---
id: green_tide
type: element
name: Green tide / Enteromorpha
task_types: [semantic_segmentation, polygon_extraction]
preferred_heads: [validity, context, semantic_segmentation]
label_formats: [mask, polygon, geojson, shp]
positive_label: green_tide
negative_policy: unlabeled_is_ignore
typical_sensors: [PMS, MUX, MSS]
typical_resolutions_m: [2, 8, 10]
---

# Green Tide / Enteromorpha

Green tide is a floating-algae target. It can be confused with land vegetation,
aquaculture structures, cloud edges, turbid water, sunglint, and black/no-data
regions if the model is trained as a simple foreground/background classifier.

## Required Context

- Validity handling: black borders and no-data pixels must not be interpreted as green tide.
- Context handling: land and water context should be learned or provided as labels/hard negatives.
- Do not assume a coastline vector or external land mask exists.

## Recommended Output

- Per-pixel probability map for green tide.
- Binary candidate mask after validity/context filtering.
- Optional polygon extraction for product delivery.

## Failure Modes

- Land vegetation is often spectrally similar to algae.
- Black borders can be falsely predicted when labels do not include invalid regions.
- Single-element binary models overpredict on unknown ecological elements.