🪐 Markdown with superpowers: from ideas to papers, presentations, websites, books, and knowledge bases.

Run AI-based weather forecasting models with ECMWF data

This initiative is inclined to address global vector (point) data accessing and hosting in a cloud-native environment.

🛰️ List of earth observation companies and job sites

Interoperable processes for openEO's big Earth observation cloud processing.

A ready-to-use curated list of Spectral Indices for Remote Sensing applications.

Perform parallel computing on clusters with R language

Master the command line, in one page

Techniques for deep learning with satellite & aerial imagery

Progress bar in your R terminal

A reproducible analysis of environmental data - from hardware to plot

The Ultimate FREE Machine Learning Study Plan

Introduction to convolutional neural networks (CNNs) with a remote sensing example.

Interpreting machine-learning models in transformed space

R package for downloading and processing Land cover/use statistics (LUCAS) data provided by EUROSTAT

Airborne LiDAR data manipulation and visualisation for forestry application

🌐 List & Map of 790+ companies for geospatial jobs (GIS, Earth Observation, UAV, Satellite, Digital Farming, ..)

pyGEDI is a Python Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) mission, data extraction, analysis, processing and visualization.

scripts and snippets for Google Earth Engine

🔏 Opinionated, typographic-centric ggplot2 themes and theme components

A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery

RMarkdown template for theses written at HU Berlin WiWi

An updated R Markdown thesis template using the bookdown package

Word counts and readability statistics in R markdown documents

⛔ ARCHIVED ⛔ What the Package Does (One Line, Title Case)

Functions for estimating wildland fire hazard using common models in the US.

Cheat sheet for GDAL/OGR command-line tools

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media