No Worries, Just Maps 🥍
No Worries, Just Maps Timon

Remote Sensing Specialist

I am a remote sensing specialist and spatial data analyst with expertise in mapping irrigated agriculture and environmental monitoring. With a Ph.D. in remote sensing of irrigated agriculture from Wageningen University, I focus on transforming complex satellite data into actionable insights for agricultural and environmental applications.

My professional work at Resilience BV involves developing monitoring systems that bridge the gap between satellite data and end users. I specialize in creating innovative solutions for tracking crop health, monitoring environmental changes, and supporting sustainable agriculture through advanced geospatial analysis.

Beyond my technical work, I’m passionate about sharing knowledge in the remote sensing community and contributing to open-source tools for Earth observation. When I’m not analyzing satellite imagery, you’ll find me playing box lacrosse at international level (proud member of Team Netherlands), hiking with my dog, or playing drums.

Interests
  • Artificial Intelligence
  • Map Making
  • Box Lacrosse
Education
  • PhD Remote sensing of irrigated agriculture

    Wageningen University

  • MSc International Land and Water management

    Wageningen University

  • MSc Geo-Information Sciences

    Wageningen University

  • BSc International Land and Water management

    Wageningen University

Solving Earthly Problems with Space Data
While satellites continuously capture terabytes of Earth observation data, the challenge lies in making this information useful on the ground. I develop specialized monitoring systems that solve real-world problems—whether it’s optimizing irrigation for farmers, tracking deforestation for conservationists, or mapping land use changes for policy makers.
Recent Projects
Technical Expertise
Remote Sensing & GIS: Multi-spectral satellite imagery (Sentinel-1 & 2, PlanetScope), Time-series analysis, Google Earth Engine, QGIS • Programming: R (terra, sf, tidyverse), Python (geopandas, gdal), JavaScript • Machine Learning: Random Forests, SVM, Neural Networks, K-means clustering, Classification, Time-series Analysis • Analysis & Development: RStudio, Visual Studio Code, Google Earth Engine, Reproducible research workflows