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2019_egu_workshop_jupyter_notebooks

Short course on interactive analysis of Big Earth Data with Jupyter Notebooks

2020_analysis_of_big_earth_data_with_jupyter

This repository hosts the Jupyter notebooks developed for the lecture on 'Analysis of Big Earth Data with Jupyter notebooks' during the OpenGeoHub Summer School 2020.

2020_opengeohub_machine-learning-madlene

OpenGeoHub Summer School: Mastering Machine Learning - Lecture and Training

aeo_radar

AEO GEE radar exercise

arrow-user2022

Larger-Than-Memory Data Workflows with Apache Arrow

bandits

Tool for extracting all kinds of statistics from Sentinel-1 time series and visualizing them

bayts

Set of tools to apply the probabilistic approach of Reiche et al. (2015, 2018) to combine multiple optical and/or Radar satellite time series and to detect deforestation/forest cover loss in near real-time. The package includes functions to apply the approach to both, single pixel time series and raster time series. Examples and test data are provided below.

bfast

Breaks For Additive Season and Trend

bfast-1

GPU Implementation for BFAST

change-forest-cover

charla_rspatiales_raster

curso-analisis-espacial2018

dashboarding_jupyter_voila

This repository hosts the Jupyter notebooks developed for the lecture on 'Dashboarding with Jupyter and Voila' during the OpenGeoHub Summer School 2020.

dl-time-series

Deep Learning algorithms applied to characterization of Remote Sensing time-series

dl_rs_gee

docker_spatial

dockerfile

Contiene los archivos Dockerfile creados en por la CGIG

earthpy

A package built to support working with spatial data using open source python

ee-tensorflow-notebooks

Repository to place example notebooks for Deep Learning applications with TensorFlow and Earth Engine.

egu_2021_lgeo_workshops

Workshop materials for EGU General Assembly 2021 sessions Spatio-temporal trend analysis of spatial climate data (temperature and rainfall) using Python Satellite image processing using Python programming

forest-fires

gee-ccdc-tools

Tools and Earth Engine apps to interact with the outputs from the CCDC algorithm

gee_s1_ard

Creates an analysis ready sentinel-1 SAR image collection in Google Earth Engine by applying additional border noise correction, speckle filtering and radiometric terrain normalization.

geostatstutorials

Tutorials in the form of Jupyter notebooks for the GeoStats.jl framework

hands-on-gradient-boosting-with-xgboost-and-scikit-learn

Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt

juliacon2020-dataframes-tutorial

lucc_abm

mexico_geo_data

Repositorio con links a las fuentes de datos geográficas de México.

monitoring-forest-disturbance-modis-michoacan

ogh_summer_school_2020

Material for the session "Introduction to Deep Learning in R for the analysis of UAV-based remote sensing data"