As the project is closed, visitors should forward their requests to coordination-team@earthmonitor.org (+31) (0)317 427537

Markus Neteler

Cofounder and senior consultant at mundialis
 https://www.mundialis.de/de/neteler/

All Sessions by Markus Neteler

  • Day 2

    June 14, 2022

  • 9:00  - 10:30
    Workshop room 2 - C223 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) The software requirements are: GRASS GIS 8
    AI
    Machine Learning
    Software

  • 11:00  - 12:30
    Workshop room 2 - C223 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 Candidate datasets: Use Sentinel, ERA5 land air temperature, surface temperature and precipitation (daily data)
    AI
    Big Data
    Earth Observation
    Machine Learning
    Software

  • 13:30  - 15:00
    Workshop room 1 - C221 Introduction into web processing (OGC WPS, OGC API - Processes, REST API) in a comparative view Accessing WPS processes Accessing WPS processes from Python code using OWSLib Accessing WPS processes from QGIS actinia cloud computing with REST API (@neteler @metzm) explain how REST and actinia work actinia in Jupyter notebook actinia-QGIS plugin
    Climate data
    Data Cubes
    Land cover
    Software
    Soil data
    Training sessions
    Vegetation mapping

  • Day 3

    June 15

  • 10:10  - 10:30
    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.
    Big Data
    Conference
    Data Cubes
    Open source
    Software
    Where
    Conference room - C202




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Markus Neteler