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+# Heightmap Pipeline — Spike Documentation
+
+**Ticket:** #778
+**Author:** Araminta
+**Date:** 2026-04-05
+**Status:** Spike complete — awaiting review before batch (#794, Sprint 33)
+**Output:** `heightmaps/GJ144d_kallast.png` (4096×2048px)
+
+---
+
+## What This Spike Validates
+
+This spike validates the full annotated heightmap pipeline from wiki data through to a
+deliverable PNG. Every stage ran successfully on Kallast (GJ144d, Ran system):
+
+- pyplatec tectonic simulation → elevation grid
+- Erosion pass → softer ridges, valley hints
+- Dynamic sea level → correct 40% land coverage from wiki spec
+- Terrain classification → 13 biome classes
+- D8 flow accumulation → river network
+- Settlement placement snapped to appropriate terrain class
+- Road network connecting all cities
+- Annotated render with title/legend in Settled Reach visual grammar
+
+**The pipeline is confirmed viable for batch production (#794).**
+
+---
+
+## Planet: Kallast (GJ144d)
+
+Selected because it showcases all annotation types:
+
+| Property | Value | Source |
+|----------|-------|--------|
+| Planet ID | `GJ144d` | systems.db |
+| System | Ran (GJ 144) | systems.db |
+| Biome | temperate | systems.db |
+| Hydrosphere | ocean | systems.db |
+| Land coverage | 40% | wiki: "amber continental shelves" |
+| Population | 2,000,000,000 | systems.db |
+| Settlement wave | 1 (580y) | systems.db |
+| Settlement pattern | urban_concentrated | systems.db |
+| Industrial | Agricultural_Syndic | systems.db |
+| Terrain character | Extensive temperate plains, amber-toned grassland | wiki narrative |
+
+Kallast was chosen over higher-population worlds (Haodu, etc.) because the wiki narrative
+explicitly describes the terrain features we need to annotate: "amber continental shelves
+broken by irrigation channels wide enough to see from low orbit." That text is a direct
+visual brief. The pipeline output should feel consistent with it.
+
+---
+
+## Pipeline Architecture
+
+```
+Input: Planet profile (wiki + systems.db)
+         ↓ planet_type, land_fraction, settlement data
+
+Stage 1: Tectonic simulation (pyplatec)
+         platec.create(seed, W, H, sea_level=land_fraction, …)
+         platec.step() × 200  [200 steps for mature, well-eroded world]
+         platec.get_heightmap() → float list → reshape → normalize [0, 1]
+         Runtime: ~1s at 512×256 (scales linearly with grid × steps)
+
+Stage 2: Erosion (scipy gaussian_filter)
+         Slope-weighted smoothing: steep cells erode more
+         4 passes on mature world (reduce to 2 for young volcanic)
+         Runtime: <0.5s at 512×256
+
+Stage 3: Dynamic sea level
+         sea_level = np.percentile(terrain, (1 - land_fraction) * 100)
+         CRITICAL: pyplatec output is heavily right-skewed (most cells at low
+         elevation). A fixed sea_level fraction (e.g. 0.40) does NOT produce
+         40% land — you get ~0.2% land. Always compute from actual distribution.
+
+Stage 4: Terrain classification (13 classes)
+         Thresholds as fractions of the land elevation range [sea_level, max]
+         so classification scales correctly across different pyplatec outputs.
+         Classes: ocean_deep → ocean_mid → ocean_shallow → coast → lowland →
+                  plains → grassland → hills → forest → highland → mountain →
+                  peak → snow
+
+Stage 5: D8 flow accumulation → river network
+         Sort land cells by elevation descending
+         Each cell drains to steepest downslope neighbour (8-directional)
+         Flow threshold: 30 (calibrated for 512×256 grid with 40% land)
+         Note: threshold scales with grid size and terrain relief — calibrate
+         per planet type. Very flat worlds (like Kallast) need lower threshold.
+
+Stage 6: Settlement placement
+         For each city from wiki data: snap to nearest plains/grain_belt cell
+         within expanding search radius (20 → 40 → 60 → 80 cells)
+         Preference order: plains (class 5) > grain_belt (6) > lowland (4) >
+                           coast (3) > hills (7)
+
+Stage 7: Road network
+         Tier-1 and tier-2 cities connected by major roads (all-pairs from capital)
+         Tier-3 nodes connected to nearest tier-1/2 by minor roads
+         Rendered as polylines on the annotated layer
+
+Stage 8: Geographic render (PIL)
+         1. Base terrain color layer (RGB from class colors)
+         2. Elevation shading on land (ambient occlusion proxy)
+         3. Dilate river mask at source grid resolution (2 iterations, preserves topology)
+         4. Scale up terrain to output resolution (4096×2048) via LANCZOS
+         5. Paint rivers AFTER upscale via NEAREST-neighbor upscaled mask
+            CRITICAL: painting before LANCZOS blurs rivers into invisibility.
+            Post-upscale NEAREST gives each source cell a 4×4px block — clearly legible.
+         6. Lat/lon grid lines (every 30°), scaled width
+         7. Title panel + legend — natural geographic features only
+            (ocean, coast, plains, grassland, mountain, river)
+            Text/panel sizes scale with UI_SCALE = OUTPUT_W / 1024
+         NOTE: settlements, roads, freight elevators are NOT rendered here.
+               They live in the JSON sidecar and are overlaid by the atlas app.
+```
+
+---
+
+## Configuration per Planet Type
+
+For the batch run (#794), per-planet config differs in:
+
+| Parameter | Kallast | Young volcanic | Ice world | Desert | Ocean world |
+|-----------|---------|----------------|-----------|--------|-------------|
+| `plate_count` | 10 | 4 | 7 | 6 | 8 |
+| `sim_steps` | 200 | 100 | 150 | 150 | 180 |
+| `erosion_passes` | 4 | 1 | 3 (glacial) | 2 (aeolian) | 3 |
+| `land_fraction` | 0.40 | 0.55 | 0.30 | 0.60 | 0.15 |
+| `river_threshold` | 60 | 320 | 80 | 60 | 200 |
+
+The `land_fraction` comes directly from the wiki's hydrosphere field:
+- `ocean` → 0.30–0.45
+- `liquid_water` → 0.40–0.60
+- `ice` → 0.20–0.35
+- `none` → 0.90–0.99
+
+---
+
+## Two-Layer Model
+
+Heightmaps are **geographic only**. Human data lives in JSON sidecars.
+
+```
+kallast_heightmap.png          ← geographic render: terrain, rivers, biomes, grid
+kallast_heightmap_settlements.json  ← human layer: city names + grid coordinates
+```
+
+The PNG renders: terrain classification colors, elevation shading, dilated river
+network, lat/lon grid, title panel.
+
+The PNG does NOT render: settlements, roads, freight elevators, city labels, irrigation
+channels, or any human-activity markers. Those exist in the JSON sidecar and are overlaid
+separately by the atlas app (Phase 3) when the map is interactive.
+
+**Rationale:** A geographic heightmap is a stable base layer. The human overlay changes
+as the simulation runs (cities grow, shrink, change character). Keeping them separate
+means the PNG can be regenerated from terrain data without recomputing settlement
+placement, and vice versa.
+
+## Output Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `kallast_heightmap.png` | 4096×2048px | Geographic world map (deliverable) |
+| `kallast_terrain.npy` | ~2MB | Raw normalised elevation grid (numpy float32, 1024×512) |
+| `kallast_heightmap_settlements.json` | <1KB | City positions for atlas DB import (human layer sidecar) |
+
+For batch production, the `.npy` and `.json` files are inputs to the Phase 3
+atlas pipeline — they pre-seed the city layer rather than requiring re-computation.
+
+---
+
+## Known Issues / Calibration Notes for Batch
+
+1. **River painting order is critical.** Painting river pixels into the source-resolution
+   array before LANCZOS upscaling blurs them into invisibility. Always dilate at source
+   resolution, then upscale with NEAREST neighbor and paint AFTER. Enforced in `render()`.
+
+2. **Flat worlds produce sparse rivers.** Kallast has low terrain relief. Threshold=60
+   at 1024×512 gives 113 pre-dilation cells (1164 post). Scale threshold with grid area:
+   `threshold_1024 ≈ threshold_512 * 4`. For this flat world, halve the baseline to get
+   denser coverage.
+
+3. **City placement uses wiki narrative coordinates, not astrophysical simulation.**
+   Relative positions (e.g. "Kallast Prime at 45% longitude, 48% latitude") are editorial
+   decisions. The snap algorithm finds nearest suitable terrain class within search radius.
+   This is intentional — settlement locations should reflect the world's narrative.
+
+4. **Agricultural layer.** The wiki describes irrigation channels wide enough to see
+   from orbit. These are human infrastructure — they belong in the JSON sidecar, not the
+   geographic heightmap. Phase 3 atlas work should render irrigation channels as a
+   separate overlay from hydrology + settlement data.
+
+---
+
+## Batch Run Estimate (#794)
+
+Grid size: 1024×512. Output: 4096×2048.
+
+| Phase | Step | Time per planet | 301 planets |
+|-------|------|-----------------|-------------|
+| Tectonic (200 steps, 1024×512) | ~3.5s | 1054s |
+| Erosion (4 passes) | ~1.0s | 301s |
+| Hydrology | ~0.5s | 151s |
+| Placement + roads | ~0.5s | 151s |
+| Render + export | ~0.8s | 241s |
+| **Total** | | **~6.3s/planet** | **~32 minutes** |
+
+Full batch of 301 systems runs in ~32 minutes single-threaded. Parallelisable across all
+CPU cores (no shared state) — realistically ~8 minutes on 4 cores.
+
+Note: if batch time is a concern, `sim_steps=100` halves tectonic time with acceptable
+terrain quality for most planet types. Only mature worlds (Kallast, old ocean worlds)
+benefit meaningfully from 200 steps.
+
+---
+
+## Running the Spike
+
+```bash
+# Standard (200 tectonic steps, ~1.5s)
+python3 spikes/heightmap-pipeline/generate_kallast.py
+
+# Fast mode (50 steps — good for testing annotation, poor terrain)
+python3 spikes/heightmap-pipeline/generate_kallast.py --fast
+
+# Different seed (changes continent layout)
+python3 spikes/heightmap-pipeline/generate_kallast.py --seed 42
+
+# Custom output path
+python3 spikes/heightmap-pipeline/generate_kallast.py --output heightmaps/GJ144d_kallast_v2.png
+```
+
+Dependencies: `pyplatec`, `scipy`, `numpy`, `Pillow` (all installable via pip)
+
+---
+
+## Open Questions for Review
+
+Before starting batch (#794), Jeroen should confirm:
+
+1. **Visual style.** Does the terrain color palette work? The grassland amber
+   (`#a59b4b`) reads as temperate plains — is this the right mood for Kallast?
+2. **Annotation density.** 12 settlements for a 2B-population world — too sparse? too
+   many? For the batch, settlement count would be derived from wiki city data (if any)
+   or a formula from population + settlement_pattern.
+3. **Output resolution.** 1024×512 adequate for wiki use? Or do we need 2048×1024
+   for the implant atlas app (Phase 3)?
+4. **River threshold calibration.** The flat terrain of Kallast needed threshold=30.
+   Should we auto-calibrate per planet by targeting N river-mouth cells, rather than
+   a fixed threshold?
diff --git a/spikes/heightmap-pipeline/generate_kallast.py b/spikes/heightmap-pipeline/generate_kallast.py
new file mode 100644
index 000000000..4b3dc6c62
--- /dev/null
+++ b/spikes/heightmap-pipeline/generate_kallast.py
@@ -0,0 +1,568 @@
+#!/usr/bin/env python3
+"""
+Heightmap Spike: Kallast (GJ144d)
+
+Produces one annotated heightmap for Kallast — Ran system's inner habitable
+world. Wave 1 agricultural planet, breathable atmosphere, 0.95g, temperate.
+
+Pipeline:
+  1. Tectonic simulation (pyplatec) → elevation grid
+  2. Hydraulic erosion (numpy/scipy) → soften ridges, carve valleys
+  3. Climate pass → moisture/temperature from latitude + elevation
+  4. Terrain classification → biome zones
+  5. Hydrology → flow accumulation → river network
+  6. Settlement placement → cities along rivers + fertile plains
+  7. Road network → minimum spanning connections between major cities
+  8. Annotated render → PNG export
+
+Usage:
+    python3 generate_kallast.py [--output path] [--seed N] [--fast]
+
+Outputs:
+    kallast_heightmap.png   — annotated world map
+    kallast_terrain.npy     — raw terrain grid (numpy, for batch reuse)
+    kallast_rivers.npy      — river network mask
+    kallast_settlements.json — city coordinates and names
+"""
+
+import argparse
+import json
+import math
+import random
+import time
+import sys
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Planet parameters (from wiki/systems.db: GJ144d Kallast)
+# ─────────────────────────────────────────────────────────────────────────────
+
+PLANET = {
+    "name": "Kallast",
+    "system": "Ran (GJ 144)",
+    "body_id": "GJ144d",
+    "gravity": 0.95,        # g — affects tectonic force scaling
+    "atmosphere": "breathable",
+    "biome_summary": "temperate",
+    "hydrosphere": "ocean",
+    "population": 2_000_000_000,
+    "settlement_wave": 1,
+    "settlement_age_years": 580,
+    "industrial": "Agricultural_Syndic",
+    # Tectonic profile: Earth-like, high activity (mature world, well-eroded)
+    "plate_count": 10,
+    "land_fraction": 0.40,  # 40% land coverage — continental grain belt world
+    # Note: SEA_LEVEL is computed dynamically from land_fraction after
+    # tectonic simulation, since pyplatec produces a skewed distribution
+    # that does not map linearly to target coverage percentages.
+}
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Grid settings
+# ─────────────────────────────────────────────────────────────────────────────
+
+GRID_W = 1024
+GRID_H = 512
+OUTPUT_W = 4096
+OUTPUT_H = 2048
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Color palette (Settled Reach visual grammar: muted, earthy, legible)
+# ─────────────────────────────────────────────────────────────────────────────
+
+PALETTE = {
+    "ocean_deep":    (18,  32,  58),
+    "ocean_mid":     (28,  52,  90),
+    "ocean_shallow": (42,  80, 110),
+    "coast":         (80, 105,  75),
+    "lowland":       (95, 115,  65),
+    "plains":        (130, 145,  80),
+    "grassland":     (165, 155,  75),   # temperate plains / savanna (amber tone)
+    "hills":         (120, 110,  80),
+    "forest":        (60,  90,  55),
+    "highland":      (110, 100,  90),
+    "mountain":      (140, 130, 120),
+    "peak":          (195, 190, 185),
+    "snow":          (230, 228, 225),
+    # Annotation colors
+    "river":         (80, 140, 200),
+    "road_major":    (180, 155,  90),
+    "road_minor":    (160, 140,  85),
+    "city_major":    (220,  60,  50),
+    "city_minor":    (200, 110,  60),
+    "city_label":    (240, 235, 220),
+    "freight_elev":  (200, 180, 100),   # freight elevator pads
+    "grid_line":     (255, 255, 255),
+    "title_bg":      (15,  18,  25),
+    "title_text":    (200, 208, 224),   # insert chrome: #c8d0e0
+}
+
+
+def step(msg: str):
+    print(f"  [{time.strftime('%H:%M:%S')}] {msg}", flush=True)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 1. Tectonic simulation
+# ─────────────────────────────────────────────────────────────────────────────
+
+def run_tectonics(seed: int, fast: bool = False) -> np.ndarray:
+    """Run pyplatec tectonic simulation. Returns normalised float32 grid."""
+    step("Running tectonic simulation (pyplatec)…")
+    import platec
+
+    sim_steps = 50 if fast else 200
+
+    p = platec.create(
+        seed,
+        GRID_W, GRID_H,
+        sea_level=PLANET["land_fraction"],
+        erosion_period=60,
+        folding_ratio=0.02,
+        aggr_overlap_abs=1_000_000,
+        aggr_overlap_rel=0.33,
+        cycle_count=2,
+        num_plates=PLANET["plate_count"],
+    )
+
+    for _ in range(sim_steps):
+        platec.step(p)
+
+    hmap_raw = platec.get_heightmap(p)
+    platec.destroy(p)
+
+    arr = np.array(hmap_raw, dtype=np.float32).reshape(GRID_H, GRID_W)
+    # Normalise to [0, 1]
+    lo, hi = arr.min(), arr.max()
+    arr = (arr - lo) / (hi - lo + 1e-9)
+    step(f"Tectonic done. Elevation range: [{lo:.1f}, {hi:.1f}]")
+    return arr
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 2. Hydraulic erosion (simplified — scipy gaussian smoothing on steep slopes)
+# ─────────────────────────────────────────────────────────────────────────────
+
+def erode(terrain: np.ndarray, passes: int = 3) -> np.ndarray:
+    """Simplified erosion: smooth with slope-weighted kernel."""
+    step(f"Applying erosion ({passes} passes)…")
+    from scipy.ndimage import gaussian_filter, uniform_filter
+
+    result = terrain.copy()
+    for i in range(passes):
+        # Identify steep slopes
+        gy, gx = np.gradient(result)
+        slope = np.sqrt(gx**2 + gy**2)
+        # Smooth strongly on steep areas (erosion), less on plains
+        smooth = gaussian_filter(result, sigma=1.5)
+        weight = np.clip(slope * 8, 0, 1)
+        result = result * (1 - weight * 0.4) + smooth * (weight * 0.4)
+    return result
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 3. Terrain classification
+# ─────────────────────────────────────────────────────────────────────────────
+
+SEA_LEVEL = None  # set dynamically after tectonic simulation
+
+def compute_sea_level(terrain: np.ndarray, land_fraction: float) -> float:
+    """
+    Compute sea level as the percentile that yields the target land fraction.
+    pyplatec produces a skewed elevation distribution (most area at low elevation,
+    peaks only at plate boundaries), so we cannot use a fixed fraction of the
+    0-1 normalised range.
+    """
+    ocean_fraction = 1.0 - land_fraction
+    sl = float(np.percentile(terrain, ocean_fraction * 100))
+    step(f"Sea level computed: {sl:.4f} (target land={land_fraction*100:.0f}%, "
+         f"actual≈{(terrain >= sl).sum() / terrain.size * 100:.1f}%)")
+    return sl
+
+
+def classify(terrain: np.ndarray) -> np.ndarray:
+    """
+    Returns integer class array relative to the dynamic sea level.
+    Thresholds are expressed as fractions of the land elevation range
+    (sea_level to max) rather than fixed offsets, so they scale correctly
+    regardless of the pyplatec output distribution.
+
+      0 = ocean_deep   (lowest)
+      1 = ocean_mid
+      2 = ocean_shallow
+      3 = coast        (just above SL)
+      4 = lowland
+      5 = plains
+      6 = grassland    (temperate plains / savanna — natural terrain class)
+      7 = hills
+      8 = forest
+      9 = highland
+     10 = mountain
+     11 = peak
+     12 = snow         (highest)
+    """
+    sl = SEA_LEVEL
+    land_max = terrain.max()
+    land_range = max(land_max - sl, 1e-6)
+
+    c = np.zeros_like(terrain, dtype=np.int8)
+    # Ocean bands (below sea level)
+    ocean_range = max(sl - terrain.min(), 1e-6)
+    c[terrain >= terrain.min()]          = 0   # ocean_deep (baseline)
+    c[terrain >= sl - ocean_range * 0.5] = 1   # ocean_mid
+    c[terrain >= sl - ocean_range * 0.2] = 2   # ocean_shallow
+    # Land bands (above sea level, as fraction of land_range)
+    c[terrain >= sl]                     = 3   # coast
+    c[terrain >= sl + land_range * 0.05] = 4   # lowland
+    c[terrain >= sl + land_range * 0.15] = 5   # plains
+    c[terrain >= sl + land_range * 0.28] = 6   # grassland
+    c[terrain >= sl + land_range * 0.42] = 7   # hills
+    c[terrain >= sl + land_range * 0.53] = 8   # forest
+    c[terrain >= sl + land_range * 0.63] = 9   # highland
+    c[terrain >= sl + land_range * 0.74] = 10  # mountain
+    c[terrain >= sl + land_range * 0.85] = 11  # peak
+    c[terrain >= sl + land_range * 0.93] = 12  # snow
+    return c
+
+
+CLASS_COLORS = [
+    PALETTE["ocean_deep"],
+    PALETTE["ocean_mid"],
+    PALETTE["ocean_shallow"],
+    PALETTE["coast"],
+    PALETTE["lowland"],
+    PALETTE["plains"],
+    PALETTE["grassland"],
+    PALETTE["hills"],
+    PALETTE["forest"],
+    PALETTE["highland"],
+    PALETTE["mountain"],
+    PALETTE["peak"],
+    PALETTE["snow"],
+]
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 4. Hydrology — flow accumulation → river network
+# ─────────────────────────────────────────────────────────────────────────────
+
+def compute_rivers(terrain: np.ndarray, threshold: int = 30) -> np.ndarray:
+    """
+    Simple D8 flow accumulation. Returns boolean mask of river cells.
+    Not physically accurate but produces plausible branching networks.
+    """
+    step("Computing river network…")
+    H, W = terrain.shape
+    # D8 direction offsets
+    dirs = [(-1,-1),(-1,0),(-1,1),(0,-1),(0,1),(1,-1),(1,0),(1,1)]
+
+    # For each land cell, find steepest descent
+    flow_acc = np.zeros((H, W), dtype=np.int32)
+    land = terrain >= SEA_LEVEL
+
+    # Simplified: accumulate flow by draining from high to low
+    # Sort cells by elevation descending
+    ys, xs = np.where(land)
+    order = np.argsort(terrain[ys, xs])[::-1]
+    ys_sorted = ys[order]
+    xs_sorted = xs[order]
+
+    for y, x in zip(ys_sorted, xs_sorted):
+        flow_acc[y, x] += 1
+        # Find steepest downslope neighbour
+        best_drop = 0
+        best_ny, best_nx = -1, -1
+        for dy, dx in dirs:
+            ny, nx = y + dy, x + dx
+            if 0 <= ny < H and 0 <= nx < W:
+                drop = terrain[y, x] - terrain[ny, nx]
+                if drop > best_drop:
+                    best_drop = drop
+                    best_ny, best_nx = ny, nx
+        if best_ny >= 0:
+            flow_acc[best_ny, best_nx] += flow_acc[y, x]
+
+    rivers = (flow_acc > threshold) & land
+    step(f"River network: {rivers.sum()} cells above threshold {threshold}")
+    return rivers
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 5. Settlement placement
+# ─────────────────────────────────────────────────────────────────────────────
+
+KALLAST_CITIES = [
+    # Name, (relative grid position), tier (1=capital, 2=regional, 3=node)
+    # Placed on fertile plains near river confluences.
+    # Temperate plains world → most cities on the grassland continental shelf.
+    ("Kallast Prime",   (0.45, 0.48), 1),   # capital, central continent
+    ("Ardenvall",       (0.30, 0.40), 2),   # western grain province
+    ("Thessmark",       (0.60, 0.52), 2),   # eastern province
+    ("Coldwater",       (0.25, 0.62), 2),   # southern coast, fishing + export
+    ("Brightfield",     (0.50, 0.36), 2),   # northern plains
+    ("Vorn's Crossing", (0.38, 0.55), 3),   # river crossing, freight node
+    ("Saltmere",        (0.68, 0.42), 3),   # coast + processing node
+    ("Kaspel",          (0.20, 0.50), 3),   # western interior node
+    ("Drenmark",        (0.72, 0.58), 3),   # southeastern node
+    ("New Farrow",      (0.55, 0.64), 3),   # southern freight hub
+    ("Ossenfield",      (0.42, 0.30), 3),   # northern highland approach
+    ("Tyne Station",    (0.33, 0.45), 3),   # freight elevator ground station
+]
+
+# Freight elevator locations (visible from orbit per wiki)
+FREIGHT_ELEVATORS = [
+    ("Kallast Anchor",  (0.45, 0.46)),
+    ("Ardenvall Lift",  (0.29, 0.38)),
+    ("Thessmark Riser", (0.61, 0.50)),
+]
+
+
+def place_settlements(terrain: np.ndarray, cities: list) -> list:
+    """
+    Snap city positions to nearest suitable terrain cell.
+    Suitable = plains or grassland class, preferably near river.
+    Returns list of (name, grid_y, grid_x, tier) tuples.
+    """
+    step("Placing settlements…")
+    classified = classify(terrain)
+    # Debug: show land cell distribution
+    for cls_id in range(13):
+        n = (classified == cls_id).sum()
+        if n > 0:
+            step(f"  class {cls_id}: {n} cells")
+    H, W = terrain.shape
+    placed = []
+
+    for name, (rx, ry), tier in cities:
+        cx = int(rx * W)
+        cy = int(ry * H)
+        # Search in expanding radius for valid terrain (up to 60 cells)
+        best_y, best_x = cy, cx
+        best_score = -1
+        for radius in [20, 40, 60, 80]:
+            for dy in range(-radius, radius + 1):
+                for dx in range(-radius, radius + 1):
+                    ty, tx = cy + dy, cx + dx
+                    if 0 <= ty < H and 0 <= tx < W:
+                        c = classified[ty, tx]
+                        # Score: higher for plains/grain_belt, acceptable for coast/lowland
+                        score = 0
+                        if c in (5, 6):   # plains/grassland (ideal settlement terrain)
+                            score = 20 - (abs(dy) + abs(dx)) * 0.2
+                        elif c == 4:      # lowland
+                            score = 12 - (abs(dy) + abs(dx)) * 0.2
+                        elif c == 3:      # coast (ports, export hubs)
+                            score = 8 - (abs(dy) + abs(dx)) * 0.2
+                        elif c == 7:      # hills (defensible / highland cities)
+                            score = 5 - (abs(dy) + abs(dx)) * 0.2
+                        if score > best_score:
+                            best_score = score
+                            best_y, best_x = ty, tx
+            if best_score > 0:
+                break   # found valid terrain at this radius, stop expanding
+        placed.append((name, best_y, best_x, tier))
+        step(f"  {name} → ({best_x}, {best_y}) terrain={classified[best_y, best_x]}")
+
+    return placed
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 6. Road network (simple greedy connections)
+# ─────────────────────────────────────────────────────────────────────────────
+
+def build_roads(settlements: list) -> list:
+    """
+    Connect tier-1 and tier-2 cities with major roads.
+    Connect tier-3 nodes to nearest tier-1 or tier-2.
+    Returns list of (y1, x1, y2, x2, road_type) tuples.
+    """
+    roads = []
+    tier12 = [(n, y, x) for (n, y, x, t) in settlements if t <= 2]
+    tier3  = [(n, y, x) for (n, y, x, t) in settlements if t == 3]
+
+    # Connect all tier-1/2 cities in order (simple chain + cross-links)
+    for i in range(len(tier12) - 1):
+        _, y1, x1 = tier12[i]
+        _, y2, x2 = tier12[i + 1]
+        roads.append((y1, x1, y2, x2, "major"))
+    # Capital spurs to each tier-2
+    cap = tier12[0]
+    for city in tier12[1:]:
+        roads.append((cap[1], cap[2], city[1], city[2], "major"))
+
+    # Tier-3 nodes to nearest tier-1/2
+    for (n3, y3, x3) in tier3:
+        best_d = 1e9
+        best = tier12[0]
+        for city in tier12:
+            d = (city[1]-y3)**2 + (city[2]-x3)**2
+            if d < best_d:
+                best_d = d
+                best = city
+        roads.append((y3, x3, best[1], best[2], "minor"))
+
+    return roads
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 7. Render
+# ─────────────────────────────────────────────────────────────────────────────
+
+def render(
+    terrain: np.ndarray,
+    rivers: np.ndarray,
+    output_path: str,
+):
+    """
+    Renders a geographic heightmap: terrain colors, rivers, coastlines,
+    biome zones, lat/lon grid, title panel.
+    Human layer (settlements, roads, irrigation) is stored in the JSON sidecar only.
+
+    River rendering order matters: dilate at source resolution to preserve flow
+    topology, then paint AFTER upscaling via NEAREST neighbor to avoid LANCZOS
+    blurring the river network into invisibility.
+    """
+    step("Rendering geographic heightmap…")
+    from scipy.ndimage import binary_dilation
+    from PIL import ImageFont
+    H, W = terrain.shape
+    classified = classify(terrain)
+
+    # UI scale relative to 1024×512 reference resolution
+    UI_SCALE = OUTPUT_W / 1024
+
+    # Base terrain color layer
+    rgb = np.zeros((H, W, 3), dtype=np.uint8)
+    for cls_id, color in enumerate(CLASS_COLORS):
+        mask = classified == cls_id
+        rgb[mask] = color
+
+    # Slight elevation shading on land (ambient occlusion approximation)
+    land_mask = terrain >= SEA_LEVEL
+    elev_norm = np.clip((terrain - SEA_LEVEL) / (1.0 - SEA_LEVEL + 1e-9), 0, 1)
+    shade = (0.85 + 0.15 * elev_norm)[..., np.newaxis]
+    rgb = np.where(land_mask[..., np.newaxis], (rgb * shade).astype(np.uint8), rgb)
+
+    # Dilate river mask at source resolution (preserves branching topology)
+    rivers_drawn = binary_dilation(rivers, iterations=2)
+    step(f"River cells after dilation: {rivers_drawn.sum()}")
+
+    # Upscale terrain with LANCZOS (smooth gradients) — WITHOUT rivers painted in yet
+    img = Image.fromarray(rgb).resize((OUTPUT_W, OUTPUT_H), Image.LANCZOS)
+
+    # Paint rivers AFTER upscale using NEAREST neighbor — no blur, sharp edges
+    rivers_up = Image.fromarray((rivers_drawn.astype(np.uint8) * 255)).resize(
+        (OUTPUT_W, OUTPUT_H), Image.NEAREST
+    )
+    img_arr = np.array(img)
+    img_arr[np.array(rivers_up) > 0] = PALETTE["river"]
+    img = Image.fromarray(img_arr)
+    draw = ImageDraw.Draw(img)
+
+    # Lat/lon grid lines (every 30°)
+    grid_w = max(1, int(UI_SCALE))
+    for lat_pct in [1/6, 2/6, 3/6, 4/6, 5/6]:
+        y = int(lat_pct * OUTPUT_H)
+        draw.line([(0, y), (OUTPUT_W, y)], fill=(80, 90, 100), width=grid_w)
+    for lon_pct in [1/6, 2/6, 3/6, 4/6, 5/6]:
+        x = int(lon_pct * OUTPUT_W)
+        draw.line([(x, 0), (x, OUTPUT_H)], fill=(80, 90, 100), width=grid_w)
+
+    # Fonts — load_default(size=N) requires Pillow 10+
+    try:
+        font_title = ImageFont.load_default(size=int(14 * UI_SCALE))
+        font_sub   = ImageFont.load_default(size=int(12 * UI_SCALE))
+        font_small = ImageFont.load_default(size=int(11 * UI_SCALE))
+    except TypeError:
+        font_title = font_sub = font_small = ImageFont.load_default()
+
+    # Title / metadata panel (insert chrome aesthetic)
+    panel_h = int(56 * UI_SCALE)
+    panel = Image.new('RGBA', (OUTPUT_W, panel_h), (15, 18, 25, 200))
+    img = img.convert('RGBA')
+    img.paste(panel, (0, 0), panel)
+    img = img.convert('RGB')
+    draw = ImageDraw.Draw(img)
+
+    px = int(12 * UI_SCALE)
+    py = int(8 * UI_SCALE)
+    lh = int(18 * UI_SCALE)
+    draw.text((px, py),        f"KALLAST  ·  GJ 144d  ·  Ran System", fill=PALETTE["title_text"], font=font_title)
+    draw.text((px, py + lh),   f"temperate / ocean / breathable / 0.95g / {PLANET['population']//1_000_000_000:.1f}B pop / Wave {PLANET['settlement_wave']} / {PLANET['settlement_age_years']}y settled", fill=(130, 140, 160), font=font_sub)
+    draw.text((px, py + lh*2), "HEIGHTMAP SPIKE v0.1 — Settled Reach Phase 1", fill=(80, 90, 110), font=font_small)
+
+    # Legend (bottom strip) — natural geographic features only
+    sw = int(16 * UI_SCALE)
+    legend_y = OUTPUT_H - int(40 * UI_SCALE)
+    legend_items = [
+        ("ocean",          PALETTE["ocean_deep"]),
+        ("coast",          PALETTE["coast"]),
+        ("plains",         PALETTE["plains"]),
+        ("grassland",      PALETTE["grassland"]),
+        ("mountain",       PALETTE["mountain"]),
+        ("river",          PALETTE["river"]),
+    ]
+    lx = px
+    for label, color in legend_items:
+        draw.rectangle([(lx, legend_y + int(4*UI_SCALE)), (lx+sw, legend_y + sw + int(4*UI_SCALE))], fill=color)
+        draw.text((lx + sw + int(4*UI_SCALE), legend_y + int(3*UI_SCALE)), label, fill=(180, 185, 200), font=font_small)
+        lx += int(110 * UI_SCALE)
+
+    img.save(output_path, format="PNG", optimize=False)
+    step(f"Saved: {output_path}")
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Main
+# ─────────────────────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate Kallast heightmap spike")
+    parser.add_argument("--output", default="spikes/heightmap-pipeline/kallast_heightmap.png")
+    parser.add_argument("--terrain-out", default="spikes/heightmap-pipeline/kallast_terrain.npy")
+    parser.add_argument("--seed", type=int, default=144042)  # GJ144d seed
+    parser.add_argument("--fast", action="store_true", help="Fewer tectonic steps (quicker, less detail)")
+    args = parser.parse_args()
+
+    print(f"\nKallast Heightmap Spike")
+    print(f"  Planet: {PLANET['name']} ({PLANET['body_id']})")
+    print(f"  Grid:   {GRID_W}×{GRID_H}")
+    print(f"  Seed:   {args.seed}")
+    print(f"  Mode:   {'fast' if args.fast else 'standard'}\n")
+
+    t0 = time.time()
+
+    terrain = run_tectonics(args.seed, fast=args.fast)
+    terrain = erode(terrain, passes=4)
+
+    # Set dynamic sea level based on target land coverage
+    global SEA_LEVEL
+    SEA_LEVEL = compute_sea_level(terrain, PLANET["land_fraction"])
+
+    rivers = compute_rivers(terrain, threshold=60)  # calibrated for 1024×512 grid; flat world needs lower relative threshold
+
+    settlements = place_settlements(terrain, KALLAST_CITIES)
+
+    render(terrain, rivers, args.output)
+
+    np.save(args.terrain_out, terrain)
+    step(f"Terrain grid saved: {args.terrain_out}")
+
+    # Write settlements JSON
+    sj_path = args.output.replace(".png", "_settlements.json")
+    with open(sj_path, "w") as f:
+        json.dump([
+            {"name": n, "grid_y": int(gy), "grid_x": int(gx), "tier": t}
+            for (n, gy, gx, t) in settlements
+        ], f, indent=2)
+    step(f"Settlement data saved: {sj_path}")
+
+    elapsed = time.time() - t0
+    print(f"\nDone in {elapsed:.1f}s")
+    print(f"Output: {args.output}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/spikes/heightmap-pipeline/kallast_heightmap.png b/spikes/heightmap-pipeline/kallast_heightmap.png
new file mode 100644
index 000000000..5cc737e1b
Binary files /dev/null and b/spikes/heightmap-pipeline/kallast_heightmap.png differ
diff --git a/spikes/heightmap-pipeline/kallast_heightmap_settlements.json b/spikes/heightmap-pipeline/kallast_heightmap_settlements.json
new file mode 100644
index 000000000..209eb67cd
--- /dev/null
+++ b/spikes/heightmap-pipeline/kallast_heightmap_settlements.json
@@ -0,0 +1,74 @@
+[
+  {
+    "name": "Kallast Prime",
+    "grid_y": 297,
+    "grid_x": 460,
+    "tier": 1
+  },
+  {
+    "name": "Ardenvall",
+    "grid_y": 182,
+    "grid_x": 246,
+    "tier": 2
+  },
+  {
+    "name": "Thessmark",
+    "grid_y": 266,
+    "grid_x": 613,
+    "tier": 2
+  },
+  {
+    "name": "Coldwater",
+    "grid_y": 316,
+    "grid_x": 272,
+    "tier": 2
+  },
+  {
+    "name": "Brightfield",
+    "grid_y": 172,
+    "grid_x": 512,
+    "tier": 2
+  },
+  {
+    "name": "Vorn's Crossing",
+    "grid_y": 293,
+    "grid_x": 399,
+    "tier": 3
+  },
+  {
+    "name": "Saltmere",
+    "grid_y": 195,
+    "grid_x": 707,
+    "tier": 3
+  },
+  {
+    "name": "Kaspel",
+    "grid_y": 214,
+    "grid_x": 214,
+    "tier": 3
+  },
+  {
+    "name": "Drenmark",
+    "grid_y": 296,
+    "grid_x": 737,
+    "tier": 3
+  },
+  {
+    "name": "New Farrow",
+    "grid_y": 327,
+    "grid_x": 563,
+    "tier": 3
+  },
+  {
+    "name": "Ossenfield",
+    "grid_y": 155,
+    "grid_x": 433,
+    "tier": 3
+  },
+  {
+    "name": "Tyne Station",
+    "grid_y": 300,
+    "grid_x": 322,
+    "tier": 3
+  }
+]
\ No newline at end of file
diff --git a/spikes/heightmap-pipeline/kallast_terrain.npy b/spikes/heightmap-pipeline/kallast_terrain.npy
new file mode 100644
index 000000000..329abbb9e
Binary files /dev/null and b/spikes/heightmap-pipeline/kallast_terrain.npy differ