Once the new training data set is ready, compute a pixel-based land-cover classification using some kind of machine learning algorithm.
After completing this worksheet you should be able to perform a straight forward machine learning model training workflow using the gpm package.
As always, please add these entries to your today's learning log in the beginning of your Rmd file you will use to push to your GitHub classroom.
As today's special, please complete the following assignment:
Please compute a sufficiently trained random forest model for predicting your land-cover classes using the training data set derived from. Use the gpm package for that. Once your model has been finished, compute the Kappa index of agreement and add it as a comment in the last line of your script.
Please update (i.e. commit) your R script publish (i.e. push) it to the GitHub classroom.
If you want to install a package directly from GitHub, type:
The gpm package is work in progress. However, the major functionality required for this task is available. In order to ease things up, the following script already shows the workflow required. Have a look at E07-1: Land-cover classification with R.
The major steps required are:
See the script on GitHub.