🪸 Coral AI
Goal: reliable, pixel-level underwater segmentation for coral reef monitoring.
We’re focusing on cover estimation, substrate classes, and benthic categories that translate cleanly into survey metrics and reports.
What we’re building
- Full-frame segmentation for reef scenes (coral types, algae, rubble, sand, etc.).
- Quality-checked outputs suitable for transect analysis and time-series change.
- Interoperability with MariMap (GeoJSON masks, tiled overlays, exports).
Assisted labelling in MariMap
We’re adding assisted segmentation to speed up annotation and QA:
- Smart brush & auto-masks: suggest masks from a click or rough stroke.
- Edge-aware refinements: quick boundary cleanups with snap-to-edge.
- Active learning loop: prioritise uncertain regions; improve the model as you label.
- Batch review: apply/compare masks across transects and image sets.
These tools are designed to support transects & image labelling workflows and to slot into your existing project → site → survey flow.
Try the demo
Direct link: reefsupport-coral-ai-demo.hf.space (opens in a new tab)
Where this is heading
- Short term: improve class set & colour maps, export masks for MariMap layers.
- Mid term: assisted labelling in MariMap with smart tools and versioned datasets.
- Long term: robust reef-wide analytics feeding ESG reporting and biodiversity restoration planning (incl. potential credit methodologies).