Stars
Plugin Qgis3 d'aide à l'extraction des zones en eau sur les images satellites
Chaine de traitement d'image satellites pour OCS et autres indicateurs dérivés
Projet SCO (CNES) Green Urban Sat (Végétation en Ville)
A Python package to create, run, and post-process MODFLOW-based models.
A list of open geospatial datasets available on AWS, Earth Engine, Planetary Computer, NASA CMR, and STAC Index
List of all datasets included in Google Earth Engine (generated from https://developers.google.com/earth-engine/datasets/catalog/)
Groundwater Time Series Modeling Challenge
Reconstruction of surface water dynamics in global reservoirs
🌎 Simple and fast watershed delineation in python.
Google Earth Engine Asset Manager with Addons
PyCrown - Fast raster-based individual tree segmentation for LiDAR data
Python Package for Airborne RGB machine learning
Interroger les référentiels géographiques plus facilement
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
A python package that extends Google Earth Engine.
3-D Scientific Visualization in the Jupyter Notebook
Python Data Science Handbook: full text in Jupyter Notebooks
A repository with Google Earth Engine workshops, courses and conferences.
Jupyter/Colab intetrace for sequential SAR change detection on GEE
Community Datasets added by users and made available for use at large
GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation da…
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Pastas is an open-source Python framework for the analysis of groundwater time series.
Hub'Eau, la plateforme pour manipuler facilement les données ouvertes sur l'eau
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models