File size: 3,128 Bytes
2227bb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | # 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:
```powershell
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:
```powershell
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:
```powershell
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:
```markdown
---
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.
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