Change Detection
Compare observations through time and explain where material field or landscape changes appear.
Capabilities
Explore LAWA
Each capability has a defined operational purpose, expected output, and review boundary.
Risk Scoring
Combine transparent signals into a prioritization score that supports human review rather than replacing it.
Anomaly Detection
Surface observations that differ from field history, neighbouring zones, or expected patterns.
Models & Pipelines
Pilot fit-for-purpose geospatial pipelines using convolutional and transformer-based approaches.
Active Learning
Pilot a review loop that focuses labeling effort on informative and uncertain examples.
Workflow
From observation to action
- 01
Frame the question
Choose a field, period, operational decision, and available evidence.
- 02
Assemble context
Bring together permitted imagery, indices, weather, water, and activity records.
- 03
Interpret
Generate a structured explanation, uncertainty, and proposed follow-up checks.
- 04
Review
Apply human and domain judgment before acting on a recommendation.
Use with the right safeguards
LAWA outputs are model-assisted interpretations, not guaranteed diagnoses, forecasts, or professional advice.
- Recommendations are only as reliable as the input data, assumptions, and model fit.
- Confidence and missing context should remain visible to the reviewer.
- High-impact decisions need qualified human review and field verification.
Keep exploring
Related pages
Put land intelligence to work
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