Earth imaging satellites, just like our computers, are shrinking and becoming more ubiquitous than ever before. It is now possible to obtain open satellite data on a daily if not weekly basis and for this data to be put to work; helping us better understand our planet and quickly respond to disaster situations.
In this talk, we will work through a jupyter notebook that covers the satellite data ecosystem and the python tools that can be used to sift through and analyze that data. Topics include python tools for using Open Street Maps data, the Geospatial Data Abstraction Library (GDAL), and OpenCV and NumPy for image processing. This talk is intended for novice and intermediate python developers who are interested in using data science and satellite imagery for social good and fundamental scientific research.
- Slides(pdf for download): An overview of satellites and satellite terminology, the basics of remote sensing, sources of free satellite imagery, and tools for processing and analyzing images.
- Requirements: A list of Python libraries you'll need for this project.
- Data: links to public and commercial sources for free satellite imagery.
- The Basics: Setting up your development environment; making a slippy map and defining an area of interest on the map; searching, filtering, and downloading satellite imagery that intersects with the area of interest.
- Open Street Map Example: Using Open Street Map and our downloaded imagery to visualize and analyze the "greenness" of parks in Portland.
- Making Movies: Wayfinding from park to park and creating a masterpiece movie of your "Great Portland Park Tour of 2017."