geospatial-learn is a Python module for using scikit-learn and xgb models with geo-spatial data, chiefly raster and vector formats.
The module also contains various fuctionality for manipulating raster and vector data as well as some utilities aimed at processing Sentinel 2 data.
The aim is to produce convenient, minimal commands for putting together geo-spatial processing chains using machine learning libs. Development will aim to expand the variety of libs/algorithms available for machine learning beyond the current complement.
geospatial-learn requires:
- Python 3
Installation use the anaconda/miniconda system please install this first
conda env create -f geolearn_env.yml
A summary of some functions can be found here:
https://github.com/Ciaran1981/geospatial-learn/blob/master/docs/quickstart.rst
This is currently a work in progress of course!
Documentation can be found here:
https://ciaran1981.github.io/geospatial-learn/docs/html/index.html
These are a work in progress!
New contributors of all experience levels are welcome
Here are some links to the principal libs used in geospatial-learn.
https://github.com/scikit-learn/
http://xgboost.readthedocs.io/en/latest/
http://scikit-learn.org/stable/
available soon
Geospatial-learn is written and maintained by Ciaran Robb. The functionality was written as part of various research projects involving Earth observation & geo-spatial data.
If you use geospatial-learn in a scientific publication, citations would be appreciated