Introduction to Google Earth Engine

Announced in 2010, the cloud-based platform called Google Earth Engine (GEE) provides a new way to conduct geospatial analysis. As a cloud-based platform, GEE has three important advantages over conventional geo-analysis techniques: 1) easy to access vast amounts of large-scale geospatial datasets; 2) easy to utilize Google’s supercomputing/AI capacities (e.g. global-scale computation, deep learning); and 3) easy to publish your results online as interactive maps. When using GEE, there is no need to download and organize the datasets, to upload the data and scripts to super-computers, or to export and download the results as maps. All you need to do is to write a script on the online code-editing platform, and GEE will do the rest for you. However, these amazing advantages do not come without a (minor) cost: GEE is significantly different from conventional programming languages. For instance, loops are limited/discouraged; math operators are implemented through object methods (e.g. “image1.add(image2)” instead of “image1+image2”); object/function names are uncommon/unfamiliar (e.g. collection, filter, reducer, map).

This talk will briefly introduce the unique features of GEE and give you some tips to navigate the platform to better utilize all its benefits. Learning GEE also has the potential benefits beyond this particular tool, as more and more cloud-based geospatial platforms are emerging, such as Earth on AWS by Amazon, ArcGIS online by ESRI. These new cloud-based platforms also share some common advantages and challenges with GEE. Early sign-up of a GEE account is encouraged but not necessary.

Kangning (Ken) Huang is an ASP postdoctoral fellow at RAL, working on the urban heat-energy-water nexus. He received his Ph.D. in Urbanization and Global Change from Yale University, School of Forestry and Environmental Studies, his M.S. and B.S. in Geographic Information Science from Sun Yat-sen University, School of Geography and Planning. He used to be an urban scientist in a forestry school, studying forests of buildings and people, not just trees and animals. Now he is a geographer in a meteorology institute, studying the interactions between buildings, trees, people and climate change. He used GEE extensively in his doctoral dissertation to build a global urban heat island model.