Advanced Remote Sensing

In this tutorial by Sean Gorman 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.
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A notebook by Pramukta Kumar 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.
This notebook builds on color matching concepts discussed in Color Adjustments for RDA Imagery Part 1. Written by Marc Pfister (marc.pfister@digitalglobe.com)
Notebook by: Chris Helm chris.helm@digitalglobe.com An example showing how to pass every pixel of an image through a user defined method
Notebook by: Mike Gleason (michael.gleason@digitalglobe.com)
In this GBDX Notebook, we demonstrate how a combination of DigitalGlobe products can be used to start mapping the airspace for drones and unmanned aerial vehicles (UAVs) in urban environments. Originally written by Mike Gleason (michael.gleason@digitalglobe.com) with updates by Chloé Hampton (chloe.hampton@digitalglobe.com)
Machine learning burn detection > NBR! In this Notebook, we propose a binary classification method to identify destroyed and unharmed buildings and vegetation using high resolution satellite imagery, image processing, remote sensing, and machine learning.
In the previous Notebook, we demonstrated two different strategies for scaling our coastline extraction algorithm to run over a full image strip. The result was a Python function that we can point at any (coastal) image and efficiently delineate the coastlines, either in downsampled resolution or full resolution. The execution of that function was conducted within our Notebook, which is convenient for hands-on development and testing, but not necessarily ideal for production-level analysis. For example, what if we wanted to run the analysis over multiple strips at the same time? Or set up a recurring job that runs on every new strip that comes in over a given area? Handling production of coastline features from the Notebook in these cases would be relatively cumbersome. At this point in the development process, it's important to consider moving our algorithm from the Notebook environment to the GBDX Platform by deploying it as a GBDX Task. A GBDX Task is simply a stored version of an algorithm (or tool, or methodology, or really any operation) that we can execute on the GBDX Platform. The code itself isn't run locally; instead, it runs as part of a Workflow in the cloud. This means we can execute the same Task against multiple images all at the same time: each will be kicked off as a separate, parallel workflow, without being constrained to the computational limits of a single machine (or GBDX Notebook Kernel). In this Notebook, we provide a walkthrough of how to deploy our coastline extraction algorithm as a GBDX Task, using some helpful tools built right into the GBDX Notebooks interface. We also do a quick test of our new task and review the results to make sure it works.