Field delineation

FieldFinder

Precise field boundaries for every field: large and small, regular and irregular, anywhere in the world.

Sentinel-2 satellite image of an agricultural landscape in India
The same area with field boundaries detected by FieldFinder drawn in yellow

The challenge

Most field delineation models are trained on large, regular European or North American fields. Applied to small, fragmented and irregular smallholder fields, they merge neighbours, miss boundaries and lose accuracy.

Our approach

FieldFinder extends a proven field boundary model with ground truth from the regions where existing models struggle, among them India, Pakistan, Turkey and Egypt. The result: reliable field delineation anywhere in the world.

A field is not a field.

Field delineation models learn what a field looks like from their training data. Most of it comes from North America and Europe. Apply those models to smallholder regions and they fail, because the fields there break every rule they learned.

Satellite image of Kansas farmland with circular and rectangular fieldsKansas, USA
Regular shapes. Circles and rectangles on a grid, one crop each, roads between them. Each circle is about 800 metres across.
Image: NASA/GSFC/METI/ERSDAC/JAROS, ASTER
Sentinel-2 satellite image of smallholder farmland in India during the growing seasonGujarat, India
Irregular shapes. Fields follow terrain, inheritance and water, with no straight edges and no grid.
Image: Sentinel-2, Marple
Combine harvester and grain cart in a large maize fieldMidwest, USA
Large fields. 50 hectares and more, harvested by machine. Tractor tracks and headlands mark every edge.
Photo: Wikideas1
Farmer harvesting rice by hand in Raichur, KarnatakaKarnataka, India
Small fields. Often under a hectare, harvested by hand. No tracks, only narrow strips between neighbours, hard for a model to see.
Photo: Nanditha Gogate, WELL Labs

What we did about it.

Instead of using only readily-available field data, we specifically trained FieldFinder on small and irregular fields. Because every result that builds on field boundaries is only as good as the boundaries themselves.

1

90,000 small fields, annotated at 2.5 m

Our own ground truth from smallholder regions in Asia and Africa. Annotated by hand on super-resolved Sentinel-2 imagery at 2.5 m, with quality checks on every batch.

2

Flexibility instead of off-the-shelf

Off-the-shelf models are trained once, mostly on European data, and used as they are. We adapt FieldFinder to your region and its field structure.

3

Extra annotations, at no extra cost

If your region needs more training data, we annotate it as part of the project. Better boundaries for you, a better model for everyone. No surcharge.

The result

Same area in India, two models. Drag the slider to compare a standard field delineation model with FieldFinder.

Field boundaries from FieldFinder: closed, regular field shapes
Field boundaries from a standard model: rounded, merged and missing fields
Standard model~320 fields FieldFinder~640 fields

Fewer merged fields. FieldFinder separates neighbouring fields that the standard model merges into one.

Sharper boundaries. Closed, regular field shapes instead of rounded, merged or missing ones.

Where FieldFinder helps

  • Supply-chain transparency
  • Sustainability certification
  • Agricultural monitoring and land-use change
  • Policy support and regulatory compliance

Who FieldFinder is for

  • Sustainability certification bodies
  • Supply-chain transparency platforms
  • Agricultural monitoring providers
  • Public institutions and climate analytics companies

Developed within the ESA InCubed programme since April 2026. Available as data delivery, API or tailored analytics.

Daniel Lanz

Interested in FieldFinder?

Tell us about your region and your use case, and we’ll show you what FieldFinder can do there.

Daniel Lanz

Email Daniel+49 (0)2207 9610 551

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