GRASS GIS 8: from Desktop to Big Data Cubes

ANDREO, VERÓNICA

Wageningen

Workshop; Open Data Science Europe; 2021

OpenGeoHub

GRASS GIS is an established, all-in-one geospatial number cruncher with Python interface, graphical user interface and command line interface. After more than six years of development, the new major version 8.0 is now released, featuring Python 3 support, transition to PROJ 6+, easier batch processing and cloud support. Most importantly, the new version has a completely redesigned user interface that makes it smooth for users to interact with their data. There are also Docker images and a growing number of Jupyter notebooks available. GRASS GIS interoperates with QGIS, R, etc. The constantly growing number of extension modules (GRASS addons) shows that special functionalities can be easily integrated (e.g., mass-preserving interpolation, or Sentinel, MODIS, Landsat, and GBIF data download, etc.). GRASS GIS supports time series processing for vector, raster, and volume data that can be created, analyzed, and also explored through the graphical user interface. Here, due to the sheer volume of data available through the Landsat and Sentinel satellites, there is a strong need for large scale automated processing. With the cloud-based actinia geoprocessing engine, GRASS GIS can be leveraged via a REST API and used for massive data processing. actinia also offers an openEO interface. The i.sentinel toolset 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 by automatically creating space-time raster datasets (strds) and temporal aggregation to achieve cloud-free temporal mosaics of any desired granularity. A dedicated add-on based on ESA's SNAP software handles the pre-processing of Sentinel-1 SAR data (radiometric calibration, speckle filtering, geometric terrain correction) and imports them into a space-time cube with image collection support. Parallelization speeds up processing times of Earth observation data. We show application examples for nationwide land cover classification, small-scale forest monitoring, flood mapping, and much more. Link: https://pretalx.opendatascience.eu/open-data-cube-europe-workshop-2021/talk/YTHZXH/