Machine Learning in Earth Engine

Google Earth Engine (GEE) is equipped with built-in tools for conducting machine learning (ML) and statistical analysis on imagery data. To effectively leverage these tools, users must have a foundational understanding of ML concepts, as the application of these tools varies significantly depending on the specific use case and user preferences. It's important to note the distinction between machine learning and deep learning within the context of GEE. While GEE facilitates traditional ML tasks, including supervised and unsupervised classification, as well as regression, it does not natively support deep learning frameworks like PyTorch and TensorFlow. However, GEE can be used to preprocess and extract features from imagery, which can then be fed into external deep learning models.

GEE has powerful capabilities in pixel characterization and aggregation, processes for which machine learning (ML) techniques are not only helpful but essential. GEE supports a range of ML algorithms, such as Random Forest and Support Vector Machines (SVM), that are instrumental in analyzing and interpreting satellite imagery and raster data. These algorithms enable users to classify pixels, identify patterns, and aggregate data across vast geographic areas efficiently. This allows for the extraction of meaningful insights from complex environmental data, facilitating a wide array of applications from land cover classification to change detection and beyond.