Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
-
Updated
Sep 10, 2024 - Jupyter Notebook
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
GeostatsPy Python package for spatial data analytics and geostatistics. Started as a reimplementation of GSLIB, Geostatistical Library (Deutsch and Journel, 1992) from Fortran to Python, Geostatistics in a Python package. Now with many additional methods. I hope this resources is helpful, Prof. Michael Pyrcz
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
Geostatistical variogram estimation expansion in the scipy style
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
Analysis of digital elevation models (DEMs)
These are python notebooks accompanying Lessons available at GeostatisticsLessons.com
Geostatistics in Python
Geostatistical utilities and tutorial in R. For the tutorials I have included Rmarkdown html files.
Fast radial basis function interpolation for large scale data
A High Performance Unified Framework for Geostatistics on Manycore Systems.
Geospatial Data Science with Julia
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.
Fast image quilting simulation solver for the GeoStats.jl framework
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"
Use SGeMS (Stanford Geostatistical Modeling Software) within Python.
Add a description, image, and links to the geostatistics topic page so that developers can more easily learn about it.
To associate your repository with the geostatistics topic, visit your repo's landing page and select "manage topics."