After taking part in the Future Planet Studies bachelor program at the University of Amsterdam Chris has worked with various levels of government and businesses. He joined OpenGeoHub to contribute as a machine learning engineer and researcher uncovering valuable insights from remote sensing data and help to bring those insights to users..
At EnvirometriX, Chris supports development of Machine Learning methods for predictive mapping. He will be involved in developing data layers through the application of machine learning techniques on satellite imagery, helping policymakers and businesses achieve scalable insights from remote sensing data.. He is also member of the OpenGeoHub development team.
Phone : +31 (0)721 1062
- MSc, Future Planet Studies, Universiteit van Amsterdam
- 2021–present: Geospatial researcher at OpenGeoHub Foundation,
- Dec. 2020 – Present: Contributor Green City Watch open-source collective,
- Jan. 2018 – Dec. 2020: Co-founder and Data Scientist at Green City Watch
- 2019 – June 2019: Developer blended learning at University of Amsterdam,
- Feb. 2018 – Feb. 2019: Trainee Data Science at Xomnia stationed at the Port of Rotterdam,
- Sep. 2017 – Feb. 2018: GIS-specialist at Geodan stationed at de Persgroep Printing,
- Ensemble Machine Learning
- Python, R
- Spatial Analysis
- Predictive Mapping
Witjes, M., Parente, L., van Diemen, C. J., Hengl, T., Landa, M., Brodsky, L., … & Glusica, L. (2021). A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. https://www.researchsquare.com/article/rs-561383/v1