scripts for analysis and interactively visualize geospatial data sets. Besides, GEE offers a Python API for python developers to develop a geospatial application benefited by Google Earth Engine. The python API is suited for building a customized geospatial app, running the TensorFlow model to build AI-powered GeoApp, Data Science, Data Mining, and many more. It’s more flexible for developers than web-based JavaScript IDE. It can easily integrate earth engine with other python data visualization libraries like Matplotlib, Folium, etc.

References

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.

Share To