Open Data Science Europe Workshop 2021

Europe is a dynamic continent and its landscape has changed over the last 20 years. Current developments in computing power and an increase in the efficiency of geospatial computing has made it possible to analyse the archive of Landsat images that date back to the year 2000 at 30 meter resolution for the entire continent of Europe. We developed methods to harmonize, analyse and visualize changes between land cover types, create overviews of NDVI trends, and trends in predicted probabilities over the years using machine learning approaches. In this talk I will be discussing some specific examples of interpretations that these new approaches allow. We will be travelling from the forest harvesting in Sweden to the hydroelectric dams in Portugal, over mysterious changes in the Alps to the apparent reforestation of the Romanian forests. I invite everyone to join the trip through Europe and help us understand our changing surroundings at scale!

Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap, scikit-learn)
Content:
Theoretical background for Ensemble ML and python implementations
General concepts and main advantages of spatiotemporal machine learning
Why use LandMapper?
Spacetime overlay to prepare the training samples
Spacetime cross-validation to evaluate the EML model performance
Hyperparameter optimization to tune the EML model
Fitting the final EML model
Generating spatial predictions using the fitted model

Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap, scikit-learn)
Content:
Theoretical background for machine learning and python implementations
Integrating raster data with scikit-learn models
Why use pyeumap.LandMapper?
Spatial overlay to prepare the training samples
Spatial cross-validation to evaluate the ML model performance
Hyperparameter optimization to tune the ML model
Fitting the final ML model
Generating spatial predictions using the fitted model