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Image Science Tutorial Notebooks

Learn how to find satellite imagery and pull it in to the GBDX system for display in a notebook.
This notebook is the third in a series of beginner tutorial to introduce news users to Jupyter Notebooks, working with satellite imagery and performing basic analytics. Specifically this notebook introduces the concepts of multispectral bands found in satellite imagery and how they can be used for analytics using "band math".
This notebook is a detailed introduction to ways to access DG imagery via the gbdxtools python package.
This notebooks shows how to work with various types of AOIs for indexing and cropping imagery via gbdxtools.
An introduction to various image classes supported by gbdxtools
A notebook that shows how to histogram match RDA imagery that is native to GBDX Notebooks to DigitalGlobe's Maps API using rio_hist. This method will help provide consistent color balanced imagery that is aesthetically pleasing for viewing.
In this tutorial we'll cover how to find SWIR imagery and use it for creating a Normalized Difference Built-Up Index for analysis of Central Park in Manhattan. This work includes discussion of the difference between VNIR and SWIR data as well as using downsampling techniques to enable the use of band math on imagery products with different resolutions.
This notebook provides a basic introduction to using Dask with satellite imagery. It covers a brief overview of Dask arrays, chunks and map_blocks to scale out an NDVI analysis for a entire strip of imagery over Sydney, Australia.