| name | cluster-labeling-auroville |
| description | Auroville (Tamil Nadu, India) AOI pack for the cluster-labeling skill — paths, label hierarchy, crop visual signatures, geography priors, reference example crops, and the running corrections log. Use whenever labeling Auroville land-cover clusters / relabeling the Auroville land-use map. Load this together with the cluster-labeling engine skill. |
Auroville AOI pack
Domain pack for the cluster-labeling engine. This file is the source of
truth for Auroville labeling knowledge (the user's two memories point here).
Update it every session with new durable learnings.
Paths / config (engine inputs)
- AOI dir:
data/av-3.5K
- Cluster rasters (
SEG): data/av-3.5K/intermediates/clusters/{k22,k44,k88}_s42.tif
— 703×703, EPSG:4326, int16, nodata −1. SEG_KEY = e.g. k88_s42.
- Basemap (
BASE): data/av-3.5K/intermediates/esri_3.5k_roi_cog.tif
— ESRI mosaic, ~1 m effective (grid ~0.58 m, oversampled), 11906×12151, EPSG:4326,
same bounds as the cluster rasters.
- Hierarchy:
data/av-3.5K/land-cover.json (43 nodes flattened).
- Prior/old labels (for review cross-tab):
data/av-3.5K/outputs/land-cover_cog.tif
data/av-3.5K/outputs/pixel-mapping.json (known weak — see below).
- CENTER (
--center): 79.8106 12.0058 = Matrimandir.
Producers (out of scope for labeling): ESRI imagery via bun run download-tiles/
stitch-tiles; cluster rasters + hierarchy via the sibling alpha-bhu repo.
Crop visual signatures
- Cashew looks scrub-like from above (low spreading crowns ≈ scrub clumps).
Hard rule: my "scrub" vs old "cashew" → it's CASHEW. Don't trust the scrub read.
degraded_barren (esp. .eroded_land) is COMMON here and was under-applied —
bare red/laterite soil, eroded/gullied ground, sparse-to-no vegetation. It is more
common than the grazing/maintained-grass reads that wrongly became defaults; actively
use it for genuinely bare/eroded ground (esp. where the old map's smooth-green default
was wrong).- Cashew vs barren discriminator (user-confirmed): crowns present ⇒ CASHEW;
bare/eroded soil with no crowns ⇒
degraded_barren. (So the "scrub→cashew" rule fires
on vegetated clumps; genuinely bare/eroded laterite is barren, not cashew.)
- Mature coconut: star-burst rosette crowns + thin long shadows (NOT scrub-like).
- Young coconut: a regular dot-grid of shrub-sized crowns in grass → new coconut,
not generic orchard.
- Casuarina is two-phase (rotational, harvested every few years, like a water
body is seasonal): (a) standing = fine feathery uniform dark-green canopy;
(b) harvested = a regular-geometry field that looks fallow/bare for a few years.
A geometric "fallow"/grassy field interspersed with or surrounded by casuarina
fields is harvested/fallow casuarina, not generic fallow or dryland crops.
(User-confirmed across k88 c5, c6, c30, c31 — a strong, recurring prior on the west.)
When you read a flat field as fallow/field_crops/grass and casuarina stands are nearby,
prefer
orchards.casuarina.
- Young / recently-planted forest reads light-green and smooth — no separable
crowns, almost grass-like at ~170 px. Do NOT default this to grassland; in a
forest/scrub matrix it is
forest.planted_forest (k88 c17/c18 were misread as
grazing land — they are planted forest).
- Tree-lines along irrigation channels / bunds are common — linear strings of
trees following field edges. Don't let a bund tree-line flip an agricultural
patch to forest; it's part of the field/agroforestry mosaic.
- Geometric field-like patches are NEVER planted forest → label agriculture
(crop/orchard/casuarina).
- There are basically NO natural forests here — never use
forest.natural_forest.
All forest is planted. Dense "natural-looking" canopy = forest.planted_forest,
or casuarina on the west.
- Planted forest clusters around Auroville communities (correlates with settlement).
- Mango orchards exist but were under-labeled in the old map; smaller clusters.
Actively hunt mango (large dense rounded dark crowns, wide regular spacing) at finer k.
Label policy (Auroville-specific class choices)
- INHERIT water from the old map — do NOT relabel it. The manual map's
water
bodies are accurate (and correctly seasonal). Freeze old-map water cells and carry
them straight through; spend judgment only where the relabel adds value. (Don't waste
exemplars re-deciding water, and don't second-guess a dry tank that the old map calls
water — it's seasonal.)
- NEVER use
grassland.grazing_land. Auroville has no land used exclusively
for grazing — herds are moved around and feed off public/common land, so grazing
is not a land-cover class here.
grassland.maintained_grass is RARE and mostly around Matrimandir (managed
gardens / lawns / campus grounds in the central zone). It became a second catch-all
for smooth green (after grazing). Do NOT apply it away from the center unless the
patch is unmistakably mown/managed and ringed by built. Away from the center, a
smooth light-green patch is far more likely forest.planted_forest (young/sparse
trees — see signature; the common case in a forest matrix), harvested
orchards.casuarina (west / amid casuarina), or agriculture.fallow (geometric
field in an agricultural context). Decide by matrix/context, not by "it's green and smooth."agriculture.field_crops.dryland_crops — VERIFY before using. Open question
whether Auroville actually has dryland crops (groundnut/millet) at scale. Many
"dryland_crops" / generic "field_crops" reads in or beside the casuarina zone are
suspected to be harvested/fallow casuarina instead (user flagged c30/c31). Prefer
casuarina when the field sits among casuarina; reserve dryland_crops for fields with a
clear active-crop signature away from the casuarina belt, pending ground verification.- Built subtypes — discriminate by what fills the space BETWEEN the buildings:
built_environment.dense_built — roofs adjacent/contiguous, little vegetation
between; town/village urban fabric.built_environment.sparse_built — buildings separated by open ground (bare
soil / grass / field) as the matrix.built_environment.forest_built — buildings embedded under/among tree canopy;
canopy is the matrix and roofs peek through. The default Auroville
community-in-greenbelt pattern.- Tie-breaker question: is the matrix roofs, open ground, or canopy? Don't pick a
built subtype by roof density alone. (k88 c2/c7 were arbitrary without this.)
Geography priors (direction from Matrimandir)
Every exemplar has lon/lat → compute 8-point compass from center
(brg=(90-deg(atan2(dLat,dLon)))%360; idx=round(brg/45)%8 over [N,NE,E,SE,S,SW,W,NW]).
- Casuarina → west. (Validated: all k22 casuarina clusters are NW.)
- Cashew → east & south.
- Coconut → east & south.
- Forest (planted) → broad central-ish belt incl. the SE; soft prior, not dead-center.
- The geometric center (around Matrimandir) is GARDENS — neither forest nor
field (managed garden/agroforestry/built mosaic). Don't default the center to forest.
- A "scrub" read in the middle of the east/south cashew belt is almost certainly cashew.
- Apply per-exemplar, not per-cluster-centroid (scattered clusters have meaningless centroids).
Reference example crops
Read these to calibrate before judging (prefer tint-free _raw; paths relative to
data/av-3.5K/intermediates/vlm_label_k22/). ✅ = confirmed.
| class | example |
|---|
| coconut, mature ✅ | ../vlm_label_k22_c5w100/crops/c005_e1_raw_x4.jpg, c005_e2_raw_x4.jpg |
| coconut, grove + young grid ✅ | ../vlm_label_k88/crops/c029_e2.jpg (maturing grove), c029_e5.jpg (young dot-grid in laterite) — tinted; clean recrop wanted. User-confirmed c29 ≈ coconut (was mis-voted cashew). |
| coconut, young/grid (older) | crops/c021_e2.jpg (needs tint-free recrop) |
| casuarina, standing ✅ | recheck_casuarina/c014_e0_raw100.jpg, c014_e2_raw100.jpg |
| casuarina, harvested | recheck_casuarina/c020_e1_raw100.jpg (tentative) |
| cashew ✅ | crops/c013_e0.jpg, c003_e0.jpg, c009_e0.jpg |
| field_crops ✅ | recheck_casuarina/c010_e0_raw100.jpg, crops/c006_e0.jpg |
| dense_built ✅ | crops/c008_e0.jpg |
| sparse_built | crops/c016_e1.jpg, crops/c007_e0.jpg |
| forest_built | crops/c004_e2.jpg, crops/c018_e0.jpg |
| sparse_scrub (true) | crops/c000_e0.jpg (only outside the cashew belt) |
Still needed: a confirmed mango, a clean planted_forest (central, irregular),
a confirmed young planted_forest (the light-green/smooth case misread as grazing),
harvested casuarina (west-side geometric fallow field), and a clean
water.seasonal_tanks (dry-season bed).
State / history
- Round 1 = k22 (22 clusters × 3 exemplars), in
data/av-3.5K/intermediates/vlm_label_k22/.
Full results + the append-only corrections.md (per-cluster geo + user feedback)
live there. Labeled by Claude in-harness; user gave 2 feedback rounds.
- Round 2 = k88 (88 clusters × 6 exemplars), in
data/av-3.5K/intermediates/vlm_label_k88/.
Judged in-harness via 11 parallel reader agents sharing one calibration brief.
Outputs: judgments.json, cluster_to_label.json, review.html, split_candidates.md,
corrections.md. Geography priors all held (casuarina W/NW, cashew E/S, center =
gardens). Per-exemplar vs old-map check: only 25% exact / 36% w/ hierarchy match —
the relabel is genuinely correcting the old map, not reproducing it. User feedback
(round 1) drove the label-policy + signature updates above. Key errors found:
grazing_land over-applied as a low-confidence default (now retired); built subtypes
arbitrary without definitions (now defined); harvested casuarina under-called.
- k88 ∩ k22 intersection BUILT (2026-07-05) via the engine's
gen_intersection.py:
the 22 flagged split candidates (see vlm_label_k88/split_candidates.md) partitioned
by k22 membership → intermediates/clusters/k88xk22_s42.tif (191 clusters, ids
0–190; largest cell keeps parent id, minorities = 88–190) + k88xk22_s42_mapping.json
(parentage). All 22 split under k22; verified px-conserving. Details + round-3 plan:
vlm_label_k88/INTERSECTION.md. NOTE: interleaved impurities (two-phase casuarina,
central gardens gradient) may survive within cells — those still need (c) below.
- Round 3 = k88∩k22 cells JUDGED (2026-07-05), in
data/av-3.5K/intermediates/vlm_label_k88xk22/: 103 minority cells + the 22 re-judged
parents, 332 verdicts by 10 parallel fresh readers off BRIEF.md. Hard rules held (no
grazing_land / natural_forest); maintained_grass stayed central; tank-margin cells
resolved as a coherent seasonal-tank family; c113-type riparian strips isolated.
⚠️ Systematic suspect: agriculture.fallow became the new smooth-green default
(30/125 votes, confetti pattern on the choropleth) — watch it like grazing_land was
watched. Awaiting user feedback via review.html; log in that dir's corrections.md.
- Next ideas (not yet done): (a) stratified exemplar selection — pick exemplars
to span the old-label strata within each cluster instead of just the N largest patches,
so minority covers in impure clusters get sampled (the current
patch_exemplars largest-N
bias under-samples them). (b) Use old-map family-spread within a cluster as an
independent split trigger. (c) Refine flagged impure clusters: k88 ∩ k22 (computable
locally, good for spatially-split impurities) for the spatial ones; a finer/local
re-cluster from alpha-bhu (k176 or sub-clustering of just the flagged masks) for the
interleaved ones (two-phase casuarina, gardens gradient, cashew-belt edges) that an
intersection with a coarser level won't separate. Avoid global k176 (re-fragments the
already-clean pure clusters; k-levels don't nest).
Run commands (this AOI)
RUN=data/av-3.5K/intermediates/vlm_label_k88
python scripts/vlm_label_prototype.py --aoi data/av-3.5K --seg-key k88_s42 \
--clusters 88 --exemplars 6 --dry-run --out $RUN
python .claude/skills/cluster-labeling/scripts/gen_locator.py $RUN \
--seg data/av-3.5K/intermediates/clusters/k88_s42.tif \
--base data/av-3.5K/intermediates/esri_3.5k_roi_cog.tif --center 79.8106 12.0058
python .claude/skills/cluster-labeling/scripts/gen_overview.py $RUN \
--seg data/av-3.5K/intermediates/clusters/k88_s42.tif \
--base data/av-3.5K/intermediates/esri_3.5k_roi_cog.tif
# split raster for round 3 (flagged impure clusters partitioned by k22):
python .claude/skills/cluster-labeling/scripts/gen_intersection.py \
data/av-3.5K/intermediates/clusters/k88xk22_s42.tif \
--seg data/av-3.5K/intermediates/clusters/k88_s42.tif \
--with data/av-3.5K/intermediates/clusters/k22_s42.tif \
data/av-3.5K/intermediates/clusters/k44_s42.tif \
--ids <flagged ids from split_candidates.md>
# → read overview_basemap.jpg + crops + locators, write $RUN/judgments.json
python .claude/skills/cluster-labeling/scripts/aggregate.py $RUN --judgments $RUN/judgments.json
python .claude/skills/cluster-labeling/scripts/gen_review_html.py $RUN \
--seg data/av-3.5K/intermediates/clusters/k88_s42.tif \
--old data/av-3.5K/outputs/land-cover_cog.tif \
--mapping data/av-3.5K/outputs/pixel-mapping.json
