Functions for images and image collections

This section explores the functions provided by the fastcubo package for working with images and image collections in Google Earth Engine (GEE).

Introduction to fastcubo functions 📚

The fastcubo package is designed to facilitate efficient data retrieval and processing from Google Earth Engine (GEE). It provides a set of streamlined functions specifically tailored for handling satellite imagery and geospatial data. The functions in fastcubo allow users to:

1. Query metadata: Functions like query_getPixels_image and query_getPixels_imagecollection enable users to query metadata from GEE, obtaining essential information to retrieve and analyze geospatial data efficiently.

2. Process images: With query_computePixels_image, users can specify complex image processing operations directly in GEE using expressions, enhancing the capability to derive more meaningful insights from satellite data.

3. Download data: The core functions getPixels and computePixels manage the downloading of geospatial data as GeoTIFF files, supporting both individual images and collections, with options for concurrent downloading and recursive handling of large data sets.

Below is a detailed explanation of each function, including their purpose, arguments, and return types, along with examples of their usage.

Main functions 🚀

Here's a quick summary of the main functions available in the fastcubo package:

• query_getPixels_image: Retrieves metadata necessary for downloading individual images based on specific points and parameters.

• query_getPixels_imagecollection: Similar to query_getPixels_image, but focuses on collections of images within a specified date range.

• query_computePixels_image: Allows for more complex queries by using image expressions (e.g., mathematical operations on images) before downloading.

• getPixels and computePixels: The primary functions for downloading images or collections as GeoTIFF files. These functions handle the actual retrieval of data, leveraging metadata queried through the other functions.

By utilizing these functions, users can efficiently manage large-scale geospatial data retrieval and processing tasks, leveraging the full power of Google Earth Engine through a simplified Python interface.