Martin Landa
Sessions
Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap)
Content:
Why Cloud Optimized GeoTIFF?
Generating COG files using GDAL
Providing COG files through S3 protocol
Accessing remote COG files in Python
QGIS Eumap Plugin
Introduction to OGC Open Web Services (OWS)
Introduction to OWSLib Python API
CSW, WMS, WFS, (WCS currently we don’t provide)
pyeumap API (Catalogue, WMS?)
Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap, scikit-learn), QGIS
Content:
Introduction to LUCAS dataset provided by Eurostat
Harmonized LUCAS dataset
Accessing LUCAS dataset using Python API (Jupyter notebooks)
Accessing LUCAS dataset from QGIS
Land product validation with LUCAS points (use case)
Introduction info OGC WPS
OWSLib Python API
OGC WPS QGIS plugin
Actinia cloud computing (linked to GRASS 8 GIS training session: Introduction and new features)
Software requirements: GRASS GIS 8
Content:
Introduction to the new version 8 of GRASS GIS, a few concepts
Showcase the heavily redesigned graphical user interface
Interaction with data (visualization, styling, map elements)
Analysis of data from different domains
Introduction to automated processing
Hints on Python 3 scripting, spatio-temporal data analysis, and more.
Candidate datasets: improved ERA5 land air temperature, surface temperature and precipitation (daily data)
Software requirements: GRASS GIS 8
Content:
GRASS GIS supports time series processing for vector, raster, and volume data
Micro-introduction to Landsat and Sentinel satellite data archives, and the various ways to access them
Introduction to the i.sentinel toolset which allows querying Sentinel data coverage for a region of interest, downloading from multiple data sources, performing atmospheric and topographic corrections, and cloud/shadow masking
Preparation of data for multitemporal analyses is enabled in the t.sentinel and t.rast.mosaic extensions through automatic creation of space-time raster datasets (strds) and temporal aggregation to obtain cloud-free temporal mosaics of arbitrary granularity.
Computation of NDVI time series
Use Sentinel, ERA5 land air temperature, surface temperature and precipitation (daily data)