Markdown Capability Registry

This directory stores composable Markdown capability cards. A card is a small human-readable configuration unit with YAML front matter and operational notes.

Cards are intentionally separated by concern:

elements/: target feature or object type, such as green tide, ship, oil spill, sea ice.
satellites/: satellite/platform assumptions.
sensors/: sensor or product type assumptions.
resolutions/: spatial-resolution policies.

Extraction and training jobs should compose the cards they need instead of using a single fixed class list. For example:

python scripts/compose_task_profile.py `
  --element green_tide `
  --satellite GF6 `
  --sensor PMS `
  --resolution 2m `
  --output configs/profiles/gf6_green_tide_2m.json

For already fused GF6-like products, inject a fused product card:

python scripts/compose_task_profile.py `
  --element green_tide `
  --satellite GF6 `
  --sensor PMS `
  --fusion FUSED_OPTICAL `
  --resolution 2m `
  --output configs/profiles/gf6_green_tide_fused_2m.json

For raw PAN+MSS products where fusion should happen tile by tile during inference, inject the streaming fusion card:

python scripts/compose_task_profile.py `
  --element green_tide `
  --satellite GF2 `
  --sensor PMS `
  --fusion STREAM_FUSION `
  --resolution 2m `
  --output configs/profiles/gf2_green_tide_stream_fusion_2m.json

Card Format

Each card starts with YAML front matter:

---
id: green_tide
type: element
task_types: [semantic_segmentation]
preferred_heads: [validity, context, semantic_segmentation]
label_formats: [mask, polygon]
---

# Green Tide

Human-readable notes, rules, constraints, and known failure modes.

The front matter is parsed by scripts/compose_task_profile.py. The Markdown body is kept in the output profile so that training/inference logs preserve the reasoning and caveats behind the selected cards.

Fused Image Expression

Fused imagery is represented as a derived observation, not as a plain multispectral image and not as a boolean flag. The selected profile and dataset manifest must keep a fusion object with:

state: none, fused_product, runtime_fusion, or unknown.
method: known method, implementation method, or unknown_vendor_product.
sources: source roles and paths such as PAN and MSS.
target_resolution_m: output grid resolution.
native_multispectral_resolution_m: original multispectral resolution.
persisted: whether the fused image exists on disk.
reproducible: whether the source data and method can reproduce it.
spectral_preservation: known or estimated spectral preservation risk.

This keeps GF6 supplied fused rasters, GF1/GF2 tile-wise fusion, and future fusion algorithms comparable without pretending they are identical inputs.

Hard Constraint

Do not assume coastline vectors, land-mask vectors, or external GIS layers are available. If a task needs land, water, invalid, or cloud handling, it must be expressed as context labels, hard-negative samples, validity rules, or model heads.