Let's see what we can do with some data Kate's provided from NEON.
I'm using Python3 here with frozen requirements.
virtualenv -p python3 .env will get things set up if a python >= 3.6 is installed.
. .env/bin/activiate to get the proper binaries loaded up.
pip install -r requirements.txt to get deps.
For dev, probably run ipython, %load_ext autoreload and %autoreload 2
to get autoreloading of modules set up.
To see what the models do right away, just exec ./main.py.
Index: The first column of each CSV appears to be just a line number, 0 indexed
GRID_CODE: Data was plucked from a grid with geohashed coords to provide a somewhat random sampling. Grid code is just the grid it came from. Probably use this as a primary key.
NLCD: Classification codes from the NLCD classication database. This identifies kind of cover a given grid contains. I'm not sure if these are Murph's manually identified classifications or if these came from the NLCD DB.
B*: Band data, will be our feature set for classification.