diff --git a/claude-notes/plans/2026-07-10-garmin-dem-satellite-skin.md b/claude-notes/plans/2026-07-10-garmin-dem-satellite-skin.md
index bfdf89701..e92e5af0d 100644
--- a/claude-notes/plans/2026-07-10-garmin-dem-satellite-skin.md
+++ b/claude-notes/plans/2026-07-10-garmin-dem-satellite-skin.md
@@ -174,3 +174,44 @@ river as a width-stamped POLYLINE from the Garmin line features instead.
   z/y/x order CORRECT, attribution follows. cockpit-server itself cannot compile in
   this container (rusty_v8 static-lib download blocked by egress policy) — Rust side
   mirrors the existing tested pattern; CI runs the tests.
+
+## Havel mosaic harmonization (2026-07-13, follow-up shipped)
+
+The Havel satellite skin is a **quilt** — a mosaic of unrelated ESRI acquisitions
+differing in season, sun angle and white balance, with visible tile seams. A big
+hazy grey acquisition tile dominated the mid-lower canyon and two pale strips ran
+down the right. Used raw as a terrain texture the quilt read as arbitrary
+bright/dark patches, not terrain.
+
+**Root cause of an earlier regression:** the prior "Weißabgleich" (a global
+per-channel percentile stretch) *amplified* the quilt — it pushed the hazy tiles
+toward white (RAW near-white 0.32% → global-WB 5.17%, 16×). White balance is the
+wrong tool: a global stretch has no notion of "this brightness is a seam, not the
+scene."
+
+**Fix — flat-field harmonization** (`scripts/harmonize_demgrid.py`, run offline on
+the `.demgrid` before the bake; the global WB is dropped):
+1. Large-radius (80px) Gaussian estimates the quilt's slowly-varying
+   exposure/white-balance *field* (`low`).
+2. Subtract `(low − global_mean) · strength` (strength 0.80) to flatten that field
+   toward one global mean. 0.80 removes the seams while a genuinely dark forest
+   block vs a bright agricultural block survives (1.0 erases them).
+3. **Water protection** — dark (low-luma) pixels flatten less, so lakes/rivers keep
+   their true dark tone instead of being lifted toward the land mean.
+4. **Chroma compression** toward a common vegetation magnitude (target 28) so an
+   over-saturated tile and a washed tile land on one green — killing the
+   "some tiles grey, some vivid" tell.
+
+Only the `rgb` block is rewritten; header/`lats`/`elev` are byte-for-byte copied,
+so geometry and elevation are untouched. Result: near-white 5.17% → **0.10%**; the
+grey acquisition tile and pale strips pull into a coherent forest green; the
+Müritz and the Havel stay dark. Operator approved the 2D after ("Looks perfect");
+3D proxy (1.21M verts) reads as one place, not a patchwork.
+
+Philosophy (operator's brief): *satellite imagery is source material, not the final
+appearance.* The pass is deliberately gentle — a stronger variant (1.0) over-
+flattens into a muddy uniform green and was rejected.
+
+Shipped asset: `cockpit/public/havel.v8grid.soa.gz` (19.4M-vert bake of the
+harmonized skin). The script reproduces the shipped `.demgrid` byte-for-byte
+(`--strength 0.80 --radius 80 --chroma 28`, the Havel-approved values).
diff --git a/cockpit/public/havel.v8grid.soa.gz b/cockpit/public/havel.v8grid.soa.gz
index 66f9a764a..92d31dfdb 100644
Binary files a/cockpit/public/havel.v8grid.soa.gz and b/cockpit/public/havel.v8grid.soa.gz differ
diff --git a/scripts/harmonize_demgrid.py b/scripts/harmonize_demgrid.py
new file mode 100644
index 000000000..05a2e68d3
--- /dev/null
+++ b/scripts/harmonize_demgrid.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""Flat-field harmonization for a `.demgrid` satellite skin (DEMG v2).
+
+A `.demgrid` produced by `fetch_iceland_dem.py` carries a raw ESRI World
+Imagery drape (the `rgb` block, DEMG v2). Over a large region that drape is a
+*quilt*: a mosaic of unrelated satellite acquisitions differing in season, sun
+angle and white balance, with visible tile seams. Used directly as a terrain
+texture the quilt reads as arbitrary bright/dark/desaturated patches rather
+than as terrain.
+
+This pass removes the *low-frequency* tile-to-tile variation (the quilt)
+while preserving *high-frequency* detail (fields, forest, roads, the actual
+terrain texture). It is deliberately gentle — the philosophy is that the
+satellite imagery is *source material*, not the final appearance, but we still
+want it to read as a real place, not a flat paint chip.
+
+Algorithm (per RGB channel, operating on the full skin as one image):
+
+  1. Large-radius Gaussian blur `low` estimates the local mean — i.e. the
+     quilt's slowly-varying exposure/white-balance field.
+  2. Subtract `(low - global_mean) * strength` to flatten that field toward a
+     single global mean. `strength=0.80` removes most of the seam contrast
+     without erasing genuine large-scale terrain (a real dark forest block vs
+     a real bright agricultural block survives at 0.80; it does not at 1.0).
+  3. Water is *protected*: dark pixels (low luma) get a reduced strength so
+     lakes/rivers keep their true dark tone instead of being lifted toward the
+     land mean.
+  4. Chroma is compressed toward a target magnitude so an over-saturated tile
+     and a washed-out tile land on a common vegetation chroma, killing the
+     "some tiles are grey, some are vivid green" tell.
+
+This is the transform that produced the shipped `havel.v8grid.soa.gz`. The
+`--strength 0.80 --radius 80 --chroma 28` defaults are the values approved for
+Havel; other regions may want a different radius (scale it with the skin's
+pixel dimensions — 80 suits ~2000px-wide skins).
+
+Only the `rgb` block is rewritten; header, `lats` and `elev` are copied byte
+-for-byte, so geometry and elevation are untouched.
+
+Usage:
+    python scripts/harmonize_demgrid.py in.demgrid out.demgrid \
+        [--strength 0.80] [--radius 80] [--chroma 28] [--water-knee 60] \
+        [--water-soft 40]
+"""
+
+import argparse
+import struct
+import sys
+
+import numpy as np
+from PIL import Image, ImageFilter
+
+LUMA = np.float32([0.299, 0.587, 0.114])
+
+
+def read_demgrid(path):
+    b = np.fromfile(path, dtype=np.uint8)
+    if b[:4].tobytes() != b"DEMG":
+        sys.exit(f"{path}: not a DEMG file")
+    ver, w, h = struct.unpack_from("<III", b, 4)
+    if ver != 2:
+        sys.exit(f"{path}: expected DEMG v2 (has raw skin); got v{ver}")
+    west, east = struct.unpack_from("<dd", b, 16)
+    n = w * h
+    o = 32
+    lats = b[o : o + h * 8].copy()
+    o += h * 8
+    elev = b[o : o + n * 4].copy()
+    o += n * 4
+    rgb = b[o : o + n * 3].astype(np.float32).reshape(h, w, 3)
+    header = b[:32].copy()
+    return {"header": header, "w": w, "h": h, "lats": lats, "elev": elev, "rgb": rgb}
+
+
+def write_demgrid(path, dem, rgb_u8):
+    with open(path, "wb") as f:
+        f.write(dem["header"].tobytes())
+        f.write(dem["lats"].tobytes())
+        f.write(dem["elev"].tobytes())
+        f.write(rgb_u8.reshape(-1).tobytes())
+
+
+def harmonize(rgb, strength, radius, chroma_target, water_knee, water_soft):
+    h, w, _ = rgb.shape
+    gmean = rgb.reshape(-1, 3).mean(0)
+    lum = rgb @ LUMA
+
+    # (1) low-frequency field = the quilt's exposure/white-balance variation
+    low = np.asarray(
+        Image.fromarray(rgb.astype("u1"), "RGB").filter(
+            ImageFilter.GaussianBlur(radius)
+        ),
+        dtype=np.float32,
+    )
+
+    # (3) water protection: dark pixels flatten less, keeping lakes dark
+    water = np.clip((water_knee - lum) / max(water_soft, 1e-3), 0.0, 1.0)
+    eff = (strength * (1.0 - 0.85 * water))[:, :, None]
+
+    # (2) flatten the low-frequency field toward the global mean
+    flat = rgb - (low - gmean) * eff
+
+    # (4) compress chroma toward a common vegetation magnitude
+    lum2 = (flat @ LUMA)[:, :, None]
+    chroma = flat - lum2
+    cmag = np.linalg.norm(chroma, axis=2, keepdims=True) + 1e-3
+    scale = np.clip(chroma_target / cmag, 0.6, 1.7)
+    harm = lum2 + chroma * scale
+
+    return np.clip(harm, 0, 255).astype(np.uint8)
+
+
+def near_white_frac(rgb_u8):
+    """Fraction of pixels that are near-white (a proxy for washed/quilt tiles)."""
+    m = rgb_u8.reshape(-1, 3).min(axis=1)
+    return float((m > 200).mean())
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("input")
+    ap.add_argument("output")
+    ap.add_argument("--strength", type=float, default=0.80)
+    ap.add_argument("--radius", type=float, default=80.0)
+    ap.add_argument("--chroma", type=float, default=28.0, help="target chroma magnitude")
+    ap.add_argument("--water-knee", type=float, default=60.0, help="luma below which water protection ramps in")
+    ap.add_argument("--water-soft", type=float, default=40.0, help="luma softness of the water knee")
+    args = ap.parse_args()
+
+    dem = read_demgrid(args.input)
+    raw_u8 = dem["rgb"].astype(np.uint8)
+    harm = harmonize(dem["rgb"], args.strength, args.radius, args.chroma, args.water_knee, args.water_soft)
+    write_demgrid(args.output, dem, harm)
+
+    print(
+        f"harmonized {dem['w']}x{dem['h']} skin  "
+        f"near-white {near_white_frac(raw_u8) * 100:.2f}% -> {near_white_frac(harm) * 100:.2f}%  "
+        f"(strength={args.strength} radius={args.radius} chroma={args.chroma})"
+    )
+    print(f"wrote {args.output}")
+
+
+if __name__ == "__main__":
+    main()