Starred repositories
Curriculum Vitae (CV), LaTeX Template, designed by Xovee Xu.
Code central to the analysis reported by Walker et al. (2022) on the global potential for increased storage of carbon on land.
R code and spatial predictions for WHRC-TNC project modeling spatial extent of soil carbon loss due to agriculture
Github Pages template based upon HTML and Markdown for personal, portfolio-based websites.
🎨 A succinct matplotlib wrapper for making beautiful, publication-quality graphics
Machine Learning algorithms for spatial and spatiotemporal data
Collection of publicly available IPTV channels from all over the world
A collection of tools to work with Google Earth Engine Python API
A set of tools to use in Google Earth Engine Code Editor (JavaScript)
Natural feeling domain-specific language for building structural equation models in R for estimation by covariance-based methods (like LISREL/Lavaan) or partial least squares (like SmartPLS)
EasyChart / Beautiful-Visualization-with-Python
Forked from Easy-Shu/Beautiful-Visualization-with-Pythonpython数据可视化之美
Latex code for making neural networks diagrams
A global, public domain map dataset available at three scales and featuring tightly integrated vector and raster data.
This repository is a LaTeX project of a document that follows all the submission requirements for Computers & Geosciences.
rabramoff / Millennial
Forked from email-clm/MillennialThis is a repository for the newly developed Millennial model
A set of common color palettes for Google Earth Engine
Global Soil Information Facilities (tutorials)
Data on CO2 and greenhouse gas emissions by Our World in Data
A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment
Phenological analysis of Remote Sensing data with Python
A curated list of Google Earth Engine resources
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
An implementation of the Co-Training semi-supervised learning technique from (Blue, Mitchell 1998) that is meant to work well with scikit-learn classifiers.
Sequence modeling benchmarks and temporal convolutional networks