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I have been attempting to use random forests to classify high resolution aerial imagery. Part one of this post series was my first attempt. The aerial imagery dataset that I am working on is made up of many ortho tiles that I need to classify into vegetation categories. The first attempt was to classify vegetation on one tile. This note documents classifying vegetation across tiles.

A look at avalanches in Colorado. Please not I'm not an avalanche expert, so please take these interpretations with a healthy dose of skepticism.

A collection of resources on data science and machine learning primarily in R.

The following note documents a proof of concept for classifying vegetation with 4 band 0.1m aerial imagery. We used sagebrush, bare ground, grass, and PJ for classes. approximately 300 training polygons were used as a training data.

Load the libraries.

This is a basic line chart built with D3. I've written a few more tutorials on how to make charts starting out very basic and moving to a little more complex. I'm no expert, so these are how a beginner (at both javascript and D3) would explain everything. Some might find that methodology helpful. My previous tutorials: My first charts, SVG Plots, Scatterplot.