THE APPROACH
Give every map a path back to the field.
Ground observations help test what a satellite or image model appears to show. Our approach links location and visual evidence with quality checks, model interpretation and review.
Record the observation where it happens
Photographs become more useful when connected to location, time and the observed crop or field condition. Structured records keep that context with the image.
- Geotagged photographs
- Observation date and field context
- Consistent records for later review
Show what needs a closer look
AI can assist interpretation, but unfamiliar inputs and low-confidence results require review. Confidence cues and visual explanations help a person inspect the model response.
- Clear confidence and needs-review cues
- Image-led model explanations
- A path for unrecognised or unfamiliar inputs
Turn field evidence into a better analysis
Reviewed records can support map validation and model improvement. Corrections should remain traceable to the original observation so analysts can understand how a result was assessed.
- Human review before accepting uncertain records
- Ground evidence for mapped classes
- Feedback for subsequent model development
What the workflow produces
- Location-linked field records
- Reviewable predictions and confidence cues
- Evidence for map and model validation
INTERPRETATION & LIMITS
Make the evidence inspectable.
An AI prediction is an aid to judgement. It does not replace field expertise, and its reliability depends on the training coverage and the quality of the input observation.
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