Combining Ground and Remotely Sensed Data
A deep dive into techniques to combine ground-based data and remotely sensed data for training and validation of machine learning algorithms used in geospatial impact evaluations of climate sensitive agricultural interventions.
A deep dive into techniques to combine ground-based data and remotely sensed data for training and validation of machine learning algorithms used in geospatial impact evaluations of climate sensitive agricultural interventions.
Course Resources
Resources
Slides (PDF)
Lesson 5 Slides: Combining Ground and Remotely Sensed Data
Code (GitHub)
https://github.com/aiddata/gie-toolkit
This GitHub repo contains code demonstrations for the entire course. This code includes examples of analysis in Stata as well as utilizing remote sensing and earth observation data through Google Earth Engine.