Markus Neteler
Markus Neteler, PhD, is a cofounder of mundialis after having spent 15 years as a researcher in Italy. His focus is on Earth Observation, GIS and cloud computing. Markus managed for two decades the GRASS GIS project, and he is a founding member of OSGeo and other organizations.
Sessions
This workshop is an introduction to the new version 8.2 of GRASS GIS, showcasing the new functionality and the heavily redesigned graphical user interface. It also explores the interaction with data (visualization, styling, map elements), the analysis of data from different domains, and it introduces to automated processing.
We will introduce to 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 8.2
GRASS GIS supports time-series processing for vector, raster, and volume data. This workshop offers a micro-introduction to Sentinel satellite data archives, and the various ways to access them. It also explores 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. This workshop also gives a preparation of data for multitemporal analyses through automatic creation of a space-time raster dataset (strds), It explores the computation of NDVI time series. Eventually we run a simple RandomForest landuse classification on Sentinel-2 data.
Software requirements: We will run GRASS GIS 8.2 on Google Colab.
Jupyter notebook: https://gitlab.com/geoharmonizer_inea/odse-workshop-2022/-/blob/main/grass_gis/notebooks/sentinel2_grass_gis_colab.ipynb
After a short introduction to geoprocessing services we will showcase OGC WPS with a focus on Request examples ("GetCapabilities", "DescribeProcess" and "Execute"). Then the OGC API - Processes Standard which supports the wrapping of computational tasks into executable processes is briefly explained.
In the second part we introduce actinia which is an open source REST API for scalable, distributed, high performance processing of geographical data.
The course notebooks are available here and the actinia material here.
With the rapidly growing availability of Earth observation and geospatial data, the need for scalable geoprocessing solutions is also increasing. Following the paradigm of bringing the algorithms to the data, we have developed the cloud-based geoprocessing platform actinia (https://actinia.mundialis.de and https://github.com/mundialis/actinia_core). This free and open source solution is designed as microservices and can ingest and analyze large amounts of data in a parallelized manner. actinia provides an HTTP REST API around GRASS GIS functionality, extended by ESA SNAP and user scripts and libraries written in Python, C or C++. Core functionality includes processing of raster and vector data and time series of satellite imagery (Landsat, Sentinel-1, Sentinel-2, Pleiades, etc.).
actinia is an OSGeo community project since 2019 and a backend of [openEO] (https://openeo.org). Recently added process chain templating, STAC support, a Python client, a QGIS plugin, Jupyter notebooks, and more. This presentation gives an overview about the actinia and its latest developments.