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The kitchen sink.

  1. 🔗Web Dev Resources

    A collection of resources that I couldn't work without

  2. 🔗Django Getting Started

    My notes on learning Django. I'm using this Django Tutorial.

  3. 🔗Classifying High Resolution Aerial Imagery - Part 2

    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.

  4. 🔗Colorado Avalanches By The Numbers in R

    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.

  5. 🔗Data Science Resources

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

  6. 🔗Classifying High Resolution Aerial Imagery - Part 1

    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.