There are 22 repositories under earth-science topic.
A directory and analysis of the open source ecosystem in the areas of climate change, sustainable energy, biodiversity and natural resources. https://docs.getgrist.com/gSscJkc5Rb1R/OpenSustaintech
The Generic Mapping Tools
A Python interface for the Generic Mapping Tools.
A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
Initial public release of code, data, and model weights for FourCastNet
PyGMTSAR (Python InSAR): Powerful and Accessible Satellite Interferometry
Directory of Fortran codes on GitHub, arranged by topic
A Curated List of Python Resources for Earth Sciences
Universal Regridder for Geospatial Data
A curated list of Earth Science's Artificial Intelligence (AI) tutorials, notebooks, software, datasets, courses, books, video lectures and papers. Contributions most welcome.
DEPRECATED in favor of our newer libraries (see www.fatiando.org). Python toolkit for modeling and inversion in geophysics.
WRF-Hydro model code
Generic Mapping Tools Library Wrapper for Julia
Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.
A MATLAB software for the analysis of digital elevation models -
The Climate Data Toolbox for MATLAB
A community-driven ontology for the representation of environments
A comprehensive list of NASA Earth science data products
Official repository for Semantic Web for Earth and Environmental Terminology (SWEET) Ontologies
Code for Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities
Collection of GMT (Generic Mapping Tools) scripts, jupyter notebooks (using PyGMT) and files (including digitized map content, colormaps, grid files etc.)
Geologic symbols library and development for QGIS
API Client for NASA POWER Global Meteorology, Surface Solar Energy and Climatology in R
A site dedicated to tutorials, course and other learning materials and resources developed by the Earth Lab team
Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data
boost data pipeline's tangibility, enhance research productivity, reduce work anxiety