Whether you need to verify thousands of plots against the EUDR cutoff date or monitor forest health over a whole region, we build automated pipelines that your teams can easily run, so you own both the code and the product.
The EUDR requires companies placing cattle, cocoa, coffee, oil palm, rubber, soy and wood on the EU market to show that these products do not come from land deforested after 31 December 2020. That means checking every plot, not just a sample, and keeping the evidence.
Our team's experience includes designing and deploying automated remote sensing pipelines in Google Earth Engine, Python and cloud machine learning to track and report deforestation in line with the EUDR, applying convolutional neural networks (CNN) and time-series methods to automate forest classification across high-risk regions, and delivering EUDR verification for international clients.
We also provide technical assistance to implement remote sensing and GIS in forestry activities, supporting your management on the ground with on-demand satellite analysis that gives forest owners actual data to make better management decisions.
Google Earth EnginePythonCloud MLCNN / deep learningTime-seriesSentinel & LandsatQGIS
Forest clearing inside a mapped plot, 2011–2021. Imagery: Google Earth.
What we offer
Services in this pillar.
Plot-level verification against deforestation cutoff dates, with automated alerts and audit-ready evidence, built on scalable remote sensing and ML pipelines.
03.1
Plot verification
Checks of farm and plot geolocations against forest baselines and the EUDR cutoff date, with a clear result for each plot.
03.2
Geolocation quality control
Validation of points and polygons: wrong coordinates, overlaps, oversized plots and other common errors.
03.3
Forest baselines & change detection
Forest maps and change detection using optical and radar satellite data and time-series analysis.
03.4
Deep learning classification
CNN-based forest and land cover classification for regions where global maps are not accurate enough.
03.5
Automated alerts & reporting
Cloud pipelines that re-run as new imagery arrives and produce audit-ready reports.
03.6
Forest resilience monitoring
Monitoring of forest health and disturbance, including in European forests.
Track record
Relevant experience.
2023 – 2025
Global Risk Assessment Services (GRAS)
Remote sensing expert
Designed and deployed automated remote sensing pipelines (Google Earth Engine, Python, cloud ML) to track and report deforestation in line with the EUDR.
Applied CNN models and time-series algorithms in Google Earth Engine to automate forest classification across high-risk regions.
Coordinated technical delivery for international clients needing EUDR verification, including large agricultural importers.
Wrote technical project proposals for forest resilience monitoring across Germany.
2018 – 2023
WWF Germany
GIS and remote sensing specialist
Built land cover classification and BFAST time-series workflows to detect land degradation over many years.
2010 – 2011
IPAM, Rio Branco, Brazil
Research assistant
Field data collection and spatial land use modelling in the Brazilian Amazon, and five REDD+ policy workshops with local community stakeholders.
