Merge feature/offroute: effort-based wilderness routing, PostGIS entry points, BLM trail filtering

- Segment breaks on: bearing change >30°, grade category change, distance >0.5mi
- Grade categories: flat (0-2°), gentle (2-5°), moderate (5-10°), steep (10-15°), very steep (15°+)
- Distance formatting: feet with commas <1mi, miles with decimal ≥1mi
- Instruction format: Head {cardinal}, gaining/descending X ft ({grade} uphill/downhill) — {dist}

Co-Authored-By: Claude <noreply@anthropic.com>

config/profiles/home.yaml

@@ -6,13 +6,13 @@ profile: home
 region_name: "North America"
 
 tileset:
-  url: "/tiles/na.pmtiles"
+  url: "/tiles/planet/current.pmtiles"
   bounds: [-168, 14, -52, 72]
   max_zoom: 15
   attribution: "Protomaps © OSM"
 
 tileset_hillshade:
-  url: "/tiles/hillshade-na.pmtiles"
+  url: "/tiles/planet-dem.pmtiles"
   encoding: "terrarium"
   max_zoom: 12
 
@@ -33,14 +33,14 @@ services:
 
 features:
   has_nominatim_details: true
-  has_kiwix_wiki: false
+  has_kiwix_wiki: true
   has_hillshade: true
   has_3d_terrain: false
   has_traffic_overlay: true
   has_landclass: true
   has_public_lands_layer: true
   has_contours: true
-  has_contours_test: true
+  has_contours_test: false
   has_contours_test_10ft: false
   has_address_book_write: false
   has_overture_enrichment: true
@@ -48,7 +48,16 @@ features:
   has_contacts: true
   has_wiki_rewriting: true
   has_wiki_discovery: false
+  has_usfs_trails: true
+  has_blm_trails: true
 
 defaults:
   center: [42.5736, -114.6066]
   zoom: 10
+
+# Offroute wilderness routing
+offroute:
+  osm_pbf_path: "/mnt/nav/sources/idaho-latest.osm.pbf"
+  densify_interval_m: 100
+  postgis_dsn: "dbname=padus"
+

lib/api.py

@@ -2722,3 +2722,214 @@ def api_auth_whoami():
         'authenticated': False,
         'username': None,
     })
+
+
+# ── OFFROUTE API ──
+
+@app.route("/api/offroute", methods=["POST"])
+def api_offroute():
+    """
+    Off-network routing from wilderness to destination.
+
+    Request body:
+        {
+            "start": [lat, lon],
+            "end": [lat, lon],
+            "mode": "foot" | "mtb" | "atv",  (default: "foot")
+            "boundary_mode": "strict" | "pragmatic" | "emergency"  (default: "pragmatic")
+        }
+
+    Response:
+        {
+            "status": "ok",
+            "route": { GeoJSON FeatureCollection with wilderness + network segments },
+            "summary": { total_distance_km, total_effort_minutes, ... }
+        }
+    """
+    try:
+        data = request.get_json()
+        if not data:
+            return jsonify({"status": "error", "message": "No JSON body provided"}), 400
+
+        # Parse coordinates
+        start = data.get("start")
+        end = data.get("end")
+
+        if not start or not end:
+            return jsonify({"status": "error", "message": "Missing start or end coordinates"}), 400
+
+        if not isinstance(start, (list, tuple)) or len(start) != 2:
+            return jsonify({"status": "error", "message": "start must be [lat, lon]"}), 400
+        if not isinstance(end, (list, tuple)) or len(end) != 2:
+            return jsonify({"status": "error", "message": "end must be [lat, lon]"}), 400
+
+        start_lat, start_lon = float(start[0]), float(start[1])
+        end_lat, end_lon = float(end[0]), float(end[1])
+
+        # Parse options
+        mode = data.get("mode", "foot")
+        if mode not in ("auto", "foot", "mtb", "atv", "vehicle"):
+            return jsonify({"status": "error", "message": "mode must be auto, foot, mtb, atv, or vehicle"}), 400
+
+        boundary_mode = data.get("boundary_mode", "pragmatic")
+        if boundary_mode not in ("strict", "pragmatic", "emergency"):
+            return jsonify({"status": "error", "message": "boundary_mode must be strict, pragmatic, or emergency"}), 400
+
+        # Import and run router
+        from .offroute.router import OffrouteRouter
+
+        router = OffrouteRouter()
+        try:
+            result = router.route(
+                start_lat=start_lat,
+                start_lon=start_lon,
+                end_lat=end_lat,
+                end_lon=end_lon,
+                mode=mode,
+                boundary_mode=boundary_mode
+            )
+        finally:
+            router.close()
+
+        if result.get("status") == "error":
+            return jsonify(result), 400
+
+        return jsonify(result)
+
+    except Exception as e:
+        logger.exception("Offroute error")
+        return jsonify({"status": "error", "message": str(e)}), 500
+
+
+# ── MVUM Places Panel API ──
+
+@app.route("/api/mvum", methods=["GET"])
+def api_mvum():
+    """
+    Query MVUM (Motor Vehicle Use Map) features near a point.
+
+    Used by the Navi frontend places panel when a user taps near a road/trail.
+
+    Query params:
+        lat: Latitude
+        lon: Longitude
+        radius: Search radius in meters (default: 50)
+
+    Response:
+        {
+            "status": "ok",
+            "feature": {
+                "id": "FR 123",
+                "name": "Some Forest Road",
+                "forest": "Sawtooth National Forest",
+                "district": "Ketchum Ranger District",
+                "surface": "NAT",
+                "maintenance_level": 2,
+                "seasonal": "Seasonal",
+                "symbol": 2,
+                "access": {
+                    "passenger_vehicle": { "status": "Open", "dates": "06/15-10/15" },
+                    "high_clearance": { "status": "Open", "dates": "06/15-10/15" },
+                    "atv": { "status": "Open", "dates": "06/15-10/15" },
+                    ...
+                },
+                "geometry": { GeoJSON LineString }
+            }
+        }
+
+    If no MVUM feature within radius:
+        { "status": "ok", "feature": null }
+    """
+    try:
+        lat = request.args.get("lat", type=float)
+        lon = request.args.get("lon", type=float)
+        radius = request.args.get("radius", 50, type=float)
+
+        if lat is None or lon is None:
+            return jsonify({"status": "error", "message": "lat and lon required"}), 400
+
+        from .offroute.mvum import MVUMReader
+
+        reader = MVUMReader()
+        try:
+            # Try roads first, then trails
+            feature = reader.query_nearest(lat, lon, radius, "mvum_roads")
+            if feature is None:
+                feature = reader.query_nearest(lat, lon, radius, "mvum_trails")
+
+            if feature is None:
+                return jsonify({"status": "ok", "feature": None})
+
+            # Format access info
+            access = {
+                "passenger_vehicle": {
+                    "status": feature.get("passengervehicle"),
+                    "dates": feature.get("passengervehicle_datesopen")
+                },
+                "high_clearance": {
+                    "status": feature.get("highclearancevehicle"),
+                    "dates": feature.get("highclearancevehicle_datesopen")
+                },
+                "atv": {
+                    "status": feature.get("atv"),
+                    "dates": feature.get("atv_datesopen")
+                },
+                "motorcycle": {
+                    "status": feature.get("motorcycle"),
+                    "dates": feature.get("motorcycle_datesopen")
+                },
+                "4wd_gt50": {
+                    "status": feature.get("fourwd_gt50inches"),
+                    "dates": feature.get("fourwd_gt50_datesopen")
+                },
+                "2wd_gt50": {
+                    "status": feature.get("twowd_gt50inches"),
+                    "dates": feature.get("twowd_gt50_datesopen")
+                },
+                "e_bike_class1": {
+                    "status": feature.get("e_bike_class1"),
+                    "dates": feature.get("e_bike_class1_dur")
+                },
+                "e_bike_class2": {
+                    "status": feature.get("e_bike_class2"),
+                    "dates": feature.get("e_bike_class2_dur")
+                },
+                "e_bike_class3": {
+                    "status": feature.get("e_bike_class3"),
+                    "dates": feature.get("e_bike_class3_dur")
+                },
+            }
+
+            # Parse maintenance level
+            maint_level = feature.get("operationalmaintlevel", "")
+            maint_num = None
+            if maint_level:
+                # Extract first digit: "2 - HIGH CLEARANCE VEHICLES" -> 2
+                import re
+                match = re.match(r"(\d+)", maint_level)
+                if match:
+                    maint_num = int(match.group(1))
+
+            result = {
+                "id": feature.get("id"),
+                "name": feature.get("name"),
+                "forest": feature.get("forestname"),
+                "district": feature.get("districtname"),
+                "surface": feature.get("surfacetype"),
+                "maintenance_level": maint_num,
+                "seasonal": feature.get("seasonal"),
+                "symbol": feature.get("symbol"),
+                "trail_class": feature.get("trailclass"),
+                "trail_system": feature.get("trailsystem"),
+                "access": access,
+                "geometry": feature.get("geojson")
+            }
+
+            return jsonify({"status": "ok", "feature": result})
+
+        finally:
+            reader.close()
+
+    except Exception as e:
+        logger.exception("MVUM query error")
+        return jsonify({"status": "error", "message": str(e)}), 500

lib/extractor.py

@@ -21,6 +21,7 @@ Config: processing.extract_workers, processing.max_pdf_size_mb,
         processing.extract_timeout, processing.page_timeout
 """
 import base64
+import re
 import json
 import os
 import random
@@ -99,6 +100,40 @@ def _is_transient(error_str):
     return any(sig in s for sig in transient_signals)
 
 
+def _text_quality_ok(text, min_length=50):
+    """Check if extracted text meets quality thresholds.
+
+    Beyond the basic length check, validates:
+    - Word-boundary ratio: at least 60% of tokens should be real words (2+ alpha chars)
+    - Concatenation ratio: lowercase-immediately-followed-by-uppercase shouldn't exceed 10% of word count
+
+    Returns True if text passes all checks.
+    """
+    text = text.strip()
+    if len(text) < min_length:
+        return False
+
+    words = text.split()
+    if not words:
+        return False
+
+    # Word-like ratio: tokens with 2+ alphabetic characters
+    word_like = sum(1 for w in words if len(re.findall(r'[a-zA-Z]', w)) >= 2)
+    word_ratio = word_like / len(words)
+    if word_ratio < 0.60:
+        return False
+
+    # Concatenation detector: lowercase immediately followed by uppercase
+    # Filter out common camelCase patterns in code (short tokens)
+    concat_hits = len(re.findall(r'[a-z][A-Z]', text))
+    concat_ratio = concat_hits / len(words) if words else 0
+    if concat_ratio > 0.10:
+        return False
+
+    return True
+
+
+
 def _render_page_to_png(pdf_path, page_num_1indexed, dpi=200, timeout=30):
     """Render a single PDF page to PNG bytes using pdftoppm.
 
@@ -224,7 +259,7 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
     # Method 1: pdftotext (poppler)
     try:
         result = subprocess.run(
-            ['pdftotext', '-f', str(page_num_0indexed + 1),
+            ['pdftotext', '-layout', '-f', str(page_num_0indexed + 1),
              '-l', str(page_num_0indexed + 1), pdf_path, '-'],
             capture_output=True, text=True, timeout=page_timeout
         )
@@ -233,7 +268,7 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
     except Exception:
         pass
 
-    if len(text.strip()) >= 50:
+    if _text_quality_ok(text):
         return text, 'pdftotext'
 
     # Method 2: pdftoppm + Tesseract OCR
@@ -258,7 +293,7 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
     except Exception:
         pass
 
-    if len(text.strip()) >= 50:
+    if _text_quality_ok(text):
         return text, 'tesseract'
 
     # Method 3: Gemini Vision (last resort)
@@ -276,8 +311,26 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
 # ── Core extraction functions ──
 
 def _pypdf2_extract(reader, page_num):
-    """Extract text from a PyPDF2 page object. Runs inside a thread for timeout."""
-    return reader.pages[page_num].extract_text() or ''
+    """Extract text from a PyPDF2 page object. Runs inside a thread for timeout.
+
+    Tries default extraction first (space_width=200). If quality check fails,
+    retries with space_width=100 which better detects word boundaries in
+    tightly-kerned PDFs (common in Haynes/workshop manuals).
+
+    Note: PyPDF2 3.0.1 does not support layout=True. The space_width parameter
+    controls word-boundary detection tolerance. Lower values = more aggressive
+    space insertion between characters.
+    """
+    text = reader.pages[page_num].extract_text() or ''
+    if _text_quality_ok(text):
+        return text
+
+    # Retry with tighter word-boundary detection
+    text_tight = reader.pages[page_num].extract_text(space_width=100.0) or ''
+    if len(text_tight.strip()) >= len(text.strip()):
+        return text_tight
+
+    return text
 
 
 def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
@@ -302,13 +355,13 @@ def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
     except Exception:
         text = ''
 
-    if len(text.strip()) >= 50:
+    if _text_quality_ok(text):
         return text, 'pypdf2'
 
     # Method 2: pdftotext via subprocess (inherently timeout-safe)
     try:
         result = subprocess.run(
-            ['pdftotext', '-f', str(page_num + 1), '-l', str(page_num + 1), pdf_path, '-'],
+            ['pdftotext', '-layout', '-f', str(page_num + 1), '-l', str(page_num + 1), pdf_path, '-'],
             capture_output=True, text=True, timeout=page_timeout
         )
         if result.returncode == 0 and len(result.stdout.strip()) > len(text.strip()):
@@ -316,7 +369,7 @@ def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
     except Exception:
         pass
 
-    if len(text.strip()) >= 50:
+    if _text_quality_ok(text):
         return text, 'pdftotext'
 
     # Method 3: pdftoppm + Tesseract OCR
@@ -340,7 +393,7 @@ def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
     except Exception:
         pass
 
-    if len(text.strip()) >= 50:
+    if _text_quality_ok(text):
         return text, 'tesseract'
 
     # Method 4: Gemini Vision (last resort — costs API calls but handles scanned docs)

lib/offroute/__init__.py

@@ -0,0 +1 @@
+"""OFFROUTE: Off-network effort-based routing module."""

lib/offroute/barriers.py

@@ -0,0 +1,440 @@
+"""
+PAD-US barrier and wilderness layers for OFFROUTE.
+
+Provides access to:
+1. Barrier raster (Pub_Access = 'XA' - closed/restricted areas)
+2. Wilderness raster (Des_Tp = 'WA' - designated wilderness areas)
+
+Build functions rasterize PAD-US geodatabase to aligned GeoTIFFs.
+Runtime functions read the rasters and resample to match elevation grids.
+"""
+import numpy as np
+from pathlib import Path
+from typing import Tuple, Optional
+import subprocess
+import tempfile
+import os
+
+try:
+    import rasterio
+    from rasterio.windows import from_bounds
+    from rasterio.enums import Resampling
+except ImportError:
+    raise ImportError("rasterio is required for barriers layer support")
+
+# Paths
+DEFAULT_BARRIERS_PATH = Path("/mnt/nav/worldcover/padus_barriers.tif")
+DEFAULT_WILDERNESS_PATH = Path("/mnt/nav/worldcover/wilderness.tif")
+PADUS_GDB_PATH = Path("/mnt/nav/padus/PADUS4_0_Geodatabase.gdb")
+PADUS_LAYER = "PADUS4_0Combined_Proclamation_Marine_Fee_Designation_Easement"
+
+# CONUS bounding box in WGS84
+CONUS_BOUNDS = {
+    "west": -125.0,
+    "east": -66.0,
+    "south": 24.0,
+    "north": 50.0,
+}
+
+# Resolution in degrees (~30m at mid-latitudes)
+PIXEL_SIZE = 0.0003  # ~33m
+
+
+class BarrierReader:
+    """Reader for PAD-US barrier raster (closed/restricted areas)."""
+
+    def __init__(self, barrier_path: Path = DEFAULT_BARRIERS_PATH):
+        self.barrier_path = barrier_path
+        self._dataset = None
+
+    def _open(self):
+        """Lazy open the dataset."""
+        if self._dataset is None:
+            if not self.barrier_path.exists():
+                raise FileNotFoundError(
+                    f"Barrier raster not found at {self.barrier_path}. "
+                    f"Run build_barriers_raster() first."
+                )
+            self._dataset = rasterio.open(self.barrier_path)
+        return self._dataset
+
+    def get_barrier_grid(
+        self,
+        south: float,
+        north: float,
+        west: float,
+        east: float,
+        target_shape: Tuple[int, int]
+    ) -> np.ndarray:
+        """
+        Get barrier values for a bounding box, resampled to target shape.
+
+        Args:
+            south, north, west, east: Bounding box coordinates (WGS84)
+            target_shape: (rows, cols) to resample to (matches elevation grid)
+
+        Returns:
+            np.ndarray of uint8 barrier values:
+                255 = closed/restricted (impassable when respect_boundaries=True)
+                0 = public/accessible
+        """
+        ds = self._open()
+        window = from_bounds(west, south, east, north, ds.transform)
+        barriers = ds.read(
+            1,
+            window=window,
+            out_shape=target_shape,
+            resampling=Resampling.nearest
+        )
+        return barriers
+
+    def sample_point(self, lat: float, lon: float) -> int:
+        """Sample barrier value at a single point."""
+        ds = self._open()
+        row, col = ds.index(lon, lat)
+        if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
+            return 0
+        window = rasterio.windows.Window(col, row, 1, 1)
+        value = ds.read(1, window=window)
+        return int(value[0, 0])
+
+    def close(self):
+        """Close the dataset."""
+        if self._dataset is not None:
+            self._dataset.close()
+            self._dataset = None
+
+
+class WildernessReader:
+    """Reader for PAD-US wilderness raster (designated wilderness areas)."""
+
+    def __init__(self, wilderness_path: Path = DEFAULT_WILDERNESS_PATH):
+        self.wilderness_path = wilderness_path
+        self._dataset = None
+
+    def _open(self):
+        """Lazy open the dataset."""
+        if self._dataset is None:
+            if not self.wilderness_path.exists():
+                raise FileNotFoundError(
+                    f"Wilderness raster not found at {self.wilderness_path}. "
+                    f"Run build_wilderness_raster() first."
+                )
+            self._dataset = rasterio.open(self.wilderness_path)
+        return self._dataset
+
+    def get_wilderness_grid(
+        self,
+        south: float,
+        north: float,
+        west: float,
+        east: float,
+        target_shape: Tuple[int, int]
+    ) -> np.ndarray:
+        """
+        Get wilderness values for a bounding box, resampled to target shape.
+
+        Args:
+            south, north, west, east: Bounding box coordinates (WGS84)
+            target_shape: (rows, cols) to resample to (matches elevation grid)
+
+        Returns:
+            np.ndarray of uint8 wilderness values:
+                255 = designated wilderness area
+                0 = not wilderness
+        """
+        ds = self._open()
+        window = from_bounds(west, south, east, north, ds.transform)
+        wilderness = ds.read(
+            1,
+            window=window,
+            out_shape=target_shape,
+            resampling=Resampling.nearest
+        )
+        return wilderness
+
+    def sample_point(self, lat: float, lon: float) -> int:
+        """Sample wilderness value at a single point."""
+        ds = self._open()
+        row, col = ds.index(lon, lat)
+        if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
+            return 0
+        window = rasterio.windows.Window(col, row, 1, 1)
+        value = ds.read(1, window=window)
+        return int(value[0, 0])
+
+    def close(self):
+        """Close the dataset."""
+        if self._dataset is not None:
+            self._dataset.close()
+            self._dataset = None
+
+
+def build_barriers_raster(
+    output_path: Path = DEFAULT_BARRIERS_PATH,
+    gdb_path: Path = PADUS_GDB_PATH,
+    pixel_size: float = PIXEL_SIZE,
+    bounds: dict = CONUS_BOUNDS,
+) -> Path:
+    """
+    Build the PAD-US barriers raster from the source geodatabase.
+
+    Extracts polygons where Pub_Access = 'XA' (Closed) and rasterizes them.
+    """
+    import shutil
+
+    if not gdb_path.exists():
+        raise FileNotFoundError(f"PAD-US geodatabase not found at {gdb_path}")
+
+    if not shutil.which('ogr2ogr'):
+        raise RuntimeError("ogr2ogr not found. Install GDAL.")
+    if not shutil.which('gdal_rasterize'):
+        raise RuntimeError("gdal_rasterize not found. Install GDAL.")
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    print(f"Building PAD-US barriers raster...")
+    print(f"  Source: {gdb_path}")
+    print(f"  Output: {output_path}")
+    print(f"  Pixel size: {pixel_size} degrees (~{pixel_size * 111000:.0f}m)")
+    print(f"  Bounds: {bounds}")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        closed_gpkg = Path(tmpdir) / "closed_areas.gpkg"
+
+        print(f"\n[1/3] Extracting closed areas (Pub_Access = 'XA')...")
+
+        ogr_cmd = [
+            "ogr2ogr",
+            "-f", "GPKG",
+            str(closed_gpkg),
+            str(gdb_path),
+            PADUS_LAYER,
+            "-where", "Pub_Access = 'XA'",
+            "-t_srs", "EPSG:4326",
+            "-nlt", "MULTIPOLYGON",
+            "-nln", "closed_areas",
+        ]
+
+        result = subprocess.run(ogr_cmd, capture_output=True, text=True)
+        if result.returncode != 0:
+            print(f"STDERR: {result.stderr}")
+            raise RuntimeError(f"ogr2ogr failed: {result.stderr}")
+
+        info_cmd = ["ogrinfo", "-so", str(closed_gpkg), "closed_areas"]
+        info_result = subprocess.run(info_cmd, capture_output=True, text=True)
+        print(f"  Extraction result:\n{info_result.stdout}")
+
+        print(f"\n[2/3] Creating raster grid...")
+
+        width = int((bounds['east'] - bounds['west']) / pixel_size)
+        height = int((bounds['north'] - bounds['south']) / pixel_size)
+        print(f"  Grid size: {width} x {height} pixels")
+
+        print(f"\n[3/3] Rasterizing closed areas...")
+
+        rasterize_cmd = [
+            "gdal_rasterize",
+            "-burn", "255",
+            "-init", "0",
+            "-a_nodata", "0",
+            "-te", str(bounds['west']), str(bounds['south']),
+                   str(bounds['east']), str(bounds['north']),
+            "-tr", str(pixel_size), str(pixel_size),
+            "-ot", "Byte",
+            "-co", "COMPRESS=LZW",
+            "-co", "TILED=YES",
+            "-l", "closed_areas",
+            str(closed_gpkg),
+            str(output_path),
+        ]
+
+        result = subprocess.run(rasterize_cmd, capture_output=True, text=True)
+        if result.returncode != 0:
+            print(f"STDERR: {result.stderr}")
+            raise RuntimeError(f"gdal_rasterize failed: {result.stderr}")
+
+    print(f"\n[Done] Verifying output...")
+    with rasterio.open(output_path) as ds:
+        print(f"  Size: {ds.width} x {ds.height}")
+        print(f"  CRS: {ds.crs}")
+        sample = ds.read(1, window=rasterio.windows.Window(0, 0, 1000, 1000))
+        closed_count = np.sum(sample == 255)
+        print(f"  Sample (1000x1000): {closed_count} closed cells")
+
+    file_size = output_path.stat().st_size / (1024**2)
+    print(f"  File size: {file_size:.1f} MB")
+
+    return output_path
+
+
+def build_wilderness_raster(
+    output_path: Path = DEFAULT_WILDERNESS_PATH,
+    gdb_path: Path = PADUS_GDB_PATH,
+    pixel_size: float = PIXEL_SIZE,
+    bounds: dict = CONUS_BOUNDS,
+) -> Path:
+    """
+    Build the PAD-US wilderness raster from the source geodatabase.
+
+    Extracts polygons where Des_Tp = 'WA' (Wilderness Area) and rasterizes them.
+    """
+    import shutil
+
+    if not gdb_path.exists():
+        raise FileNotFoundError(f"PAD-US geodatabase not found at {gdb_path}")
+
+    if not shutil.which('ogr2ogr'):
+        raise RuntimeError("ogr2ogr not found. Install GDAL.")
+    if not shutil.which('gdal_rasterize'):
+        raise RuntimeError("gdal_rasterize not found. Install GDAL.")
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    print(f"Building PAD-US wilderness raster...")
+    print(f"  Source: {gdb_path}")
+    print(f"  Output: {output_path}")
+    print(f"  Pixel size: {pixel_size} degrees (~{pixel_size * 111000:.0f}m)")
+    print(f"  Bounds: {bounds}")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        wilderness_gpkg = Path(tmpdir) / "wilderness_areas.gpkg"
+
+        print(f"\n[1/3] Extracting wilderness areas (Des_Tp = 'WA')...")
+
+        ogr_cmd = [
+            "ogr2ogr",
+            "-f", "GPKG",
+            str(wilderness_gpkg),
+            str(gdb_path),
+            PADUS_LAYER,
+            "-where", "Des_Tp = 'WA'",
+            "-t_srs", "EPSG:4326",
+            "-nlt", "MULTIPOLYGON",
+            "-nln", "wilderness_areas",
+        ]
+
+        result = subprocess.run(ogr_cmd, capture_output=True, text=True)
+        if result.returncode != 0:
+            print(f"STDERR: {result.stderr}")
+            raise RuntimeError(f"ogr2ogr failed: {result.stderr}")
+
+        info_cmd = ["ogrinfo", "-so", str(wilderness_gpkg), "wilderness_areas"]
+        info_result = subprocess.run(info_cmd, capture_output=True, text=True)
+        print(f"  Extraction result:\n{info_result.stdout}")
+
+        print(f"\n[2/3] Creating raster grid...")
+
+        width = int((bounds['east'] - bounds['west']) / pixel_size)
+        height = int((bounds['north'] - bounds['south']) / pixel_size)
+        print(f"  Grid size: {width} x {height} pixels")
+
+        print(f"\n[3/3] Rasterizing wilderness areas...")
+
+        rasterize_cmd = [
+            "gdal_rasterize",
+            "-burn", "255",
+            "-init", "0",
+            "-a_nodata", "0",
+            "-te", str(bounds['west']), str(bounds['south']),
+                   str(bounds['east']), str(bounds['north']),
+            "-tr", str(pixel_size), str(pixel_size),
+            "-ot", "Byte",
+            "-co", "COMPRESS=LZW",
+            "-co", "TILED=YES",
+            "-l", "wilderness_areas",
+            str(wilderness_gpkg),
+            str(output_path),
+        ]
+
+        result = subprocess.run(rasterize_cmd, capture_output=True, text=True)
+        if result.returncode != 0:
+            print(f"STDERR: {result.stderr}")
+            raise RuntimeError(f"gdal_rasterize failed: {result.stderr}")
+
+    print(f"\n[Done] Verifying output...")
+    with rasterio.open(output_path) as ds:
+        print(f"  Size: {ds.width} x {ds.height}")
+        print(f"  CRS: {ds.crs}")
+        sample = ds.read(1, window=rasterio.windows.Window(0, 0, 1000, 1000))
+        wilderness_count = np.sum(sample == 255)
+        print(f"  Sample (1000x1000): {wilderness_count} wilderness cells")
+
+    file_size = output_path.stat().st_size / (1024**2)
+    print(f"  File size: {file_size:.1f} MB")
+
+    return output_path
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) > 1:
+        cmd = sys.argv[1]
+
+        if cmd == "build":
+            print("=" * 60)
+            print("PAD-US Barriers Raster Build")
+            print("=" * 60)
+            build_barriers_raster()
+
+        elif cmd == "build-wilderness":
+            print("=" * 60)
+            print("PAD-US Wilderness Raster Build")
+            print("=" * 60)
+            build_wilderness_raster()
+
+        elif cmd == "build-all":
+            print("=" * 60)
+            print("Building all PAD-US rasters")
+            print("=" * 60)
+            build_barriers_raster()
+            print("\n")
+            build_wilderness_raster()
+
+        else:
+            print(f"Unknown command: {cmd}")
+            print("Usage:")
+            print("  python barriers.py build           # Build barriers raster")
+            print("  python barriers.py build-wilderness # Build wilderness raster")
+            print("  python barriers.py build-all       # Build both rasters")
+            sys.exit(1)
+
+    else:
+        # Test readers
+        print("Testing BarrierReader...")
+
+        if not DEFAULT_BARRIERS_PATH.exists():
+            print(f"Barrier raster not found at {DEFAULT_BARRIERS_PATH}")
+            print(f"Run: python barriers.py build")
+            sys.exit(1)
+
+        reader = BarrierReader()
+        barriers = reader.get_barrier_grid(
+            south=42.2, north=42.6, west=-114.8, east=-113.8,
+            target_shape=(400, 1000)
+        )
+        print(f"\nBarrier grid shape: {barriers.shape}")
+        print(f"Unique values: {np.unique(barriers)}")
+        closed_cells = np.sum(barriers == 255)
+        print(f"Closed cells: {closed_cells} ({100*closed_cells/barriers.size:.2f}%)")
+        reader.close()
+
+        print("\nTesting WildernessReader...")
+
+        if not DEFAULT_WILDERNESS_PATH.exists():
+            print(f"Wilderness raster not found at {DEFAULT_WILDERNESS_PATH}")
+            print(f"Run: python barriers.py build-wilderness")
+        else:
+            wilderness_reader = WildernessReader()
+            wilderness = wilderness_reader.get_wilderness_grid(
+                south=42.2, north=42.6, west=-114.8, east=-113.8,
+                target_shape=(400, 1000)
+            )
+            print(f"Wilderness grid shape: {wilderness.shape}")
+            print(f"Unique values: {np.unique(wilderness)}")
+            wilderness_cells = np.sum(wilderness == 255)
+            print(f"Wilderness cells: {wilderness_cells} ({100*wilderness_cells/wilderness.size:.2f}%)")
+            wilderness_reader.close()
+
+        print("\nDone.")

lib/offroute/cost.py

@@ -0,0 +1,494 @@
+"""
+Multi-mode travel cost functions for OFFROUTE.
+
+Supports four travel modes: foot, mtb, atv, vehicle.
+Each mode has its own speed function, max slope, trail access rules,
+and terrain friction overrides.
+
+Mode profiles are data-driven — adding a new mode means adding a profile entry.
+"""
+import math
+import numpy as np
+from dataclasses import dataclass, field
+from typing import Optional, Literal, Dict, Callable
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# SPEED FUNCTIONS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def tobler_off_path_speed(grade: np.ndarray, base_speed: float = 6.0) -> np.ndarray:
+    """
+    Tobler off-path hiking function.
+
+    W = 0.6 * base_speed * exp(-3.5 * |S + 0.05|)
+
+    Peak ~3.6 km/h at grade = -0.05 (slight downhill).
+    The 0.6 multiplier is the off-trail penalty.
+    """
+    return 0.6 * base_speed * np.exp(-3.5 * np.abs(grade + 0.05))
+
+
+def herzog_wheeled_speed(grade: np.ndarray, base_speed: float = 12.0) -> np.ndarray:
+    """
+    Herzog wheeled-transport polynomial.
+
+    Relative speed factor:
+    1 / (1337.8·S^6 + 278.19·S^5 − 517.39·S^4 − 78.199·S^3 + 93.419·S^2 + 19.825·|S| + 1.64)
+
+    Multiply by base_speed to get km/h.
+    """
+    S = grade
+    S_abs = np.abs(S)
+
+    # Herzog polynomial (returns relative speed factor 0-1)
+    denom = (1337.8 * S**6 + 278.19 * S**5 - 517.39 * S**4
+             - 78.199 * S**3 + 93.419 * S**2 + 19.825 * S_abs + 1.64)
+
+    # Avoid division by zero and negative speeds
+    denom = np.maximum(denom, 0.1)
+    rel_speed = 1.0 / denom
+
+    # Clamp relative speed to reasonable bounds (0.05 to 1.5)
+    rel_speed = np.clip(rel_speed, 0.05, 1.5)
+
+    return base_speed * rel_speed
+
+
+def linear_degrade_speed(grade: np.ndarray, base_speed: float = 40.0, max_grade: float = 0.364) -> np.ndarray:
+    """
+    Linear speed degradation with slope.
+
+    speed = base_speed * max(0, 1 - |grade| / max_grade)
+
+    max_grade = tan(20°) ≈ 0.364 for 20° max slope.
+    """
+    speed = base_speed * np.maximum(0, 1.0 - np.abs(grade) / max_grade)
+    return np.maximum(speed, 0.1)  # Minimum crawl speed
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# MODE PROFILES (Data-driven configuration)
+# ═══════════════════════════════════════════════════════════════════════════════
+
+@dataclass
+class ModeProfile:
+    """Configuration for a travel mode."""
+
+    name: str
+    description: str
+
+    # Speed function parameters
+    speed_function: str  # "tobler", "herzog", "linear"
+    base_speed_kmh: float
+    max_slope_deg: float
+
+    # Trail access: trail_value -> friction multiplier (None = impassable)
+    # Trail values: 5=road, 15=track, 25=foot trail
+    trail_friction: Dict[int, Optional[float]] = field(default_factory=dict)
+
+    # Off-trail terrain friction overrides (by WorldCover class)
+    # These MULTIPLY the base WorldCover friction
+    # None = use default, np.inf = impassable
+    # WorldCover values: 10=tree, 20=shrub, 30=grass, 40=crop, 50=urban,
+    #                    60=bare, 80=water, 90=wetland, 95=mangrove, 100=moss
+    terrain_friction_override: Dict[int, Optional[float]] = field(default_factory=dict)
+
+    # Should wilderness areas be impassable?
+    wilderness_impassable: bool = False
+
+    # For vehicle mode: can traverse off-trail flat terrain?
+    off_trail_flat_threshold_deg: float = 0.0  # 0 = no off-trail allowed
+    off_trail_flat_friction: float = np.inf  # friction if allowed
+
+
+# Define all mode profiles
+MODE_PROFILES: Dict[str, ModeProfile] = {
+    "foot": ModeProfile(
+        name="foot",
+        description="Hiking on foot (Tobler off-path model)",
+        speed_function="tobler",
+        base_speed_kmh=6.0,
+        max_slope_deg=40.0,
+        trail_friction={
+            5: 0.1,   # road
+            15: 0.3,  # track
+            25: 0.5,  # foot trail
+        },
+        terrain_friction_override={
+            # Use default WorldCover friction for foot mode
+        },
+        wilderness_impassable=False,
+    ),
+
+    "mtb": ModeProfile(
+        name="mtb",
+        description="Mountain bike / dirt bike (Herzog wheeled model)",
+        speed_function="herzog",
+        base_speed_kmh=12.0,
+        max_slope_deg=25.0,
+        trail_friction={
+            5: 0.1,   # road
+            15: 0.2,  # track
+            25: 0.5,  # foot trail (rideable but slow)
+        },
+        terrain_friction_override={
+            30: 2.0,   # Grassland: rideable but slow
+            20: 4.0,   # Shrubland: barely rideable
+            10: 8.0,   # Tree cover/forest: effectively impassable
+            60: 3.0,   # Bare/rocky
+            90: np.inf,  # Wetland: impassable
+            95: np.inf,  # Mangrove: impassable
+            80: np.inf,  # Water: impassable
+        },
+        wilderness_impassable=True,
+    ),
+
+    "atv": ModeProfile(
+        name="atv",
+        description="ATV / side-by-side (Herzog wheeled model, higher base speed)",
+        speed_function="herzog",
+        base_speed_kmh=25.0,
+        max_slope_deg=30.0,
+        trail_friction={
+            5: 0.1,    # road
+            15: 0.3,   # track
+            25: None,  # foot trail: impassable (too narrow)
+        },
+        terrain_friction_override={
+            30: 1.5,   # Grassland: passable
+            20: 3.0,   # Shrubland: rough
+            10: np.inf,  # Forest: impassable
+            60: 2.0,   # Bare/rocky
+            90: np.inf,  # Wetland: impassable
+            95: np.inf,  # Mangrove: impassable
+            80: np.inf,  # Water: impassable
+        },
+        wilderness_impassable=True,
+    ),
+
+    "vehicle": ModeProfile(
+        name="vehicle",
+        description="4x4 truck / jeep (linear speed degradation)",
+        speed_function="linear",
+        base_speed_kmh=40.0,
+        max_slope_deg=20.0,
+        trail_friction={
+            5: 0.1,    # road
+            15: 0.5,   # track (rough but passable)
+            25: None,  # foot trail: impassable
+        },
+        terrain_friction_override={
+            # All off-trail terrain is impassable by default
+            10: np.inf,  # Forest
+            20: np.inf,  # Shrubland
+            30: np.inf,  # Grassland (except flat - see below)
+            40: np.inf,  # Cropland (except flat - see below)
+            60: np.inf,  # Bare
+            90: np.inf,  # Wetland
+            95: np.inf,  # Mangrove
+            80: np.inf,  # Water
+        },
+        wilderness_impassable=True,
+        off_trail_flat_threshold_deg=5.0,  # Can drive on flat fields
+        off_trail_flat_friction=5.0,       # But very slow
+    ),
+}
+
+
+# Pragmatic mode friction multiplier for private land
+PRAGMATIC_BARRIER_MULTIPLIER = 5.0
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# COST GRID COMPUTATION
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def compute_cost_grid(
+    elevation: np.ndarray,
+    cell_size_m: float,
+    cell_size_lat_m: float = None,
+    cell_size_lon_m: float = None,
+    friction: Optional[np.ndarray] = None,
+    friction_raw: Optional[np.ndarray] = None,
+    trails: Optional[np.ndarray] = None,
+    barriers: Optional[np.ndarray] = None,
+    wilderness: Optional[np.ndarray] = None,
+    mvum: Optional[np.ndarray] = None,
+    boundary_mode: Literal["strict", "pragmatic", "emergency"] = "pragmatic",
+    mode: Literal["foot", "mtb", "atv", "vehicle"] = "foot"
+) -> np.ndarray:
+    """
+    Compute isotropic travel cost grid from elevation data.
+
+    Args:
+        elevation: 2D array of elevation values in meters
+        cell_size_m: Average cell size in meters
+        cell_size_lat_m: Cell size in latitude direction (optional)
+        cell_size_lon_m: Cell size in longitude direction (optional)
+        friction: Optional 2D array of friction multipliers (WorldCover).
+                  Values should be float (1.0 = baseline, 2.0 = 2x slower).
+                  np.inf marks impassable cells.
+        friction_raw: Optional 2D array of raw WorldCover class values (uint8).
+                  Used for mode-specific terrain overrides.
+                  Values: 10=tree, 20=shrub, 30=grass, etc.
+        trails: Optional 2D array of trail values (uint8).
+                  0 = no trail, 5 = road, 15 = track, 25 = foot trail
+        barriers: Optional 2D array of barrier values (uint8).
+                  255 = closed/restricted area (PAD-US Pub_Access = XA).
+        wilderness: Optional[np.ndarray] of wilderness values (uint8).
+                  255 = designated wilderness area.
+        mvum: Optional[np.ndarray] of MVUM access values (uint8).
+                  0 = no MVUM data, 1 = open, 255 = closed to this mode.
+                  MVUM closures respond to boundary_mode (strict/pragmatic/emergency).
+                  Foot mode should pass None (MVUM is motor-vehicle specific).
+        boundary_mode: How to handle barriers ("strict", "pragmatic", "emergency")
+        mode: Travel mode ("foot", "mtb", "atv", "vehicle")
+
+    Returns:
+        2D array of travel cost in seconds per cell.
+        np.inf for impassable cells.
+    """
+    if boundary_mode not in ("strict", "pragmatic", "emergency"):
+        raise ValueError(f"boundary_mode must be 'strict', 'pragmatic', or 'emergency'")
+
+    if mode not in MODE_PROFILES:
+        raise ValueError(f"mode must be one of {list(MODE_PROFILES.keys())}")
+
+    profile = MODE_PROFILES[mode]
+
+    if cell_size_lat_m is None:
+        cell_size_lat_m = cell_size_m
+    if cell_size_lon_m is None:
+        cell_size_lon_m = cell_size_m
+
+    rows, cols = elevation.shape
+
+    # ─── Compute gradients (in-place where possible) ─────────────────────────
+    # Use float32 to reduce memory footprint
+    grade = np.zeros(elevation.shape, dtype=np.float32)
+
+    # Compute dy contribution to grade squared
+    dy_contrib = np.zeros(elevation.shape, dtype=np.float32)
+    dy_contrib[1:-1, :] = ((elevation[:-2, :] - elevation[2:, :]) / (2 * cell_size_lat_m)) ** 2
+    dy_contrib[0, :] = ((elevation[0, :] - elevation[1, :]) / cell_size_lat_m) ** 2
+    dy_contrib[-1, :] = ((elevation[-2, :] - elevation[-1, :]) / cell_size_lat_m) ** 2
+
+    # Compute dx contribution and add to dy_contrib in-place
+    dy_contrib[:, 1:-1] += ((elevation[:, 2:] - elevation[:, :-2]) / (2 * cell_size_lon_m)) ** 2
+    dy_contrib[:, 0] += ((elevation[:, 1] - elevation[:, 0]) / cell_size_lon_m) ** 2
+    dy_contrib[:, -1] += ((elevation[:, -1] - elevation[:, -2]) / cell_size_lon_m) ** 2
+
+    # grade = sqrt(dx^2 + dy^2)
+    np.sqrt(dy_contrib, out=grade)
+    del dy_contrib  # Free memory immediately
+
+    # ─── Compute speed based on mode ─────────────────────────────────────────
+    max_grade_val = np.tan(np.radians(profile.max_slope_deg))
+
+    if profile.speed_function == "tobler":
+        speed_kmh = tobler_off_path_speed(grade, profile.base_speed_kmh)
+    elif profile.speed_function == "herzog":
+        speed_kmh = herzog_wheeled_speed(grade, profile.base_speed_kmh)
+    elif profile.speed_function == "linear":
+        speed_kmh = linear_degrade_speed(grade, profile.base_speed_kmh, max_grade_val)
+    else:
+        raise ValueError(f"Unknown speed function: {profile.speed_function}")
+
+    # ─── Base cost (seconds per cell) ─────────────────────────────────────────
+    avg_cell_size = (cell_size_lat_m + cell_size_lon_m) / 2
+    cost = (avg_cell_size * 3.6) / speed_kmh
+    del speed_kmh
+
+    # ─── Max slope limit ──────────────────────────────────────────────────────
+    cost[grade > max_grade_val] = np.inf
+
+    # ─── NaN elevations ──────────────────────────────────────────────────────
+    cost[np.isnan(elevation)] = np.inf
+
+    # ─── Apply friction in-place ─────────────────────────────────────────────
+    # Instead of creating effective_friction copy, apply directly to cost
+
+    # Start with base friction
+    if friction is not None:
+        if friction.shape != elevation.shape:
+            raise ValueError(f"Friction shape mismatch")
+        np.multiply(cost, friction, out=cost)
+
+    # ─── Mode-specific terrain friction overrides (memory-efficient) ─────────
+    if friction_raw is not None and profile.terrain_friction_override:
+        if friction_raw.shape != elevation.shape:
+            raise ValueError(f"Friction_raw shape mismatch")
+
+        # Process all overrides without creating large intermediate masks
+        for wc_class, override in profile.terrain_friction_override.items():
+            if override is not None:
+                if override == np.inf:
+                    # Use np.where for in-place-like behavior
+                    np.putmask(cost, friction_raw == wc_class, np.inf)
+                else:
+                    # Multiply cost where friction_raw matches
+                    # Using a loop with putmask is more memory efficient
+                    mask = friction_raw == wc_class
+                    cost[mask] *= override
+                    del mask
+
+    # ─── Vehicle mode: allow flat grassland/cropland ─────────────────────────
+    if mode == "vehicle" and profile.off_trail_flat_threshold_deg > 0:
+        if friction_raw is not None:
+            # Compute slope in degrees for flat terrain check
+            slope_deg = np.degrees(np.arctan(grade))
+            # Flat grassland or cropland - recompute cost for these cells
+            flat_field_mask = (
+                (slope_deg <= profile.off_trail_flat_threshold_deg) &
+                ((friction_raw == 30) | (friction_raw == 40))
+            )
+            del slope_deg
+            # Recalculate cost for these cells with flat field friction
+            if np.any(flat_field_mask):
+                base_time = avg_cell_size * 3.6 / linear_degrade_speed(
+                    grade[flat_field_mask], profile.base_speed_kmh, max_grade_val
+                )
+                cost[flat_field_mask] = base_time * profile.off_trail_flat_friction
+                del base_time
+            del flat_field_mask
+
+    # ─── Trail friction (mode-specific) ──────────────────────────────────────
+    if trails is not None:
+        if trails.shape != elevation.shape:
+            raise ValueError(f"Trails shape mismatch")
+
+        for trail_value, trail_friction in profile.trail_friction.items():
+            if trail_friction is None:
+                # Impassable for this mode
+                np.putmask(cost, trails == trail_value, np.inf)
+            else:
+                # Trail friction REPLACES terrain friction
+                # Recalculate cost = base_time * trail_friction
+                trail_mask = trails == trail_value
+                if np.any(trail_mask):
+                    # Get base travel time (without friction)
+                    if profile.speed_function == "tobler":
+                        trail_speed = tobler_off_path_speed(grade[trail_mask], profile.base_speed_kmh)
+                    elif profile.speed_function == "herzog":
+                        trail_speed = herzog_wheeled_speed(grade[trail_mask], profile.base_speed_kmh)
+                    else:
+                        trail_speed = linear_degrade_speed(
+                            grade[trail_mask], profile.base_speed_kmh, max_grade_val
+                        )
+                    cost[trail_mask] = (avg_cell_size * 3.6 / trail_speed) * trail_friction
+                    del trail_speed
+                del trail_mask
+
+    # ─── Wilderness areas (mode-specific) ────────────────────────────────────
+    if wilderness is not None and profile.wilderness_impassable:
+        if wilderness.shape != elevation.shape:
+            raise ValueError(f"Wilderness shape mismatch")
+        np.putmask(cost, wilderness == 255, np.inf)
+
+    # ─── Barriers (private land) ─────────────────────────────────────────────
+    if barriers is not None and boundary_mode != "emergency":
+        if barriers.shape != elevation.shape:
+            raise ValueError(f"Barriers shape mismatch")
+
+        if boundary_mode == "strict":
+            np.putmask(cost, barriers == 255, np.inf)
+        elif boundary_mode == "pragmatic":
+            barrier_mask = barriers == 255
+            cost[barrier_mask] *= PRAGMATIC_BARRIER_MULTIPLIER
+            del barrier_mask
+
+    # ─── MVUM closures (motor vehicle restrictions) ──────────────────────────
+    # MVUM only applies to motorized modes, not foot. Foot mode should pass mvum=None.
+    # MVUM closures respond to the same boundary_mode as PAD-US barriers:
+    #   "strict"    = MVUM-closed road/trail is impassable
+    #   "pragmatic" = MVUM-closed road/trail gets 5× friction penalty
+    #   "emergency" = MVUM closures ignored entirely
+    if mvum is not None and mode != "foot" and boundary_mode != "emergency":
+        if mvum.shape != elevation.shape:
+            raise ValueError(f"MVUM shape mismatch")
+
+        # Value 255 = road/trail exists but is closed to this mode
+        mvum_closed_mask = mvum == 255
+
+        if boundary_mode == "strict":
+            np.putmask(cost, mvum_closed_mask, np.inf)
+        elif boundary_mode == "pragmatic":
+            cost[mvum_closed_mask] *= PRAGMATIC_BARRIER_MULTIPLIER
+
+        del mvum_closed_mask
+
+    return cost
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# LEGACY API (backward compatibility)
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def tobler_speed(grade: float) -> float:
+    """Legacy single-value Tobler speed function."""
+    return 0.6 * 6.0 * math.exp(-3.5 * abs(grade + 0.05))
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# TESTING
+# ═══════════════════════════════════════════════════════════════════════════════
+
+if __name__ == "__main__":
+    print("=" * 70)
+    print("OFFROUTE Multi-Mode Cost Function Tests")
+    print("=" * 70)
+
+    print("\n[1] Speed functions at various grades:")
+    print(f"{'Grade':<10} {'Foot':<12} {'MTB':<12} {'ATV':<12} {'Vehicle':<12}")
+    print("-" * 60)
+
+    for grade_val in [-0.3, -0.1, 0.0, 0.1, 0.2, 0.3]:
+        grade_arr = np.array([grade_val])
+        foot = tobler_off_path_speed(grade_arr, 6.0)[0]
+        mtb = herzog_wheeled_speed(grade_arr, 12.0)[0]
+        atv = herzog_wheeled_speed(grade_arr, 25.0)[0]
+        veh = linear_degrade_speed(grade_arr, 40.0, np.tan(np.radians(20)))[0]
+        print(f"{grade_val:+.2f}      {foot:>6.2f} km/h   {mtb:>6.2f} km/h   {atv:>6.2f} km/h   {veh:>6.2f} km/h")
+
+    print("\n[2] Mode profiles:")
+    for name, profile in MODE_PROFILES.items():
+        print(f"\n  {name.upper()}: {profile.description}")
+        print(f"    Max slope: {profile.max_slope_deg}°")
+        print(f"    Trail access: {profile.trail_friction}")
+        print(f"    Wilderness blocked: {profile.wilderness_impassable}")
+
+    print("\n[3] Cost grid test (flat terrain, forest):")
+    elev = np.ones((10, 10), dtype=np.float32) * 1000
+    friction = np.ones((10, 10), dtype=np.float32) * 2.0  # Forest friction
+    friction_raw = np.ones((10, 10), dtype=np.uint8) * 10  # Tree cover class
+
+    trails = np.zeros((10, 10), dtype=np.uint8)
+    trails[5, :] = 5  # Road across middle
+
+    for mode_name in ["foot", "mtb", "atv", "vehicle"]:
+        cost = compute_cost_grid(
+            elev, cell_size_m=30.0,
+            friction=friction,
+            friction_raw=friction_raw,
+            trails=trails,
+            mode=mode_name
+        )
+        off_trail_cost = cost[0, 0]
+        road_cost = cost[5, 0]
+        impassable = np.sum(np.isinf(cost))
+        print(f"  {mode_name:8s}: off-trail={off_trail_cost:>8.1f}s, road={road_cost:>6.1f}s, impassable={impassable}")
+
+    print("\n[4] Wilderness blocking test:")
+    wilderness = np.zeros((10, 10), dtype=np.uint8)
+    wilderness[3:7, 3:7] = 255
+
+    for mode_name in ["foot", "mtb", "atv", "vehicle"]:
+        cost = compute_cost_grid(
+            elev, cell_size_m=30.0,
+            wilderness=wilderness,
+            mode=mode_name
+        )
+        wilderness_impassable = np.sum(np.isinf(cost[3:7, 3:7]))
+        print(f"  {mode_name:8s}: wilderness cells impassable = {wilderness_impassable}/16")
+
+    print("\nDone.")

lib/offroute/dem.py

@@ -0,0 +1,190 @@
+"""
+DEM tile reader for OFFROUTE.
+
+Reads elevation tiles from planet-dem.pmtiles (Terrarium-encoded WebP),
+decodes them into numpy arrays, and provides a stitched elevation grid
+for a given bounding box.
+"""
+import math
+from functools import lru_cache
+from io import BytesIO
+from pathlib import Path
+from typing import Tuple, Optional
+
+import numpy as np
+from PIL import Image
+from pmtiles.reader import MmapSource, Reader as PMTilesReader
+
+# Default path to the planet DEM PMTiles file
+DEFAULT_DEM_PATH = Path("/mnt/nas/nav/planet-dem.pmtiles")
+
+# Tile size in pixels (z12 tiles are 512x512 in this tileset)
+TILE_SIZE = 512
+
+# Zoom level to use for elevation data
+ZOOM_LEVEL = 12
+
+
+def terrarium_decode(rgb_array: np.ndarray) -> np.ndarray:
+    """
+    Decode Terrarium-encoded RGB values to elevation in meters.
+    
+    Formula: elevation = (R * 256 + G + B/256) - 32768
+    """
+    r = rgb_array[:, :, 0].astype(np.float32)
+    g = rgb_array[:, :, 1].astype(np.float32)
+    b = rgb_array[:, :, 2].astype(np.float32)
+    
+    elevation = (r * 256.0 + g + b / 256.0) - 32768.0
+    return elevation
+
+
+def lat_lon_to_tile(lat: float, lon: float, zoom: int) -> Tuple[int, int]:
+    """Convert lat/lon to tile coordinates at given zoom level."""
+    n = 2 ** zoom
+    x = int((lon + 180.0) / 360.0 * n)
+    lat_rad = math.radians(lat)
+    y = int((1.0 - math.asinh(math.tan(lat_rad)) / math.pi) / 2.0 * n)
+    return x, y
+
+
+def tile_to_lat_lon(x: int, y: int, zoom: int) -> Tuple[float, float, float, float]:
+    """Convert tile coordinates to bounding box (north, south, west, east)."""
+    n = 2 ** zoom
+    lon_west = x / n * 360.0 - 180.0
+    lon_east = (x + 1) / n * 360.0 - 180.0
+    lat_north = math.degrees(math.atan(math.sinh(math.pi * (1 - 2 * y / n))))
+    lat_south = math.degrees(math.atan(math.sinh(math.pi * (1 - 2 * (y + 1) / n))))
+    return lat_north, lat_south, lon_west, lon_east
+
+
+class DEMReader:
+    """Reader for Terrarium-encoded DEM tiles from PMTiles."""
+    
+    def __init__(self, pmtiles_path: Path = DEFAULT_DEM_PATH, tile_cache_size: int = 128):
+        self.pmtiles_path = pmtiles_path
+        self._source = MmapSource(open(pmtiles_path, "rb"))
+        self._reader = PMTilesReader(self._source)
+        self._header = self._reader.header()
+        self._decode_tile = lru_cache(maxsize=tile_cache_size)(self._decode_tile_impl)
+    
+    def _decode_tile_impl(self, z: int, x: int, y: int) -> Optional[np.ndarray]:
+        """Fetch and decode a single tile."""
+        tile_data = self._reader.get(z, x, y)
+        if tile_data is None:
+            return None
+        
+        img = Image.open(BytesIO(tile_data))
+        rgb_array = np.array(img)
+        
+        if rgb_array.shape[2] == 4:
+            rgb_array = rgb_array[:, :, :3]
+        
+        elevation = terrarium_decode(rgb_array)
+        return elevation
+    
+    def get_elevation_grid(
+        self,
+        south: float,
+        north: float,
+        west: float,
+        east: float,
+        zoom: int = ZOOM_LEVEL
+    ) -> Tuple[np.ndarray, dict]:
+        """Get a stitched elevation grid for the given bounding box."""
+        x_min, y_max = lat_lon_to_tile(south, west, zoom)
+        x_max, y_min = lat_lon_to_tile(north, east, zoom)
+        
+        n = 2 ** zoom
+        x_min = max(0, x_min)
+        x_max = min(n - 1, x_max)
+        y_min = max(0, y_min)
+        y_max = min(n - 1, y_max)
+        
+        n_tiles_x = x_max - x_min + 1
+        n_tiles_y = y_max - y_min + 1
+        out_height = n_tiles_y * TILE_SIZE
+        out_width = n_tiles_x * TILE_SIZE
+        
+        elevation = np.full((out_height, out_width), np.nan, dtype=np.float32)
+        
+        for ty in range(y_min, y_max + 1):
+            for tx in range(x_min, x_max + 1):
+                tile_elev = self._decode_tile(zoom, tx, ty)
+                if tile_elev is not None:
+                    out_y = (ty - y_min) * TILE_SIZE
+                    out_x = (tx - x_min) * TILE_SIZE
+                    elevation[out_y:out_y + TILE_SIZE, out_x:out_x + TILE_SIZE] = tile_elev
+        
+        grid_north, _, grid_west, _ = tile_to_lat_lon(x_min, y_min, zoom)
+        _, grid_south, _, grid_east = tile_to_lat_lon(x_max, y_max, zoom)
+        
+        pixel_size_lat = (grid_north - grid_south) / out_height
+        pixel_size_lon = (grid_east - grid_west) / out_width
+        
+        origin_lat = grid_north - pixel_size_lat / 2
+        origin_lon = grid_west + pixel_size_lon / 2
+        
+        center_lat = (south + north) / 2
+        lat_m = 111320.0
+        lon_m = 111320.0 * math.cos(math.radians(center_lat))
+        cell_size_lat_m = abs(pixel_size_lat) * lat_m
+        cell_size_lon_m = abs(pixel_size_lon) * lon_m
+        cell_size_m = (cell_size_lat_m + cell_size_lon_m) / 2
+        
+        row_start = int((grid_north - north) / abs(pixel_size_lat))
+        row_end = int((grid_north - south) / abs(pixel_size_lat))
+        col_start = int((west - grid_west) / pixel_size_lon)
+        col_end = int((east - grid_west) / pixel_size_lon)
+        
+        row_start = max(0, row_start)
+        row_end = min(out_height, row_end)
+        col_start = max(0, col_start)
+        col_end = min(out_width, col_end)
+        
+        elevation = elevation[row_start:row_end, col_start:col_end]
+        
+        origin_lat = grid_north - (row_start + 0.5) * abs(pixel_size_lat)
+        origin_lon = grid_west + (col_start + 0.5) * pixel_size_lon
+        
+        metadata = {
+            "bounds": (south, north, west, east),
+            "pixel_size_lat": -abs(pixel_size_lat),
+            "pixel_size_lon": pixel_size_lon,
+            "origin_lat": origin_lat,
+            "origin_lon": origin_lon,
+            "cell_size_m": cell_size_m,
+            "shape": elevation.shape,
+        }
+        
+        return elevation, metadata
+    
+    def pixel_to_latlon(self, row: int, col: int, metadata: dict) -> Tuple[float, float]:
+        """Convert pixel coordinates to lat/lon."""
+        lat = metadata["origin_lat"] + row * metadata["pixel_size_lat"]
+        lon = metadata["origin_lon"] + col * metadata["pixel_size_lon"]
+        return lat, lon
+    
+    def latlon_to_pixel(self, lat: float, lon: float, metadata: dict) -> Tuple[int, int]:
+        """Convert lat/lon to pixel coordinates."""
+        row = int((metadata["origin_lat"] - lat) / abs(metadata["pixel_size_lat"]))
+        col = int((lon - metadata["origin_lon"]) / metadata["pixel_size_lon"])
+        return row, col
+    
+    def close(self):
+        """Close the PMTiles file."""
+        pass  # MmapSource handles cleanup
+
+
+if __name__ == "__main__":
+    reader = DEMReader()
+    elevation, meta = reader.get_elevation_grid(
+        south=42.4, north=42.6, west=-114.5, east=-114.3
+    )
+    print(f"Elevation grid shape: {elevation.shape}")
+    print(f"Cell size: {meta['cell_size_m']:.1f} m")
+    print(f"Elevation range: {np.nanmin(elevation):.1f} - {np.nanmax(elevation):.1f} m")
+    center_row, center_col = elevation.shape[0] // 2, elevation.shape[1] // 2
+    lat, lon = reader.pixel_to_latlon(center_row, center_col, meta)
+    print(f"Center pixel lat/lon: {lat:.4f}, {lon:.4f}")
+    reader.close()

lib/offroute/friction.py

@@ -0,0 +1,137 @@
+"""
+Friction layer reader for OFFROUTE.
+
+Reads friction values from the WorldCover friction VRT and resamples
+to match the elevation grid dimensions.
+"""
+import numpy as np
+from pathlib import Path
+from typing import Tuple, Optional
+
+try:
+    import rasterio
+    from rasterio.windows import from_bounds
+    from rasterio.enums import Resampling
+except ImportError:
+    raise ImportError("rasterio is required for friction layer support")
+
+# Default path to the friction VRT
+DEFAULT_FRICTION_PATH = Path("/mnt/nav/worldcover/friction/friction_conus.vrt")
+
+
+class FrictionReader:
+    """Reader for WorldCover friction raster."""
+    
+    def __init__(self, friction_path: Path = DEFAULT_FRICTION_PATH):
+        self.friction_path = friction_path
+        self._dataset = None
+    
+    def _open(self):
+        """Lazy open the dataset."""
+        if self._dataset is None:
+            self._dataset = rasterio.open(self.friction_path)
+        return self._dataset
+    
+    def get_friction_grid(
+        self,
+        south: float,
+        north: float,
+        west: float,
+        east: float,
+        target_shape: Tuple[int, int]
+    ) -> np.ndarray:
+        """
+        Get friction values for a bounding box, resampled to target shape.
+        
+        Args:
+            south, north, west, east: Bounding box coordinates
+            target_shape: (rows, cols) to resample to (matches elevation grid)
+        
+        Returns:
+            np.ndarray of uint8 friction values, same shape as target_shape.
+            Values: 10-40 = friction multiplier (divide by 10)
+                    255 = impassable
+                    0 = nodata (treat as impassable)
+        """
+        ds = self._open()
+        
+        # Create a window from the bounding box
+        window = from_bounds(west, south, east, north, ds.transform)
+        
+        # Read with resampling to target shape
+        # Use nearest neighbor for categorical data
+        friction = ds.read(
+            1,
+            window=window,
+            out_shape=target_shape,
+            resampling=Resampling.nearest
+        )
+        
+        return friction
+    
+    def sample_point(self, lat: float, lon: float) -> int:
+        """Sample friction value at a single point."""
+        ds = self._open()
+        
+        # Get pixel coordinates
+        row, col = ds.index(lon, lat)
+        
+        # Check bounds
+        if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
+            return 0  # Out of bounds = nodata
+        
+        # Read single pixel
+        window = rasterio.windows.Window(col, row, 1, 1)
+        value = ds.read(1, window=window)
+        return int(value[0, 0])
+    
+    def close(self):
+        """Close the dataset."""
+        if self._dataset is not None:
+            self._dataset.close()
+            self._dataset = None
+
+
+def friction_to_multiplier(friction: np.ndarray) -> np.ndarray:
+    """
+    Convert friction values to cost multipliers.
+    
+    Args:
+        friction: uint8 array of friction values
+        
+    Returns:
+        float32 array of multipliers.
+        Values 10-40 become 1.0-4.0 (divide by 10).
+        Values 0 or 255 become np.inf (impassable).
+    """
+    multiplier = friction.astype(np.float32) / 10.0
+    
+    # Mark impassable cells
+    multiplier[friction == 0] = np.inf    # nodata
+    multiplier[friction == 255] = np.inf  # water/impassable
+    
+    return multiplier
+
+
+if __name__ == "__main__":
+    print("Testing FrictionReader...")
+    
+    reader = FrictionReader()
+    
+    # Test point sampling - Murtaugh Lake (should be water = 255)
+    lake_lat, lake_lon = 42.47, -114.15
+    lake_friction = reader.sample_point(lake_lat, lake_lon)
+    print(f"Murtaugh Lake ({lake_lat}, {lake_lon}): friction = {lake_friction}")
+    print(f"  Expected: 255 (water/impassable)")
+    
+    # Test grid read for small bbox
+    friction = reader.get_friction_grid(
+        south=42.4, north=42.5, west=-114.2, east=-114.1,
+        target_shape=(100, 100)
+    )
+    print(f"\nGrid test shape: {friction.shape}")
+    print(f"Unique values: {np.unique(friction)}")
+    print(f"Water cells (255): {np.sum(friction == 255)}")
+    
+    reader.close()
+    print("\nFrictionReader test complete.")

lib/offroute/mvum.py

@@ -0,0 +1,623 @@
+"""
+MVUM (Motor Vehicle Use Map) legal access layer for OFFROUTE.
+
+Queries USFS MVUM data from navi.db and provides rasterized access grids
+indicating which roads/trails are open or closed to specific vehicle modes.
+
+MVUM is motor-vehicle specific — foot mode should skip this layer entirely.
+"""
+import re
+import sqlite3
+import warnings
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Literal
+
+import numpy as np
+
+# Path to navi.db
+NAVI_DB_PATH = Path("/mnt/nav/navi.db")
+
+
+def parse_date_range(date_str: str) -> List[Tuple[int, int, int, int]]:
+    """
+    Parse MVUM date range strings like "05/01-11/30" or "06/15-10/15,12/01-03/31".
+
+    Returns list of (start_month, start_day, end_month, end_day) tuples.
+    Returns empty list if unparseable.
+    """
+    if not date_str or date_str.strip() == "":
+        return []
+
+    ranges = []
+    # Split by comma for multi-period strings
+    for part in date_str.split(","):
+        part = part.strip()
+        # Match MM/DD-MM/DD pattern
+        match = re.match(r"(\d{1,2})/(\d{1,2})-(\d{1,2})/(\d{1,2})", part)
+        if match:
+            try:
+                sm, sd, em, ed = int(match.group(1)), int(match.group(2)), int(match.group(3)), int(match.group(4))
+                if 1 <= sm <= 12 and 1 <= sd <= 31 and 1 <= em <= 12 and 1 <= ed <= 31:
+                    ranges.append((sm, sd, em, ed))
+            except ValueError:
+                pass
+
+    return ranges
+
+
+def is_date_in_range(month: int, day: int, ranges: List[Tuple[int, int, int, int]]) -> bool:
+    """
+    Check if a given month/day falls within any of the date ranges.
+    Handles ranges that wrap around year end (e.g., 12/01-03/31).
+    """
+    if not ranges:
+        return True  # No ranges = assume open
+
+    date_num = month * 100 + day  # Simple numeric comparison
+
+    for sm, sd, em, ed in ranges:
+        start_num = sm * 100 + sd
+        end_num = em * 100 + ed
+
+        if start_num <= end_num:
+            # Normal range (e.g., 05/01-11/30)
+            if start_num <= date_num <= end_num:
+                return True
+        else:
+            # Wrapping range (e.g., 12/01-03/31)
+            if date_num >= start_num or date_num <= end_num:
+                return True
+
+    return False
+
+
+def check_access(
+    status_field: Optional[str],
+    dates_field: Optional[str],
+    seasonal: Optional[str],
+    check_date: Optional[Tuple[int, int]] = None
+) -> Optional[bool]:
+    """
+    Determine if a road/trail is open to a vehicle type.
+
+    Args:
+        status_field: Value of vehicle-class field (e.g., "open", null)
+        dates_field: Value of *_DATESOPEN field (e.g., "05/01-11/30")
+        seasonal: Value of SEASONAL field ("yearlong", "seasonal")
+        check_date: Optional (month, day) tuple to check against date ranges
+
+    Returns:
+        True = open
+        False = closed
+        None = no data (field not populated, defer to SYMBOL)
+    """
+    if status_field is None or status_field.strip() == "":
+        return None  # No data
+
+    status = status_field.strip().lower()
+
+    if status != "open":
+        return False  # Explicitly closed or restricted
+
+    # Status is "open" - check seasonal restrictions
+    if check_date is not None:
+        month, day = check_date
+
+        # Parse date ranges
+        if dates_field:
+            ranges = parse_date_range(dates_field)
+            if ranges:
+                return is_date_in_range(month, day, ranges)
+
+        # No date field but seasonal = "yearlong" means always open
+        if seasonal and seasonal.strip().lower() == "yearlong":
+            return True
+
+        # Seasonal with no dates - assume open (data quality issue)
+        if seasonal and seasonal.strip().lower() == "seasonal":
+            warnings.warn(f"Seasonal road/trail with no DATESOPEN, assuming open")
+            return True
+
+    return True  # Open with no date check
+
+
+def get_mode_field(mode: str) -> Tuple[str, str]:
+    """
+    Get the MVUM field names for a given travel mode.
+
+    Returns (status_field, dates_field) tuple.
+    """
+    mode_mapping = {
+        "atv": ("atv", "atv_datesopen"),
+        "motorcycle": ("motorcycle", "motorcycle_datesopen"),
+        "mtb": ("e_bike_class1", "e_bike_class1_dur"),  # Closest analog for e-bikes
+        "vehicle": ("highclearancevehicle", "highclearancevehicle_datesopen"),
+        "passenger": ("passengervehicle", "passengervehicle_datesopen"),
+    }
+
+    return mode_mapping.get(mode, ("highclearancevehicle", "highclearancevehicle_datesopen"))
+
+
+def symbol_to_access(symbol: str, mode: str, maint_level: Optional[str] = None) -> Optional[bool]:
+    """
+    Fallback: interpret SYMBOL field when per-vehicle-class fields are null.
+
+    MVUM SYMBOL meanings (roads):
+        1 = Open to all vehicles
+        2 = Open to highway legal vehicles only
+        3 = Road closed to motorized
+        4 = Road open seasonally
+        11 = Administrative use only
+        12 = Decommissioned
+
+    For trails, similar logic applies based on TRAILCLASS.
+    """
+    if symbol is None:
+        return None
+
+    sym = str(symbol).strip()
+
+    # Symbol 1: Open to all
+    if sym == "1":
+        return True
+
+    # Symbol 2: Highway legal only
+    if sym == "2":
+        # ATVs/motorcycles typically not highway legal
+        if mode in ("atv", "motorcycle"):
+            return False
+        return True
+
+    # Symbol 3: Closed to motorized
+    if sym == "3":
+        return False
+
+    # Symbol 4: Seasonally open (assume open if no date check)
+    if sym == "4":
+        return True
+
+    # Symbol 11/12: Administrative/decommissioned = closed
+    if sym in ("11", "12"):
+        return False
+
+    # Unknown symbol - defer
+    return None
+
+
+class MVUMReader:
+    """
+    Reader for MVUM data from navi.db.
+
+    Queries roads and trails by bounding box and returns access grids.
+    """
+
+    def __init__(self, db_path: Path = NAVI_DB_PATH):
+        self.db_path = db_path
+        self._conn = None
+
+    def _get_conn(self) -> sqlite3.Connection:
+        if self._conn is None:
+            if not self.db_path.exists():
+                raise FileNotFoundError(f"navi.db not found at {self.db_path}")
+            self._conn = sqlite3.connect(str(self.db_path))
+            self._conn.row_factory = sqlite3.Row
+            # Load Spatialite extension if available
+            try:
+                self._conn.enable_load_extension(True)
+                self._conn.load_extension("mod_spatialite")
+            except Exception:
+                pass  # Spatialite not available, will use manual bbox queries
+        return self._conn
+
+    def table_exists(self, table_name: str) -> bool:
+        """Check if an MVUM table exists."""
+        conn = self._get_conn()
+        cur = conn.execute(
+            "SELECT name FROM sqlite_master WHERE type='table' AND name=?",
+            (table_name,)
+        )
+        return cur.fetchone() is not None
+
+    def query_roads_bbox(
+        self,
+        south: float, north: float, west: float, east: float,
+        mode: str = "atv",
+        check_date: Optional[Tuple[int, int]] = None
+    ) -> List[Dict]:
+        """
+        Query MVUM roads within a bounding box.
+
+        Returns list of dicts with access info for the given mode.
+        """
+        if not self.table_exists("mvum_roads"):
+            return []
+
+        conn = self._get_conn()
+
+        # Query using bbox on geometry
+        # Since we don't have spatialite, we'll query all and filter in Python
+        # For production, consider pre-computing bbox columns
+        cur = conn.execute("""
+            SELECT ogc_fid, id, name, symbol, operationalmaintlevel, seasonal,
+                   atv, atv_datesopen, motorcycle, motorcycle_datesopen,
+                   highclearancevehicle, highclearancevehicle_datesopen,
+                   passengervehicle, passengervehicle_datesopen,
+                   e_bike_class1, e_bike_class1_dur,
+                   shape
+            FROM mvum_roads
+        """)
+
+        status_field, dates_field = get_mode_field(mode)
+        results = []
+
+        for row in cur:
+            # Parse geometry to check bbox intersection
+            # The shape is stored as WKB blob
+            shape = row["shape"]
+            if shape is None:
+                continue
+
+            # Quick bbox check using geometry extent
+            # Since we don't have Spatialite functions, we'll include all
+            # and let the rasterization handle it
+
+            access = check_access(
+                row[status_field] if status_field in row.keys() else None,
+                row[dates_field] if dates_field in row.keys() else None,
+                row["seasonal"],
+                check_date
+            )
+
+            # Fallback to SYMBOL if no per-vehicle data
+            if access is None:
+                access = symbol_to_access(row["symbol"], mode, row["operationalmaintlevel"])
+
+            if access is not None:
+                results.append({
+                    "id": row["id"],
+                    "name": row["name"],
+                    "access": access,
+                    "symbol": row["symbol"],
+                    "maint_level": row["operationalmaintlevel"],
+                    "shape": shape,
+                })
+
+        return results
+
+    def query_trails_bbox(
+        self,
+        south: float, north: float, west: float, east: float,
+        mode: str = "atv",
+        check_date: Optional[Tuple[int, int]] = None
+    ) -> List[Dict]:
+        """
+        Query MVUM trails within a bounding box.
+        """
+        if not self.table_exists("mvum_trails"):
+            return []
+
+        conn = self._get_conn()
+
+        cur = conn.execute("""
+            SELECT ogc_fid, id, name, symbol, seasonal, trailclass,
+                   atv, atv_datesopen, motorcycle, motorcycle_datesopen,
+                   highclearancevehicle, highclearancevehicle_datesopen,
+                   passengervehicle, passengervehicle_datesopen,
+                   e_bike_class1, e_bike_class1_dur,
+                   shape
+            FROM mvum_trails
+        """)
+
+        status_field, dates_field = get_mode_field(mode)
+        results = []
+
+        for row in cur:
+            shape = row["shape"]
+            if shape is None:
+                continue
+
+            access = check_access(
+                row[status_field] if status_field in row.keys() else None,
+                row[dates_field] if dates_field in row.keys() else None,
+                row["seasonal"],
+                check_date
+            )
+
+            if access is None:
+                access = symbol_to_access(row["symbol"], mode)
+
+            if access is not None:
+                results.append({
+                    "id": row["id"],
+                    "name": row["name"],
+                    "access": access,
+                    "symbol": row["symbol"],
+                    "trail_class": row["trailclass"],
+                    "shape": shape,
+                })
+
+        return results
+
+    def query_nearest(
+        self,
+        lat: float, lon: float,
+        radius_m: float = 50,
+        table: str = "mvum_roads"
+    ) -> Optional[Dict]:
+        """
+        Query the nearest MVUM feature to a point.
+
+        Used for the places panel API.
+        """
+        if not self.table_exists(table):
+            return None
+
+        conn = self._get_conn()
+
+        # Convert radius to degrees (approximate)
+        radius_deg = radius_m / 111000
+
+        # Query features in bbox around point
+        if table == "mvum_roads":
+            cur = conn.execute("""
+                SELECT ogc_fid, id, name, forestname, districtname, symbol,
+                       operationalmaintlevel, surfacetype, seasonal, jurisdiction,
+                       passengervehicle, passengervehicle_datesopen,
+                       highclearancevehicle, highclearancevehicle_datesopen,
+                       atv, atv_datesopen, motorcycle, motorcycle_datesopen,
+                       fourwd_gt50inches, fourwd_gt50_datesopen,
+                       twowd_gt50inches, twowd_gt50_datesopen,
+                       e_bike_class1, e_bike_class1_dur,
+                       e_bike_class2, e_bike_class2_dur,
+                       e_bike_class3, e_bike_class3_dur,
+                       shape
+                FROM mvum_roads
+                LIMIT 1000
+            """)
+        else:
+            cur = conn.execute("""
+                SELECT ogc_fid, id, name, forestname, districtname, symbol,
+                       seasonal, jurisdiction, trailclass, trailsystem,
+                       passengervehicle, passengervehicle_datesopen,
+                       highclearancevehicle, highclearancevehicle_datesopen,
+                       atv, atv_datesopen, motorcycle, motorcycle_datesopen,
+                       fourwd_gt50inches, fourwd_gt50_datesopen,
+                       twowd_gt50inches, twowd_gt50_datesopen,
+                       e_bike_class1, e_bike_class1_dur,
+                       e_bike_class2, e_bike_class2_dur,
+                       e_bike_class3, e_bike_class3_dur,
+                       shape
+                FROM mvum_trails
+                LIMIT 1000
+            """)
+
+        # Find nearest feature
+        # This is a simplified approach - for production, use spatial index
+        try:
+            from shapely import wkb
+            from shapely.geometry import Point
+
+            query_point = Point(lon, lat)
+            nearest = None
+            min_dist = float('inf')
+
+            for row in cur:
+                try:
+                    geom = wkb.loads(row["shape"])
+                    dist = query_point.distance(geom)
+                    if dist < min_dist and dist < radius_deg:
+                        min_dist = dist
+                        nearest = dict(row)
+                        nearest["geometry"] = geom
+                except Exception:
+                    continue
+
+            if nearest:
+                # Convert geometry to GeoJSON
+                nearest["geojson"] = nearest["geometry"].__geo_interface__
+                del nearest["geometry"]
+                del nearest["shape"]
+                return nearest
+
+        except ImportError:
+            warnings.warn("shapely not available for nearest query")
+
+        return None
+
+    def close(self):
+        if self._conn:
+            self._conn.close()
+            self._conn = None
+
+
+def get_mvum_access_grid(
+    south: float, north: float, west: float, east: float,
+    target_shape: Tuple[int, int],
+    mode: Literal["foot", "mtb", "atv", "vehicle"] = "atv",
+    check_date: Optional[str] = None,
+    db_path: Path = NAVI_DB_PATH
+) -> np.ndarray:
+    """
+    Get MVUM access grid for pathfinding.
+
+    Args:
+        south, north, west, east: Bounding box (WGS84)
+        target_shape: (rows, cols) to match elevation grid
+        mode: Travel mode (foot skips MVUM entirely)
+        check_date: Optional "MM/DD" string for seasonal checking
+        db_path: Path to navi.db
+
+    Returns:
+        np.ndarray of uint8:
+            0   = no MVUM data (defer to existing trail/friction logic)
+            1   = road/trail is OPEN to this vehicle mode
+            255 = road/trail EXISTS but is CLOSED to this mode
+    """
+    # Foot mode bypasses MVUM entirely
+    if mode == "foot":
+        return np.zeros(target_shape, dtype=np.uint8)
+
+    # Parse check_date if provided
+    parsed_date = None
+    if check_date:
+        match = re.match(r"(\d{1,2})/(\d{1,2})", check_date)
+        if match:
+            parsed_date = (int(match.group(1)), int(match.group(2)))
+
+    # Initialize output grid
+    grid = np.zeros(target_shape, dtype=np.uint8)
+    rows, cols = target_shape
+
+    # Pixel size
+    pixel_lat = (north - south) / rows
+    pixel_lon = (east - west) / cols
+
+    reader = MVUMReader(db_path)
+
+    try:
+        # Query roads and trails
+        roads = reader.query_roads_bbox(south, north, west, east, mode, parsed_date)
+        trails = reader.query_trails_bbox(south, north, west, east, mode, parsed_date)
+
+        # Rasterize features
+        try:
+            from shapely import wkb
+
+            for features in [roads, trails]:
+                for feat in features:
+                    try:
+                        geom = wkb.loads(feat["shape"])
+
+                        # Get geometry bounds
+                        minx, miny, maxx, maxy = geom.bounds
+
+                        # Check if intersects our bbox
+                        if maxx < west or minx > east or maxy < south or miny > north:
+                            continue
+
+                        # Rasterize line
+                        value = 1 if feat["access"] else 255
+
+                        # Simple line rasterization
+                        if geom.geom_type in ("LineString", "MultiLineString"):
+                            if geom.geom_type == "MultiLineString":
+                                coords_list = [list(line.coords) for line in geom.geoms]
+                            else:
+                                coords_list = [list(geom.coords)]
+
+                            for coords in coords_list:
+                                for i in range(len(coords) - 1):
+                                    x1, y1 = coords[i]
+                                    x2, y2 = coords[i + 1]
+
+                                    # Convert to pixel coordinates
+                                    col1 = int((x1 - west) / pixel_lon)
+                                    row1 = int((north - y1) / pixel_lat)
+                                    col2 = int((x2 - west) / pixel_lon)
+                                    row2 = int((north - y2) / pixel_lat)
+
+                                    # Bresenham's line algorithm
+                                    _draw_line(grid, row1, col1, row2, col2, value)
+
+                    except Exception as e:
+                        continue
+
+        except ImportError:
+            warnings.warn("shapely not available, MVUM rasterization skipped")
+
+    finally:
+        reader.close()
+
+    return grid
+
+
+def _draw_line(grid: np.ndarray, r1: int, c1: int, r2: int, c2: int, value: int):
+    """Draw a line on the grid using Bresenham's algorithm."""
+    rows, cols = grid.shape
+
+    dr = abs(r2 - r1)
+    dc = abs(c2 - c1)
+    sr = 1 if r1 < r2 else -1
+    sc = 1 if c1 < c2 else -1
+    err = dr - dc
+
+    r, c = r1, c1
+
+    while True:
+        if 0 <= r < rows and 0 <= c < cols:
+            # Only overwrite if current value is 0 (no data) or we're marking closed
+            if grid[r, c] == 0 or value == 255:
+                grid[r, c] = value
+
+        if r == r2 and c == c2:
+            break
+
+        e2 = 2 * err
+        if e2 > -dc:
+            err -= dc
+            r += sr
+        if e2 < dr:
+            err += dr
+            c += sc
+
+
+if __name__ == "__main__":
+    import sys
+
+    print("=" * 60)
+    print("MVUM Reader Test")
+    print("=" * 60)
+
+    reader = MVUMReader()
+
+    if not reader.table_exists("mvum_roads"):
+        print("ERROR: mvum_roads table not found in navi.db")
+        sys.exit(1)
+
+    # Test bbox query (Sawtooth NF area)
+    print("\n[1] Testing bbox query (Sawtooth NF area)...")
+    roads = reader.query_roads_bbox(
+        south=43.5, north=44.0, west=-115.0, east=-114.0,
+        mode="atv"
+    )
+    print(f"  Found {len(roads)} roads")
+
+    open_count = sum(1 for r in roads if r["access"])
+    closed_count = sum(1 for r in roads if not r["access"])
+    print(f"  Open to ATV: {open_count}")
+    print(f"  Closed to ATV: {closed_count}")
+
+    # Test with seasonal date
+    print("\n[2] Testing with date check (July 15)...")
+    roads_summer = reader.query_roads_bbox(
+        south=43.5, north=44.0, west=-115.0, east=-114.0,
+        mode="atv",
+        check_date=(7, 15)
+    )
+    open_summer = sum(1 for r in roads_summer if r["access"])
+    print(f"  Open to ATV on 07/15: {open_summer}")
+
+    print("\n[3] Testing with date check (January 15)...")
+    roads_winter = reader.query_roads_bbox(
+        south=43.5, north=44.0, west=-115.0, east=-114.0,
+        mode="atv",
+        check_date=(1, 15)
+    )
+    open_winter = sum(1 for r in roads_winter if r["access"])
+    print(f"  Open to ATV on 01/15: {open_winter}")
+
+    # Test grid generation
+    print("\n[4] Testing grid generation...")
+    grid = get_mvum_access_grid(
+        south=43.5, north=44.0, west=-115.0, east=-114.0,
+        target_shape=(500, 1000),
+        mode="atv"
+    )
+    print(f"  Grid shape: {grid.shape}")
+    print(f"  No data (0): {np.sum(grid == 0)}")
+    print(f"  Open (1): {np.sum(grid == 1)}")
+    print(f"  Closed (255): {np.sum(grid == 255)}")
+
+    reader.close()
+    print("\nDone.")

lib/offroute/prototype.py

@@ -0,0 +1,414 @@
+#!/usr/bin/env python3
+"""
+OFFROUTE Phase O3a Prototype
+
+Validates trail burn-in integration with the MCP pathfinder.
+The path should actively seek out trails and roads when nearby.
+
+Compares paths with and without trail burn-in to show the benefit
+of trail-seeking behavior.
+"""
+import json
+import time
+import sys
+from pathlib import Path
+
+import numpy as np
+from skimage.graph import MCP_Geometric
+
+# Add parent to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+from lib.offroute.dem import DEMReader
+from lib.offroute.cost import compute_cost_grid
+from lib.offroute.friction import FrictionReader, friction_to_multiplier
+from lib.offroute.barriers import BarrierReader, DEFAULT_BARRIERS_PATH
+from lib.offroute.trails import TrailReader, DEFAULT_TRAILS_PATH
+
+# Test bounding box - Idaho area
+BBOX = {
+    "south": 42.21,
+    "north": 42.60,
+    "west": -114.76,
+    "east": -113.79,
+}
+
+# Start point: wilderness area away from roads
+START_LAT = 42.35
+START_LON = -114.60
+
+# End point: near Twin Falls (has roads/trails)
+END_LAT = 42.55
+END_LON = -114.20
+
+# Output files
+OUTPUT_PATH_WITH_TRAILS = Path("/opt/recon/data/offroute-test-trails.geojson")
+OUTPUT_PATH_NO_TRAILS = Path("/opt/recon/data/offroute-test-no-trails.geojson")
+
+# Memory limit in GB
+MEMORY_LIMIT_GB = 12
+
+
+def check_memory_usage():
+    """Check current memory usage and abort if over limit."""
+    try:
+        import psutil
+        process = psutil.Process()
+        mem_gb = process.memory_info().rss / (1024**3)
+        if mem_gb > MEMORY_LIMIT_GB:
+            print(f"ERROR: Memory usage {mem_gb:.1f}GB exceeds {MEMORY_LIMIT_GB}GB limit")
+            sys.exit(1)
+        return mem_gb
+    except ImportError:
+        return 0
+
+
+def run_pathfinder(
+    elevation: np.ndarray,
+    meta: dict,
+    friction_mult: np.ndarray,
+    trails: np.ndarray,
+    barriers: np.ndarray,
+    use_trails: bool,
+    start_row: int,
+    start_col: int,
+    end_row: int,
+    end_col: int,
+    dem_reader: DEMReader,
+) -> dict:
+    """Run the MCP pathfinder with given parameters."""
+    # Compute cost grid
+    cost = compute_cost_grid(
+        elevation,
+        cell_size_m=meta["cell_size_m"],
+        friction=friction_mult,
+        trails=trails if use_trails else None,
+        barriers=barriers,
+        boundary_mode="pragmatic",
+    )
+
+    # Run MCP
+    mcp = MCP_Geometric(cost, fully_connected=True)
+    cumulative_costs, traceback = mcp.find_costs([(start_row, start_col)])
+
+    end_cost = cumulative_costs[end_row, end_col]
+
+    if np.isinf(end_cost):
+        return {
+            "success": False,
+            "reason": "No path found (blocked by impassable terrain)",
+        }
+
+    # Traceback path
+    path_indices = mcp.traceback((end_row, end_col))
+
+    # Convert to coordinates and collect stats
+    coordinates = []
+    elevations = []
+    trail_values = []
+
+    for row, col in path_indices:
+        lat, lon = dem_reader.pixel_to_latlon(row, col, meta)
+        elev = elevation[row, col]
+        trail_val = trails[row, col] if trails is not None else 0
+        coordinates.append([lon, lat])
+        elevations.append(elev)
+        trail_values.append(trail_val)
+
+    # Compute distance
+    total_distance_m = 0
+    for i in range(1, len(coordinates)):
+        lon1, lat1 = coordinates[i-1]
+        lon2, lat2 = coordinates[i]
+        R = 6371000
+        dlat = np.radians(lat2 - lat1)
+        dlon = np.radians(lon2 - lon1)
+        a = np.sin(dlat/2)**2 + np.cos(np.radians(lat1)) * np.cos(np.radians(lat2)) * np.sin(dlon/2)**2
+        c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))
+        total_distance_m += R * c
+
+    # Elevation stats
+    elev_arr = np.array(elevations)
+    elev_diff = np.diff(elev_arr)
+    elev_gain = np.sum(elev_diff[elev_diff > 0])
+    elev_loss = np.sum(np.abs(elev_diff[elev_diff < 0]))
+
+    # Trail stats
+    trail_arr = np.array(trail_values)
+    road_cells = np.sum(trail_arr == 5)
+    track_cells = np.sum(trail_arr == 15)
+    trail_cells = np.sum(trail_arr == 25)
+    off_trail_cells = np.sum(trail_arr == 0)
+    on_trail_cells = road_cells + track_cells + trail_cells
+    total_cells = len(trail_arr)
+
+    return {
+        "success": True,
+        "coordinates": coordinates,
+        "total_time_seconds": float(end_cost),
+        "total_time_minutes": float(end_cost / 60),
+        "total_distance_m": float(total_distance_m),
+        "total_distance_km": float(total_distance_m / 1000),
+        "elevation_gain_m": float(elev_gain),
+        "elevation_loss_m": float(elev_loss),
+        "min_elevation_m": float(np.min(elev_arr)),
+        "max_elevation_m": float(np.max(elev_arr)),
+        "cell_count": total_cells,
+        "road_cells": int(road_cells),
+        "track_cells": int(track_cells),
+        "trail_cells": int(trail_cells),
+        "off_trail_cells": int(off_trail_cells),
+        "on_trail_pct": float(100 * on_trail_cells / total_cells) if total_cells > 0 else 0,
+    }
+
+
+def main():
+    print("=" * 80)
+    print("OFFROUTE Phase O3a Prototype (Trail Burn-In)")
+    print("=" * 80)
+
+    t0 = time.time()
+
+    # Check for required rasters
+    if not DEFAULT_BARRIERS_PATH.exists():
+        print(f"\nERROR: Barrier raster not found at {DEFAULT_BARRIERS_PATH}")
+        sys.exit(1)
+    if not DEFAULT_TRAILS_PATH.exists():
+        print(f"\nERROR: Trails raster not found at {DEFAULT_TRAILS_PATH}")
+        sys.exit(1)
+
+    # Step 1: Load elevation data
+    print(f"\n[1] Loading DEM for bbox: {BBOX}")
+    dem_reader = DEMReader()
+
+    elevation, meta = dem_reader.get_elevation_grid(
+        south=BBOX["south"],
+        north=BBOX["north"],
+        west=BBOX["west"],
+        east=BBOX["east"],
+    )
+
+    print(f"    Elevation grid shape: {elevation.shape}")
+    print(f"    Cell count: {elevation.size:,}")
+    print(f"    Cell size: {meta['cell_size_m']:.1f} m")
+
+    mem = check_memory_usage()
+    if mem > 0:
+        print(f"    Memory usage: {mem:.1f} GB")
+
+    # Step 2: Load friction data
+    print(f"\n[2] Loading WorldCover friction layer...")
+    friction_reader = FrictionReader()
+
+    friction_raw = friction_reader.get_friction_grid(
+        south=BBOX["south"],
+        north=BBOX["north"],
+        west=BBOX["west"],
+        east=BBOX["east"],
+        target_shape=elevation.shape
+    )
+    friction_mult = friction_to_multiplier(friction_raw)
+
+    print(f"    Friction grid shape: {friction_raw.shape}")
+    print(f"    Water/impassable cells: {np.sum(np.isinf(friction_mult)):,}")
+
+    # Step 3: Load barrier data
+    print(f"\n[3] Loading PAD-US barrier layer...")
+    barrier_reader = BarrierReader()
+
+    barriers = barrier_reader.get_barrier_grid(
+        south=BBOX["south"],
+        north=BBOX["north"],
+        west=BBOX["west"],
+        east=BBOX["east"],
+        target_shape=elevation.shape
+    )
+
+    closed_cells = np.sum(barriers == 255)
+    print(f"    Barrier grid shape: {barriers.shape}")
+    print(f"    Closed/restricted cells: {closed_cells:,}")
+
+    # Step 4: Load trails data
+    print(f"\n[4] Loading OSM trails layer...")
+    trail_reader = TrailReader()
+
+    trails = trail_reader.get_trails_grid(
+        south=BBOX["south"],
+        north=BBOX["north"],
+        west=BBOX["west"],
+        east=BBOX["east"],
+        target_shape=elevation.shape
+    )
+
+    road_cells = np.sum(trails == 5)
+    track_cells = np.sum(trails == 15)
+    trail_cells = np.sum(trails == 25)
+    print(f"    Trails grid shape: {trails.shape}")
+    print(f"    Road cells: {road_cells:,}")
+    print(f"    Track cells: {track_cells:,}")
+    print(f"    Trail cells: {trail_cells:,}")
+    print(f"    Total trail coverage: {100*(road_cells+track_cells+trail_cells)/trails.size:.2f}%")
+
+    mem = check_memory_usage()
+    if mem > 0:
+        print(f"    Memory usage: {mem:.1f} GB")
+
+    # Step 5: Convert start/end to pixel coordinates
+    print(f"\n[5] Converting coordinates...")
+    start_row, start_col = dem_reader.latlon_to_pixel(START_LAT, START_LON, meta)
+    end_row, end_col = dem_reader.latlon_to_pixel(END_LAT, END_LON, meta)
+
+    print(f"    Start: ({START_LAT}, {START_LON}) -> pixel ({start_row}, {start_col})")
+    print(f"    End: ({END_LAT}, {END_LON}) -> pixel ({end_row}, {end_col})")
+
+    # Validate coordinates
+    rows, cols = elevation.shape
+    if not (0 <= start_row < rows and 0 <= start_col < cols):
+        print(f"ERROR: Start point outside grid bounds")
+        sys.exit(1)
+    if not (0 <= end_row < rows and 0 <= end_col < cols):
+        print(f"ERROR: End point outside grid bounds")
+        sys.exit(1)
+
+    # Step 6: Run pathfinder WITH trails
+    print(f"\n[6] Running pathfinder WITH trail burn-in...")
+    t6a = time.time()
+    result_trails = run_pathfinder(
+        elevation, meta, friction_mult, trails, barriers,
+        use_trails=True,
+        start_row=start_row, start_col=start_col,
+        end_row=end_row, end_col=end_col,
+        dem_reader=dem_reader,
+    )
+    t6b = time.time()
+    print(f"    Completed in {t6b - t6a:.1f}s")
+
+    # Step 7: Run pathfinder WITHOUT trails
+    print(f"\n[7] Running pathfinder WITHOUT trail burn-in...")
+    t7a = time.time()
+    result_no_trails = run_pathfinder(
+        elevation, meta, friction_mult, trails, barriers,
+        use_trails=False,
+        start_row=start_row, start_col=start_col,
+        end_row=end_row, end_col=end_col,
+        dem_reader=dem_reader,
+    )
+    t7b = time.time()
+    print(f"    Completed in {t7b - t7a:.1f}s")
+
+    # Step 8: Save GeoJSON outputs
+    print(f"\n[8] Saving GeoJSON outputs...")
+
+    OUTPUT_PATH_WITH_TRAILS.parent.mkdir(parents=True, exist_ok=True)
+
+    if result_trails["success"]:
+        geojson = {
+            "type": "Feature",
+            "properties": {
+                "type": "offroute_with_trails",
+                "phase": "O3a",
+                "trail_burn_in": True,
+                "start": {"lat": START_LAT, "lon": START_LON},
+                "end": {"lat": END_LAT, "lon": END_LON},
+                **{k: v for k, v in result_trails.items() if k not in ["success", "coordinates"]},
+            },
+            "geometry": {
+                "type": "LineString",
+                "coordinates": result_trails["coordinates"],
+            }
+        }
+        with open(OUTPUT_PATH_WITH_TRAILS, "w") as f:
+            json.dump(geojson, f, indent=2)
+        print(f"    Saved: {OUTPUT_PATH_WITH_TRAILS}")
+
+    if result_no_trails["success"]:
+        geojson = {
+            "type": "Feature",
+            "properties": {
+                "type": "offroute_no_trails",
+                "phase": "O3a",
+                "trail_burn_in": False,
+                "start": {"lat": START_LAT, "lon": START_LON},
+                "end": {"lat": END_LAT, "lon": END_LON},
+                **{k: v for k, v in result_no_trails.items() if k not in ["success", "coordinates"]},
+            },
+            "geometry": {
+                "type": "LineString",
+                "coordinates": result_no_trails["coordinates"],
+            }
+        }
+        with open(OUTPUT_PATH_NO_TRAILS, "w") as f:
+            json.dump(geojson, f, indent=2)
+        print(f"    Saved: {OUTPUT_PATH_NO_TRAILS}")
+
+    t_total = time.time()
+
+    # Final report
+    print(f"\n" + "=" * 80)
+    print("SIDE-BY-SIDE COMPARISON: Trail Burn-In Effect")
+    print("=" * 80)
+
+    if result_trails["success"] and result_no_trails["success"]:
+        print(f"{'Metric':<25} {'WITH TRAILS':<20} {'WITHOUT TRAILS':<20} {'Delta':<15}")
+        print("-" * 80)
+
+        metrics = [
+            ("Distance (km)", "total_distance_km", ".2f"),
+            ("Effort time (min)", "total_time_minutes", ".1f"),
+            ("Cell count", "cell_count", "d"),
+            ("Elevation gain (m)", "elevation_gain_m", ".0f"),
+            ("On-trail %", "on_trail_pct", ".1f"),
+            ("Road cells", "road_cells", "d"),
+            ("Track cells", "track_cells", "d"),
+            ("Trail cells", "trail_cells", "d"),
+        ]
+
+        for label, key, fmt in metrics:
+            val_with = result_trails[key]
+            val_without = result_no_trails[key]
+            if isinstance(val_with, int):
+                delta = val_with - val_without
+                delta_str = f"{delta:+d}"
+            else:
+                delta = val_with - val_without
+                delta_str = f"{delta:+.2f}"
+            print(f"{label:<25} {val_with:<20{fmt}} {val_without:<20{fmt}} {delta_str:<15}")
+
+        # Analysis
+        print(f"\n" + "-" * 80)
+        print("ANALYSIS")
+        print("-" * 80)
+
+        time_saved = result_no_trails["total_time_minutes"] - result_trails["total_time_minutes"]
+        if time_saved > 0:
+            print(f"Trail burn-in saves {time_saved:.1f} minutes ({100*time_saved/result_no_trails['total_time_minutes']:.1f}% faster)")
+        elif time_saved < 0:
+            print(f"Trail burn-in adds {-time_saved:.1f} minutes (path seeks trails even if longer)")
+
+        on_trail_with = result_trails["on_trail_pct"]
+        on_trail_without = result_no_trails["on_trail_pct"]
+        if on_trail_with > on_trail_without:
+            print(f"Trail burn-in increases on-trail travel: {on_trail_without:.1f}% → {on_trail_with:.1f}%")
+        else:
+            print(f"Both paths have similar on-trail percentage")
+
+    else:
+        if not result_trails["success"]:
+            print(f"WITH TRAILS: FAILED - {result_trails.get('reason', 'unknown')}")
+        if not result_no_trails["success"]:
+            print(f"WITHOUT TRAILS: FAILED - {result_no_trails.get('reason', 'unknown')}")
+
+    print(f"\n" + "-" * 80)
+    print(f"Total wall time: {t_total - t0:.1f}s")
+
+    # Cleanup
+    dem_reader.close()
+    friction_reader.close()
+    barrier_reader.close()
+    trail_reader.close()
+
+    print("\nPrototype completed.")
+
+
+if __name__ == "__main__":
+    main()

lib/offroute/trails.py

@@ -0,0 +1,174 @@
+"""
+Trail corridor reader for OFFROUTE.
+
+Provides access to the OSM-derived trail raster for pathfinding.
+Trail values replace WorldCover friction where trails exist.
+
+Raster values:
+    0  = no trail (use WorldCover friction)
+    5  = road (0.1× friction)
+    15 = track (0.3× friction)
+    25 = foot trail (0.5× friction)
+"""
+import numpy as np
+from pathlib import Path
+from typing import Tuple, Optional
+
+try:
+    import rasterio
+    from rasterio.windows import from_bounds
+    from rasterio.enums import Resampling
+except ImportError:
+    raise ImportError("rasterio is required for trails layer support")
+
+# Default path to the trails raster
+DEFAULT_TRAILS_PATH = Path("/mnt/nav/worldcover/trails.tif")
+
+# Trail value to friction multiplier mapping
+TRAIL_FRICTION_MAP = {
+    5: 0.1,   # road
+    15: 0.3,  # track
+    25: 0.5,  # foot trail
+}
+
+
+class TrailReader:
+    """Reader for OSM-derived trail corridor raster."""
+
+    def __init__(self, trails_path: Path = DEFAULT_TRAILS_PATH):
+        self.trails_path = trails_path
+        self._dataset = None
+
+    def _open(self):
+        """Lazy open the dataset."""
+        if self._dataset is None:
+            if not self.trails_path.exists():
+                raise FileNotFoundError(
+                    f"Trails raster not found at {self.trails_path}. "
+                    f"Run the Phase B rasterization script first."
+                )
+            self._dataset = rasterio.open(self.trails_path)
+        return self._dataset
+
+    def get_trails_grid(
+        self,
+        south: float,
+        north: float,
+        west: float,
+        east: float,
+        target_shape: Tuple[int, int]
+    ) -> np.ndarray:
+        """
+        Get trail values for a bounding box, resampled to target shape.
+
+        Args:
+            south, north, west, east: Bounding box coordinates (WGS84)
+            target_shape: (rows, cols) to resample to (matches elevation grid)
+
+        Returns:
+            np.ndarray of uint8 trail values:
+                0  = no trail
+                5  = road (0.1× friction)
+                15 = track (0.3× friction)
+                25 = foot trail (0.5× friction)
+        """
+        ds = self._open()
+
+        # Create a window from the bounding box
+        window = from_bounds(west, south, east, north, ds.transform)
+
+        # Read with resampling to target shape
+        # Use nearest neighbor to preserve discrete values
+        trails = ds.read(
+            1,
+            window=window,
+            out_shape=target_shape,
+            resampling=Resampling.nearest
+        )
+
+        return trails
+
+    def sample_point(self, lat: float, lon: float) -> int:
+        """Sample trail value at a single point."""
+        ds = self._open()
+
+        # Get pixel coordinates
+        row, col = ds.index(lon, lat)
+
+        # Check bounds
+        if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
+            return 0  # Out of bounds = no trail
+
+        # Read single pixel
+        window = rasterio.windows.Window(col, row, 1, 1)
+        value = ds.read(1, window=window)
+        return int(value[0, 0])
+
+    def close(self):
+        """Close the dataset."""
+        if self._dataset is not None:
+            self._dataset.close()
+            self._dataset = None
+
+
+def trails_to_friction(trails: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Convert trail values to friction multipliers.
+
+    Args:
+        trails: uint8 array of trail values (0, 5, 15, or 25)
+
+    Returns:
+        Tuple of:
+        - friction: float32 array of friction multipliers
+        - has_trail: bool array indicating where trails exist
+    """
+    friction = np.ones_like(trails, dtype=np.float32)
+    has_trail = trails > 0
+
+    # Apply friction values where trails exist
+    friction[trails == 5] = 0.1   # road
+    friction[trails == 15] = 0.3  # track
+    friction[trails == 25] = 0.5  # foot trail
+
+    return friction, has_trail
+
+
+if __name__ == "__main__":
+    print("Testing TrailReader...")
+
+    if not DEFAULT_TRAILS_PATH.exists():
+        print(f"Trails raster not found at {DEFAULT_TRAILS_PATH}")
+        print("Run Phase B rasterization first.")
+        exit(1)
+
+    reader = TrailReader()
+
+    # Test point sampling - Twin Falls downtown (should have roads)
+    test_lat, test_lon = 42.563, -114.461
+    trail_value = reader.sample_point(test_lat, test_lon)
+    print(f"\nTwin Falls ({test_lat}, {test_lon}): trail value = {trail_value}")
+    label = {0: "no trail", 5: "road", 15: "track", 25: "trail"}.get(trail_value, "unknown")
+    print(f"  Type: {label}")
+
+    # Test grid read for test bbox
+    trails = reader.get_trails_grid(
+        south=42.21, north=42.60, west=-114.76, east=-113.79,
+        target_shape=(400, 1000)
+    )
+    print(f"\nGrid test shape: {trails.shape}")
+
+    unique, counts = np.unique(trails, return_counts=True)
+    print("Value distribution:")
+    for v, c in zip(unique, counts):
+        pct = 100 * c / trails.size
+        label = {0: "no trail", 5: "road", 15: "track", 25: "trail"}.get(v, f"unknown({v})")
+        print(f"  {label}: {c:,} pixels ({pct:.2f}%)")
+
+    # Test conversion to friction
+    friction, has_trail = trails_to_friction(trails)
+    print(f"\nTrail coverage: {100 * np.sum(has_trail) / trails.size:.2f}%")
+    print(f"Friction range (on trails): {friction[has_trail].min():.1f} - {friction[has_trail].max():.1f}")
+
+    reader.close()
+    print("\nTrailReader test complete.")

lib/processors/zim_processor.py

@@ -77,10 +77,73 @@ def _text_hash(text):
     return hashlib.md5(text.encode('utf-8')).hexdigest()
 
 
+def _flatten_table(table_el):
+    """Convert a <table> element to pipe-delimited text.
+
+    Each <tr> becomes a row with cells joined by ' | '.
+    Returns the formatted table as a string with blank lines around it.
+    """
+    rows = []
+    for tr in table_el.iter('tr'):
+        cells = []
+        for cell in tr:
+            if cell.tag in ('td', 'th'):
+                cell_text = (cell.text_content() or '').strip()
+                # Collapse internal whitespace in each cell
+                cell_text = re.sub(r'\s+', ' ', cell_text)
+                if cell_text:
+                    cells.append(cell_text)
+        if cells:
+            rows.append(' | '.join(cells))
+    if not rows:
+        return ''
+    return '\n'.join(rows)
+
+
+def _preprocess_tree(doc):
+    """Pre-process HTML tree to add delimiters before text_content() flattens it.
+
+    Handles: <table>, <br>, <li>, <dt>, <dd> -- elements that lxml's
+    text_content() would concatenate without any separators.
+    """
+    from lxml import etree
+
+    # 1. Replace <table> elements with their pipe-delimited text
+    for table in list(doc.iter('table')):
+        formatted = _flatten_table(table)
+        if formatted:
+            replacement = etree.Element('div')
+            replacement.text = '\n\n' + formatted + '\n\n'
+            parent = table.getparent()
+            if parent is not None:
+                parent.replace(table, replacement)
+        else:
+            table.drop_tree()
+
+    # 2. <br> -> inject newline
+    for br in list(doc.iter('br')):
+        br.tail = '\n' + (br.tail or '')
+
+    # 3. <li> -> inject newline + "- " prefix
+    for li in list(doc.iter('li')):
+        li.text = '- ' + (li.text or '')
+        li.tail = '\n' + (li.tail or '')
+
+    # 4. <dt> -> inject newline before
+    for dt in list(doc.iter('dt')):
+        dt.tail = '\n' + (dt.tail or '')
+
+    # 5. <dd> -> inject newline + indent
+    for dd in list(doc.iter('dd')):
+        dd.text = '  ' + (dd.text or '')
+        dd.tail = '\n' + (dd.tail or '')
+
+
 def _html_to_text(html_bytes):
     """Convert HTML bytes to clean text via lxml.
 
     Strips nav, footer, script, style elements. Decodes entities.
+    Pre-processes tables, lists, and line breaks for proper delimiters.
     Normalizes whitespace.
     """
     try:
@@ -93,6 +156,9 @@ def _html_to_text(html_bytes):
         for el in doc.iter(tag):
             el.drop_tree()
 
+    # Pre-process tree: tables -> pipe-delimited, br -> newlines, li -> dashes
+    _preprocess_tree(doc)
+
     # Extract text
     text = doc.text_content()