Advanced Remote Sensing

An example showing how to pass every pixel of an image through a user defined method
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.
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.
This Notebook illustrates the idea of adjusting the RGB color values of a CatalogImage to match the colors in the "browse" (aka, "thumbnail") version of the same image. Normally, the RGB colors of a CatalogImage that are displayed when the .plot() method is called are produced by an automated algorithm that simply clips out the extreme light and dark colors, and then interpolates the values to the normal 0-255 scale. On the other hand, browse imagery is color adjusted by a human operator at DigitalGlobe, and therefore can be better suited for special conditions (e.g., very dark/shadowy imagery such as when the image was captured at a low sun elevation angle, or very bright imagery, such as a snowy scene). Although browse imagery is much lower spatial resolution than the CatalogImagery, the color values can still be used to produce imagery that is more suitable for some use cases.
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.
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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.
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.