ML-Assisted Classification
Hybrid heuristic + machine-learning models tuned to your sensors, environments and delivery specification — deployed behind the same QA discipline as our manual cells.
Overview
ML that clears production-grade QA, not just a benchmark — we combine heuristics and machine learning to classify point clouds, imagery and features. Every model is trained on client data, validated per class and integrated with our manual QA cells so the deliverable meets contract, not just F1.
Example outputs
Client confidentiality
All project samples are shared with client permission. We treat every dataset as confidential and never disclose client data.
Want to explore further? Get in touch or schedule a meeting to help build trust.
How we do it
- 01Data and target definitionSensor characteristics, environment types and target class definitions are locked before any labelling starts.
- 02Labelling and trainingTraining data is labelled by production operators and used to train hybrid heuristic + ML models tuned per environment.
- 03Per-class validationIndependent validation sets measure per-class precision and recall so we know exactly where the model is trusted.
- 04Integration with manual QAModel outputs are routed through the same manual QA cells that handle classical delivery — humans review low-confidence areas.
- 05Delivery and re-trainingDeliverables ship to contract; models are re-trained periodically on new labelled data to keep accuracy trending up.
Use cases
- Point cloud auto-classification
- Feature extraction from imagery
- Damage and defect detection
- Land cover classification at scale
- Custom asset detection
Deliverables
- Trained ML models
- Per-class validation report
- Inference pipeline (batch / API)
- Training data documentation
- Retraining plan
Continue exploring
Customised Geospatial Solutions