Introduction#
GeoDataCubes are an efficient way to manage and analyze spatiotemporal datasets, particularly for Earth observation, geospatial analysis and remote sensing. The most commonly used tools and libraries in working with GeoDataCubes often involve handling multidimensional arrays, geospatial data and integrating with cloud computing.
Here are the key tools and libraries frequently used:
Geospatial Data Abstraction Library (GDAL) is an open-source library designed to read, write, process, and transform geospatial data across various formats. It supports a vast array of raster and vector formats, making it a powerful tool for geospatial data management, transformation, and analysis.
Rasterio is a powerful, open-source Python library designed for reading, writing and manipulating geospatial raster data. Built on top of the Geospatial Data Abstraction Library (GDAL), Rasterio provides a high-level interface that makes working with geospatial rasters more intuitive and Pythonic. It is widely used by geospatial analysts, remote sensing experts, and developers for handling raster datasets such as satellite imagery, digital elevation models (DEMs) and other grid-based geospatial data.
Xarray is an open-source Python library designed to work with multi-dimensional arrays, particularly those with labeled dimensions. Xarray extends the capabilities of NumPy, making it easier to manipulate, analyze and visualize complex multi-dimensional datasets.