PyGIS™ - Open Source Spatial Programming & Remote Sensing#
The globe is now digital. Everything from monitoring deforestation, predicting wildfires, to training autonomous vehicles and tracking uprisings on social media requires you to understand how to leverage location data. This book will introduce you to the methods required for spatial programming. We focus on building your core programming techniques while helping you: leverage spatial data from OSM and the US Census, use satellite imagery, track land-use change, and track social distance during a pandemic, amongst others. We will leverage open source Python packages such as GeoPandas, Rasterio, Sklearn, and Geowombat to better understand our world and help predict its future. Some Python programming experience is required, however the material will be presented in a student-friendly manner and will focus on real-world application.
0 - Get Started in Spatial Python
1 - Spatial Data Types in Python
2 - Nature of Coordinate Systems in Python
3 - Vector Operations in Python
4 - Raster Operations in Python
5 - Accessing OSM & Census Data in Python
6 - Remote Sensing in Python
- Reading/Writing Remote Sensed Images
- Accessing STAC Imagery with GeoWombat
- Configuration manager
- Editing Rasters and Remotely Sensed Data
- Plot Remote Sensed Images
- Remote Sensing Coordinate Reference Systems
- Handle Multiple Remotely Sensed Images
- Band Math & Vegetation Indices
- Raster Data Extraction
- Spatial Prediction using ML in Python
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PyGIS™ is a trademark of Michael Mann. The book’s content is openly licensed under CC BY-NC-SA 4.0, but the PyGIS™ name and logo are not covered by that license. Please review the PyGIS™ Trademark Policy before using the name or logo in your own projects, forks, courses, or products.