Data Structures and Methods
Understanding the data types available in Google Earth Engine (GEE) and how to work with them is crucial for effective analysis. This section will introduce the various data structures you'll encounter in GEE and outline the foundational operations for analyzing this data. Below, each bullet point includes a link to the GEE documentation, which is an invaluable resource. Familiarizing yourself with these key concepts early on will significantly enhance your proficiency with the platform and speed up the development process.
Summary Descriptions
A thorough review of all of the data types can be found on the Google Earth documentation page: Intro to Data
- Represents a single raster image, consisting of pixels with associated values.
- A collection or sequence of images sharing common attributes, essentially forming a temporal or thematic stack.
- Vector data that includes points, lines, and polygons. Geometries can be created within Earth Engine or imported from external sources.
- A specific geometry paired with associated attributes. For example, a point geometry representing the city of Paris with its name and population as attributes.
- A collection of features that share a common theme, such as a set of points representing the capitals of all countries.
- Methods for statistical computation and data aggregation across various dimensions, such as spatial areas, time periods, and data bands. An example is calculating the average pixel value within each neighborhood polygon.
- Methods for merging datasets (either Image or Feature collections) based on shared attributes like time or location.
- A data structure supporting multi-dimensional analysis, offering flexibility at the cost of potential inefficiency in some operations.