Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome

Kriging Toolkit for Python

GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more

An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

GeostatsPy Python package for spatial data analytics and geostatistics. Mostly a reimplementation of GSLIB, Geostatistical Library (Deutsch and Journel, 1992) in Python. Geostatistics in a Python package. I hope this resources is helpful, Prof. Michael Pyrcz

Geostatistical variogram estimation expansion in the scipy style

Fast radial basis function interpolation for large scale data

Analysis of digital elevation models (DEMs)

A set of numerical demonstrations in Excel to assist with teaching / learning concepts in probability, statistics, spatial data analytics and geostatistics. I hope these resources are helpful, Prof. Michael Pyrcz

Geostatistics in Python

Geospatial Data Science with Julia

GeoStats.jl tutorials

These are python notebooks accompanying Lessons available at GeostatisticsLessons.com

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

A flexible MPS framework

Fast image quilting simulation solver for the GeoStats.jl framework

Use SGeMS (Stanford Geostatistical Modeling Software) within Python.

GammaRay: a graphical interface to GSLib and other geomodeling algorithms. *NEW* in May, 6th: Drift analysis.

The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.