Global Earth Explorer (GEE) is a powerful platform designed to facilitate remote sensing data analysis, providing users with a range of tools and features to perform complex tasks. The platform offers three main modules: Data Retrieval, Processing and Analysis, and Machine Learning. Within these modules, users can access a vast collection of remote sensing datasets, apply various algorithms for data processing, and train machine learning models for tasks such as image classification and object detection.
Data Retrieval Module
The Data Retrieval module allows users to quickly obtain query results by selecting diverse data sources and specifying retrieval criteria like temporal, spatial, and cloud coverage parameters. Users can directly download the source data, making it easy to access and analyze remote sensing data.
Processing and Analysis Module
In the Processing and Analysis module, users can perform tasks through interactive methods within the web-based user interface or utilize Python programming language to invoke API interfaces and write code for embedded code editors. The platform offers a diverse array of atomic functions for computations, including image fusion, change detection, and feature extraction.
Machine Learning Module
The Machine Learning module provides users with various machine learning algorithms for tasks like image classification and object detection. Although GEE offers a rich collection of remote sensing datasets and tools, data retrieval requires users to understand the structure of the datasets and query syntax, as well as use code to retrieve the desired data. This can be challenging for users without programming skills.
Spatial Aggregation Statistics
The spatial aggregation statistics process is complex, involving the consolidation of intermediate results from parallel computations for statistical aggregation. The platform provides aggregation functions primarily for regional statistics, attribute aggregation, and image sampling for training a classifier. Although distributed aggregation involves a complex computation process, users do not need to understand any distributed computing rules.
In conclusion, GEE is an extensive tool that simplifies remote sensing data analysis by providing users with a comprehensive platform for data retrieval, processing, and machine learning tasks. By leveraging its robust features, users can easily access, analyze, and make use of remote sensing data, even if they lack programming skills. With GEE, users can streamline their work process, save time, and gain valuable insights from remote sensing data more efficiently than ever before.
