Geospatial Data Science with Julia

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

Fast image quilting simulation solver for the GeoStats.jl framework

Training images for geostastical simulation

This repository hosts content created from courses, reading books, and personal studies to record and document all knowledge acquired throughout my years of study. This repository is continuously evolving.

Analysis of digital elevation models (DEMs)

"Space-Time Interpolation and Forecasting" - Predicting spatio-temporally distributed variables via space-time regression kriging using numpy and numba.

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

This repository contains notes, explanations, and code snippets related to essential statistics concepts and techniques. The materials cover a range of topics, from basic probability and descriptive statistics to more advanced concepts like hypothesis testing and confidence intervals.

Geostatistical variogram estimation expansion in the scipy style

中国地质大学（武汉）地理信息工程学院开设的一门选修课。An optional curriculum held by School of GI Engineering, CUG

Base package for the GeoStats.jl framework

A flexible MPS framework

Spatialize: A Python/C++ Library for Ensemble Spatial Interpolation

Fast radial basis function interpolation and kriging for large scale data

ArchPy - Stochastic geological modeling

An uncondition random field generation tools that are easy to use

Utilities to read/write extended GSLIB files in Julia

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

These are python notebooks accompanying Lessons available at GeostatisticsLessons.com