All Tutorial Notebooks

This notebook leverages a Single Shot Detector (SSD) model to detect buildings in Las Vegas, NV.
An example of comparing changes in imagery over time in Dubai. This notebook explores ways to collect several images, compute water indices from each, extract vector features, and compare changes to land area over time.
Animating imagery over time from a list of CatalogIDs
This Notebook demonstrates a simple workflow for identifying standing water in an image, extracting and rasterizing roads vectors from OpenStreetMap, and assessing the extent of road flooding.
An example showing how to pass every pixel of an image through a user defined method
Using SWIR data to explore and extract the location of active fires in Southern California.
A first beginner guide to notebooks. What is a notebook? How do you enter and use text and code? How do you manage output from code?
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.
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.
Exploring ways to leverage DigitalGlobe imagery and OSM data for training ML models