appelmar / CONAE_2022

Materials for CONAE spring school tutorial on "Analyzing satellite image collections on public cloud platforms with R"

Analyzing satellite image collections on public cloud platforms with R

Workshop materials for CONAE spring school 2022

Workshop website (including slides and hands-on notebooks): https://appelmar.github.io/CONAE_2022.

Overview

This tutorial demonstrates how to access and process satellite image collections on cloud computing platforms using R and modern cloud-native tools including SpatioTemporal Asset Catalogs, cloud optimized GeoTIFFs, and on-demand data cubes. After a quick introduction and overview of corresponding R packages, practical examples on image compositing, time series analysis, and the extraction of training data for machine learning models will be presented in a live demonstration. The tutorial will end with a discussion of limitations and future developments in R. Materials and further information will be published at https://github.com/appelmar/CONAE_2022.

Contents

1. Introduction
   1. The cloud
   2. Satellite imagery on cloud platforms
   3. Cloud-native technologies: STAC, COGs, data cubes
   4. R ecosystem for analyzing satellite imagery
   5. The gdalcubes R package
2. Hands-on examples
   1. Computing cloud-free mosaic images from Sentinel-2 images
   2. Time series analysis (trend, changes) using MODIS image time series
   3. Extraction of training data for ML applications from Sentinel-2 images
3. Discussion