ml-biomass

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An example of linking TERN AusCover field and image data to predict biomass

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# ml-biomass
[![DOI](https://zenodo.org/badge/89922939.svg)](https://zenodo.org/badge/latestdoi/89922939)  
An example of linking TERN AusCover field and image data to predict biomass
<img src='http://www.auscover.org.au/wp-content/themes/auscover/assets/images/logo.png'>

#### A demonstration on linking field data to image data using machine learning
_DISCLAIMER: This is only a demonstration of some cool data science methods you can pull off on your laptop at home, and not about the science of Biomass estimation. There are some very good scientists and programs looking at how to improve biomass estimation and better quantify the error budget. Whatever comes out of the bottom of this worksheet is to be used for your amusement only :)_

### Abstract
This is a quick and dirty notebook demonstrating how to link two data sets I pulled off the [AusCover](http://auscover.org.au) portal. In particular, I wanted to show how powerful the combination of Raster Attribute Tables (RATs) and Machine Learning (ML) is for getting quick insights into data at a national scale.  I pulled this together pretty quickly it's not necessarily pretty or efficient but it might give someone else a heads up into getting started with integrated ecological data science using [TERN](http://www.tern.org.au/) data.

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