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docs: add OFFROUTE effort-based routing architecture
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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# OFFROUTE — Off-Network Effort-Based Routing Architecture
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**Status:** Draft
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**Author:** Matt / Claude
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**Date:** 2026-05-07
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**Canonical location:** `matt/refactored-recon` alongside PROJECT-BIBLE.md, NAV-INTEGRATION-v4.md
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---
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## 1. Vision
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From any arbitrary point in the backcountry — no trails, no roads, no signal — route via effort cost and safety to the nearest trail, to a BLM/forest road, to a paved road, to home. Four segments, one continuous path, one GeoJSON response.
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The system serves two interfaces:
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- **Navi frontend** (`navi.echo6.co`) — visual route overlay on the map
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- **Aurora via Meshtastic** — text-based step-by-step directions for a lost person with no map display
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This capability does not exist in any open-source consumer product. CalTopo, OnX, Gaia GPS, AllTrails — all route on-network only. The military has Primordial Ground Guidance (closed-source ATAK plugin). We are building the open, self-hosted equivalent.
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---
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## 2. The Routing Chain
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```
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[Lost person]
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│
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▼
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┌──────────────────────────────────────────┐
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│ Segment 1: WILDERNESS → TRAIL │
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│ Engine: Raster cost-surface pathfinder │
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│ Cost: slope effort + vegetation + │
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│ water barriers + land ownership │
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│ Output: lat/lon waypoint sequence │
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└──────────────────────────────────────────┘
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│ snap to nearest trail entry point
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▼
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┌──────────────────────────────────────────┐
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│ Segment 2: TRAIL → BLM/FOREST ROAD │
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│ Engine: Valhalla (pedestrian/MTB) │
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│ Cost: elevation-aware hike/bike profile │
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└──────────────────────────────────────────┘
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│ transition to road network
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▼
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┌──────────────────────────────────────────┐
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│ Segment 3: BLM ROAD → PAVED ROAD │
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│ Engine: Valhalla (auto/motorcycle) │
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│ Cost: standard + surface preference │
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└──────────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────┐
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│ Segment 4: PAVED ROAD → HOME │
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│ Engine: Valhalla (auto) │
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│ Cost: standard routing │
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└──────────────────────────────────────────┘
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```
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Segments 2–4 already work today via Valhalla. **Segment 1 is the engineering gap.**
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---
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## 3. Endpoint Design
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### `POST /api/offroute`
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**Request:**
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```json
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{
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"start": { "lat": 43.512, "lon": -114.823 },
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"destination": { "lat": 42.736, "lon": -114.514 },
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"mode": "foot",
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"max_search_km": 15
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}
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```
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**Modes:** `foot` | `mtb` | `atv`
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**Response:**
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```json
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{
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"segments": [
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{
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"type": "wilderness",
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"geometry": { "type": "LineString", "coordinates": [...] },
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"distance_m": 4200,
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"elevation_gain_m": 310,
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"elevation_loss_m": 85,
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"estimated_time_min": 72,
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"surface": "cross-country",
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"instructions": [
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{ "bearing": 245, "distance_m": 320, "terrain": "sagebrush slope", "grade_pct": 8 },
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{ "bearing": 260, "distance_m": 510, "terrain": "drainage crossing", "grade_pct": -12 }
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]
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},
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{
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"type": "trail",
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"geometry": { "type": "LineString", "coordinates": [...] },
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"trail_name": "Pioneer Cabin Trail",
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"distance_m": 6100,
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"estimated_time_min": 85
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},
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{
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"type": "road_unpaved",
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"geometry": { "type": "LineString", "coordinates": [...] },
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"road_name": "FR-227",
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"distance_m": 12400,
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"estimated_time_min": 22
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},
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{
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"type": "road_paved",
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"geometry": { "type": "LineString", "coordinates": [...] },
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"distance_m": 34000,
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"estimated_time_min": 28
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}
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],
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"total_distance_m": 56700,
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"total_time_min": 207,
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"confidence": 0.82
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}
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```
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**Aurora tool integration:** Add `offroute` to `nav_tools.py` alongside existing `route()` and `reverse_geocode()`. The semantic query router gets a new embedding for "I'm lost, help me get home" / "navigate to nearest road" type queries.
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---
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## 4. Pathfinder Architecture (Segment 1)
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### 4.1 No Pre-Rendered Slope Rasters
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The pathfinder does NOT need pre-computed slope layers, GDAL processing, or reprojection. It reads elevation directly:
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1. Routing request arrives with a start point and search radius
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2. Determine which PMTiles z12 tiles cover the search area
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3. Fetch + decode Terrarium tiles from `planet-dem.pmtiles` → numpy elevation arrays
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4. Cache decoded arrays keyed by (z, x, y) — LRU, in-memory
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5. A* / Dijkstra runs on the elevation grid, computing grade between neighbors on the fly
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6. Cost function = `grade → effort model → multiply by land-cover friction → check barriers`
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### 4.2 Elevation Data Source
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**Primary:** `planet-dem.pmtiles` (658GB on pi-nas, served via nginx at `/tiles/planet-dem.pmtiles`)
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- Mapterhorn, Copernicus GLO-30 source, Terrarium encoding (lossless WebP)
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- z12 with 512px tiles = ~13–16m pixels at Idaho latitude
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- 30m effective resolution (upsampled from source)
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- Decode: `elevation = (R * 256 + G + B/256) - 32768` (metres, EGM2008)
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- Precision: ~3.9mm quantization — far below source noise (~4m RMSE)
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**Upgrade path:** USGS 3DEP 1/3 arc-second (10m bare-earth DTM, CONUS). Same architecture, denser grid. Free download. Address when/if 30m proves insufficient for safety.
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**Regional GeoTIFFs** (203GB on NAS at `/mnt/nas/nav/contour-rebuild/dem/`): Keep as insurance until this pipeline is validated, then delete.
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### 4.3 Cost Function
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For each candidate move from cell A to cell B:
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```python
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def travel_cost(elev_a, elev_b, distance_m, friction_ab):
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grade = (elev_b - elev_a) / distance_m
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# Safety gate — impassable above threshold
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slope_deg = math.degrees(math.atan(abs(grade)))
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if slope_deg > MAX_SLOPE[mode]: # foot=40°, mtb=25°, atv=30°
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return INF
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# Effort model (speed in km/h)
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if mode == "foot":
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# Tobler off-path hiking function
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speed = 0.6 * 6.0 * math.exp(-3.5 * abs(grade + 0.05))
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elif mode == "mtb":
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# Herzog wheeled-transport polynomial (crit_slope=8%)
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speed = herzog_wheeled(grade, crit_slope=0.08, base_speed=12)
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elif mode == "atv":
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# Herzog with higher base speed and slope tolerance
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speed = herzog_wheeled(grade, crit_slope=0.15, base_speed=25)
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# Time cost (seconds to traverse this cell)
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time_s = (distance_m / 1000.0) / speed * 3600.0
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# Multiply by land-cover friction
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time_s *= friction_ab
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return time_s
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```
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**Tobler off-path:** `W = 0.6 × 6 × exp(-3.5 × |S + 0.05|)` km/h
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Peak speed 3.6 km/h at ~-2.86° (slight downhill). The 0.6 multiplier is the off-trail penalty.
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**Herzog wheeled-transport:** sixth-degree polynomial fitted to wheeled vehicle energy expenditure. Has a `crit_slope` parameter where switchbacks become more efficient than direct climb. Best published proxy for MTB/ATV in open-source literature.
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**Reference implementations:** R `leastcostpath` package contains 30+ validated cost functions including Tobler, Tobler off-path, Irmischer-Clarke (male/female/off-path, fitted to USMA cadets), Naismith-Langmuir, Herzog, Minetti, Campbell 2019 percentiles. Port as needed.
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### 4.4 Friction Layers (Cost Surface Inputs)
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All pre-computed offline, tiled, cached. Updated infrequently.
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| Layer | Source | Resolution | Purpose | Update Frequency |
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| Elevation | planet-dem.pmtiles | ~30m (z12) | Slope/grade calculation | Static |
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| Land cover | NLCD | 30m | Vegetation traversal friction | ~Annual |
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| Waterways | OSM | Rasterized from vectors | Barrier (∞ cost) except at bridges/fords | Weekly from planet PBF |
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| Water bodies | OSM `natural=water` | Rasterized polygons | Barrier (∞) | Weekly |
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| Cliffs | OSM `natural=cliff` | Rasterized lines | Barrier (∞) | Weekly |
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| Land ownership | PAD-US | Polygon raster | Access restrictions per mode | ~Quarterly |
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| Trails/roads | OSM + USFS | Rasterized lines | Low-cost corridors (negative friction) | Weekly |
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**NLCD friction mapping (foot mode example):**
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| NLCD Class | Description | Friction Multiplier |
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|---|---|---|
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| 11 | Open Water | ∞ |
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| 21 | Developed, Open Space | 1.0 |
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| 22 | Developed, Low Intensity | 1.2 |
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| 31 | Barren Land | 1.1 |
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| 41 | Deciduous Forest | 1.8 |
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| 42 | Evergreen Forest | 2.0 |
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| 43 | Mixed Forest | 1.9 |
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| 52 | Shrub/Scrub | 1.5 |
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| 71 | Grassland/Herbaceous | 1.2 |
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| 90 | Woody Wetlands | 3.5 |
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| 95 | Emergent Herbaceous Wetlands | 4.0 |
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Mode-specific adjustments: MTB and ATV get higher penalties on forest/wetland. ATV gets ∞ on wilderness-designated areas (PAD-US `Des_Tp = WA`).
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**Trail burn-in:** Rasterize OSM trails/tracks as cells with reduced friction (trail cell = 0.5× base, track = 0.3×, road = 0.1×). The pathfinder naturally gravitates toward and follows these corridors without special logic.
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### 4.5 Engine Choice
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**Recommended: scikit-image `MCP_Geometric` for initial build.**
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- Cython Dijkstra, 1–5 seconds on 2–4M cell grids
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- `find_costs(start)` computes cumulative cost surface once; `traceback(target)` for any target is O(path length) — reuse for "nearest trail," "nearest road," and destination all in one pass
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- `MCP_Flexible` subclass allows overriding `_travel_cost()` for anisotropic costs (uphill ≠ downhill)
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- Pure Python integration with Flask backend
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- Memory OK up to ~20–40M cells on 24GB
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**Performance path: Rust `pathfinding` crate as a microservice.**
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- A*, Dijkstra, HPA* (hierarchical) all available
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- Custom successor function encodes anisotropic cost
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- Sub-second on 4M cells
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- `hierarchical_pathfinding` crate enables multi-resolution: coarse pass → refine in corridor
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- Wrap in Axum HTTP server, call from Flask
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**Decision:** Start with scikit-image Python. If latency is a problem, rewrite the inner loop in Rust. The cost function, data pipeline, and API don't change.
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### 4.6 Multi-Resolution Strategy
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For routes where the wilderness segment exceeds ~10km, full-resolution pathfinding on the entire search area gets expensive. Use the Primordial Ground Guidance approach:
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1. **Coarse pass:** Downsample cost grid 4× (120m cells). Solve A*. Sub-second.
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2. **Corridor extraction:** Buffer the coarse path by 200m.
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3. **Fine pass:** Re-solve at native 30m resolution only within the corridor. Sub-second.
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4. **Total:** <2 seconds for a 15km wilderness segment.
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### 4.7 Network Hand-Off
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The raster pathfinder needs to know where the trail/road network starts so it can stop:
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1. **Pre-compute trail entry points:** Extract from OSM all endpoints and intersections of `highway=path|track|footway|bridleway|unclassified|tertiary|secondary|primary`. Store as a PostGIS point table (or SQLite spatial index in `navi.db`).
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2. **Rasterize entry points** onto the cost grid as target cells.
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3. **Run `MCP.find_costs(start)`** — the Dijkstra wave expands until it reaches any entry-point cell. Use `goal_reached()` override in `MCP_Flexible` for early termination.
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4. **Snap** the reached entry point to its nearest Valhalla graph node.
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5. **Call Valhalla** from that node to destination with appropriate costing profile.
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6. **Concatenate** raster path + Valhalla path into one GeoJSON with per-segment metadata.
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---
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## 5. Data Acquisition Checklist
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| Dataset | Status | Size | Action |
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|---|---|---|---|
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| DEM (planet-dem.pmtiles) | ✅ Have it | 658GB | Serving via nginx from pi-nas |
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| NLCD Land Cover (CONUS) | ❌ Not acquired | ~5GB | Download from USGS MRLC |
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| NLCD Tree Canopy (CONUS) | ❌ Not acquired | ~2GB | Optional — continuous friction surface |
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| OSM Planet PBF | ❌ Not acquired for this use | ~70GB | Extract waterways, cliffs, trails via osmium |
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| PAD-US | ✅ Have source | 1.6GB in /mnt/nav/padus/ | Rasterize by access class |
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| USFS Trail/Road layers | ✅ Have PMTiles | 848MB + 496MB | Need raw vectors for rasterization |
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| Trail entry points index | ❌ Not built | ~50MB | Extract from OSM + USFS |
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**First acquisition:** NLCD. It's the single most impactful layer after the DEM — without land cover, the pathfinder can't distinguish open meadow from dense forest.
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---
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## 6. Safety Considerations
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This system may guide people through dangerous terrain. Design constraints:
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- **Hard slope cutoffs are non-negotiable.** No route segment should ever cross terrain above the mode's max slope threshold, regardless of how much faster the direct path would be.
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- **Confidence scoring:** Every response includes a `confidence` field (0.0–1.0) based on: DEM resolution vs route steepness, distance from nearest verified trail data, land cover data freshness, number of barrier crossings.
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- **Fallback behaviors:** If no safe route exists within `max_search_km`, return an error with the direction and distance to the nearest trail (as a bearing, not a route). Never hallucinate a route through impassable terrain.
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- **Per-step user confirmation (Aurora/Meshtastic):** In text mode, Aurora should confirm each major terrain transition ("You will cross a drainage heading southwest — confirm you can see safe footing"). A lost person should never blindly follow instructions into terrain they can't visually verify.
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- **DSM vs DTM caveat:** Copernicus GLO-30 is a Digital Surface Model (includes treetops, buildings). A flat meadow next to tall pines will show false slope at the treeline. The system should note this in Aurora's instructions for forested areas.
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- **30m resolution risk:** A 15m-wide cliff band can be smoothed into a single "steep but passable" cell. The safety gate catches obvious cliffs but may miss narrow features. Documented limitation; mitigated by upgrading to 10m USGS 3DEP in the future.
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---
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## 7. Implementation Phases
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### Phase O1: Foundation
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- Acquire NLCD CONUS land cover
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- Build PMTiles elevation decoder + tile cache module
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- Implement Tobler off-path cost function
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- Prototype: scikit-image MCP on a small Idaho bbox (e.g., 20km × 20km around Sun Valley)
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- Validate: does the path avoid canyons, prefer gentle slopes, follow drainages?
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### Phase O2: Friction Integration
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- Rasterize NLCD into friction grid
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- Rasterize OSM waterways/cliffs as barriers
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- Rasterize PAD-US access restrictions
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- Burn OSM trails/roads as low-cost corridors
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- Combined cost surface for foot mode
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### Phase O3: Network Hand-Off
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- Build trail entry point index from OSM + USFS
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- Implement MCP → Valhalla stitching
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- `/api/offroute` endpoint (foot mode only)
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- GeoJSON response with per-segment metadata
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### Phase O4: Multi-Mode + Aurora
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- Add MTB cost function (Herzog wheeled-transport)
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- Add ATV cost function
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- Mode-specific barrier rules (wilderness restrictions for MTB/ATV)
|
||||||
|
- Aurora tool integration — `offroute` in nav_tools.py
|
||||||
|
- Meshtastic text-based instruction generation (bearings, terrain descriptions)
|
||||||
|
|
||||||
|
### Phase O5: Performance + Polish
|
||||||
|
- Multi-resolution pathfinding (coarse → corridor → fine)
|
||||||
|
- Rust pathfinder microservice (if Python latency is insufficient)
|
||||||
|
- Confidence scoring
|
||||||
|
- Navi frontend route visualization with segment coloring
|
||||||
|
- Elevation profile display per segment
|
||||||
|
|
||||||
|
### Phase O6: Pi 5 Field Kit
|
||||||
|
- Offline PMTiles elevation access
|
||||||
|
- Pre-baked cost tiles for Idaho/CONUS-West
|
||||||
|
- Bbox-filter packager for all spatial datasets
|
||||||
|
- Full offline operation via Meshtastic ↔ Aurora ↔ offroute chain
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 8. Infrastructure
|
||||||
|
|
||||||
|
**Runtime services (VM 1130):**
|
||||||
|
- `/api/offroute` — Flask endpoint in RECON dashboard
|
||||||
|
- Tile cache — LRU in-memory decoded elevation arrays
|
||||||
|
- Valhalla Docker :8002 — on-network routing (already running)
|
||||||
|
|
||||||
|
**Data (VM 1130 /mnt/nav/):**
|
||||||
|
- Pre-baked friction rasters (NLCD, barriers, trails) — tiled GeoTIFF or COG
|
||||||
|
- Trail entry point index — SQLite spatial in navi.db
|
||||||
|
|
||||||
|
**Data (pi-nas /mnt/nas/nav/):**
|
||||||
|
- planet-dem.pmtiles — 658GB, served via nginx
|
||||||
|
- Regional GeoTIFF DEMs — 203GB, insurance until pipeline validated
|
||||||
|
|
||||||
|
**Compute (cortex or matt-desktop):**
|
||||||
|
- One-time cost surface generation jobs (NLCD rasterization, OSM extraction, barrier tiling)
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 9. Key Decisions Made
|
||||||
|
|
||||||
|
| Decision | Rationale |
|
||||||
|
|---|---|
|
||||||
|
| No pre-rendered slope rasters | Pathfinder computes grade on the fly from cached elevation arrays. Simpler, no GDAL dependency at runtime. |
|
||||||
|
| planet-dem.pmtiles as single elevation source | Same data already drives contours + hillshade. 30m sufficient for first build. Global coverage. |
|
||||||
|
| scikit-image MCP for initial engine | Cython Dijkstra, proven on 2–4M cell grids, Python-native, anisotropic via MCP_Flexible. Rust upgrade path if needed. |
|
||||||
|
| Tobler off-path as primary foot cost model | Best-validated off-trail hiking function. Inherently anisotropic. 0.6× off-trail multiplier built in. |
|
||||||
|
| Trail burn-in (not separate hand-off logic) | Rasterizing trails as low-cost cells lets the pathfinder naturally follow them without mode-switching logic. |
|
||||||
|
| Pre-baked friction rasters (offline) | NLCD, barriers, and land ownership change slowly. Build once, cache, update periodically. |
|
||||||
|
| Multi-resolution for long routes | Coarse pass → corridor → fine pass. Standard technique from military route planning (Primordial Ground Guidance). |
|
||||||
|
| Confidence scoring on every response | Safety-critical system. User must know when to trust vs. verify the route. |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 10. Open Questions
|
||||||
|
|
||||||
|
- [ ] What is the right `max_slope` cutoff per mode? Needs field testing / literature review.
|
||||||
|
- [ ] Should the pathfinder use A* (faster, needs admissible heuristic) or Dijkstra (guaranteed optimal, slower)? MCP uses Dijkstra; pyastar2d uses A*.
|
||||||
|
- [ ] How to generate natural-language terrain descriptions for Aurora from raster data? (e.g., "sagebrush slope" vs. "forested drainage")
|
||||||
|
- [ ] Should we pre-compute the full cost surface for Idaho/CONUS-West, or generate it on demand per request?
|
||||||
|
- [ ] How to handle seasonal/weather variations? (Snow, spring runoff, wildfire closures)
|
||||||
|
- [ ] Valhalla pedestrian elevation costing (PR #3234) — test and validate before relying on it for segments 2–4.
|
||||||
|
- [ ] USFS MVUM (Motor Vehicle Use Maps) — authoritative ATV/4WD legal access layer. Acquire and integrate for ATV mode.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## References
|
||||||
|
|
||||||
|
- Tobler, W. (1993). Three Presentations on Geographical Analysis and Modeling. NCGIA TR 93-1.
|
||||||
|
- Irmischer, I.J. & Clarke, K.C. (2018). Measuring and modeling the speed of human navigation. *Cartography and GIS*, 45(2), 177-186.
|
||||||
|
- Herzog, I. (2020). Spatial Analysis Based on Cost Functions. In *Archaeological Spatial Analysis*.
|
||||||
|
- Lewis, J. (2023). `leastcostpath` R package. CRAN.
|
||||||
|
- GRASS GIS. `r.walk` manual. grass.osgeo.org.
|
||||||
|
- Hoover, B. et al. (2019). CostMAP: An open-source software package for developing cost surfaces. LANL.
|
||||||
|
- Mapterhorn project. mapterhorn.com. BSD-3.
|
||||||
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