Geospatial Data Science with Julia
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Updated
Feb 18, 2025 - TeX
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
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
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