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Align code with polygon-only training
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---
id: aquaculture
type: element
name: Aquaculture area and facilities
task_types: [semantic_segmentation, instance_segmentation, change_detection, polygon_extraction]
preferred_heads: [validity, context, semantic_segmentation, instance, change_detection]
label_formats: [mask, polygon, coco, geojson, shp]
positive_label: aquaculture
negative_policy: unlabeled_is_ignore
typical_sensors: [PMS, MUX, MSI, SAR]
typical_resolutions_m: [1, 2, 10]
---
# Aquaculture
Aquaculture may appear as dense rafts, cages, ponds, lines, grids, or coastal
facilities. It is not only a semantic segmentation problem; some datasets may
require instance, polygon, or change-detection heads.
## Required Context
- Ships, docks, land structures, and waves are hard negatives.
- Scale is critical; preserve resolution and patch-size metadata.