Recent Posts
Python tutorials:
R tutorials:
- Hengl, T., Parente, L. & Bonannella, C. (2022). Spatial and spatiotemporal interpolation using Ensemble Machine Learning: https://opengeohub.github.io/spatial-prediction-eml/index.html
- Hengl, T., Parente, L., & Bonannella, C. (2021, September 17). Predictive mapping using spatiotemporal Ensemble ML (R tutorial). Zenodo. https://doi.org/10.5281/zenodo.5513827
Video tutorials:
- Antonić L.: “Introduction to spatial and spatiotemporal data in Python”
- Brodský L., Landa M., Bouček T.: “Working with harmonized LUCAS datasets”
- Hengl, T., Bonannella C.: “Spatiotemporal Ensemble ML in R”
- Hengl T.: “Computing with Cloud-Optimized GeoTIFFs”
- Hengl T.: “Introduction to spatial and spatiotemporal data in R”
- Hengl T.: “Modeling with spatial and spatiotemporal data in R: spatial interpolation”
- Landa M.: “Using OGC Web Services in Python”
- Parente L.: “Introduction to ODSE datasets in Python”
- Parente L., Antonić L.: “Working with Cloud-Optimized GeoTIFFs in Python”
- Parente L., van Diemen C.: “Spatiotemporal machine learning in Python”
- Parente L. “High performance computing in Python”
A copy of Videos with DOI is available via: Open Data Science Workshop series for 2021 edition, and 2022 edition (see also complete list of videos).
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CEF Telecom project 2018-EU-IA-0095.
This project, co-financed by the European Union,
is now officially closed. For any inquiry, please refer to the Open Earth Monitor website https://earthmonitor.org/- The GeoHarmonizer project consortium
- OpenGeoHub, Agro Business Park 10, 6708PW Wageningen, NL
- (+31) (0)317 427537 | | | | |
- coordination-team@earthmonitor.org
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