Sector · Agriculture
From satellite imagery to field decisions.
Fields change week to week, and so does the imagery over them. The hard part is turning that stream into something an agronomist or farm manager acts on.
Problems we solve
Where GeoAI helps in agriculture
Crop and field maps are incomplete
Boundaries and crop types are often missing or years old. Classification on multi-date imagery builds them and keeps them current.
In-season problems are noticed too late
Vegetation time series show stress and uneven growth before it is visible from the farm track, if the series is cleaned of clouds and compared against a sensible baseline.
Indices never become decisions
A map of NDVI is not an instruction. We work with agronomists to define thresholds, zones and alerts that map to an actual action.
Consulting
Imagery AI for crop and condition layers, GeoAI projects for yield, risk and suitability questions.
Academy
Remote sensing and Google Earth Engine
Teach analysts to work with satellite time series themselves, from preprocessing to classification.
See the trainingInsights
Remote sensing for agriculture: from satellite imagery to field decisions
A walk from raw satellite imagery to a decision an agronomist can act on, with a video walkthrough.
Read the articleWorking in agriculture? Tell us the problem.
A short call is enough to tell whether GeoAI can help, and what a pilot would look like.