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SYSTEM ARCHITECTURE • YONDA-002

Spatial Intelligence & Edge Sync

The engineering reality of delivering sub-second spatial audio across 5,000km of cellular dead-zones using predictive geometry.

Blake Carter|Version v3.0.0|CANONICAL
Predictive Cone
50 km
The forward bounding box cached locally during edge sync.
Data Structure
Geohash (L7)
153m grids replace brittle polygon intersections.
Earth Engine
Bi-weekly
Satellites recalculate canopy density every 14 days.

The Polygons vs. Reality

Initially, we attempted to map environmental biomes and trigger zones manually using complex OpenStreetMap (OSM) multi-polygons. This proved fundamentally brittle. Forcing a low-power mobile device running CarPlay to calculate complex polygon ST_Intersects at 110 km/h drains the battery, causes thermal throttling, and results in audio triggering 5 seconds too late.

Furthermore, static polygons suffer from Seasonal Drift. A riverbed might be lush in winter but a dry dust-bowl in summer. Static vectors don’t know the difference.

Earth Engine & The Geohash Grid

Instead of running intersections on the client, Yonda shifts the heavy lifting to an asynchronous cloud pipeline driven by Google Earth Engine.

Every 14 days, a GCP Cloud Function ingests 10m Sentinel-2 multispectral surface reflectance data for all supported highway corridors. We analyze the NDVI (Vegetation Index) and rasterize the physical world into a lightweight spatial index.

  1. Quantization: The continuous satellite data is quantized into a Level-7 Geohash grid (approx. 153m × 153m).
  2. Parameterization: Each Geohash cell is assigned a specific acoustic code (e.g., DRY_SCLEROPHYLL_04).
  3. Distribution: These are compiled into highly compressed SQLite dictionaries and pushed to the Vercel Edge Cache.

When the car drives, the mobile client simply does a fast string-match against its current GPS Geohash. The time complexity drops from $O(N \log N)$ polygon math to an $O(1)$ dictionary lookup.

Predictive Edge Caching

The defining feature of regional travel is the sudden, catastrophic loss of cellular signal. A streaming architecture is unacceptable.

To guarantee playback continuity, the client runs a predictive routing algorithm. By analyzing the vector of travel and the current highway geometry, it projects a 50km Trajectory Cone.

While the phone still has 4G/5G, it reaches out to the Edge CDN and downloads the entire 450 KB SQLite package for that forward cone. This payload contains every Geohash biome parameter, the Story-Loom narrative graph, and all associated highly-compressed opus audio files.

When the bars drop to zero, the app doesn’t even notice.

OFFLINE PREDICTIVE CACHING PIPELINESYS.CACHE
[ In-Car Client ] -----(Vector & Speed)-----> [ Local Path Predictor ]
     |                                              |
     | (If cell signal == True)                     | (Calculates 50km cone)
     |                                              v
     +--------------------------------------> [ Edge CDN / SQLite Cache ]
                                                    |
                                                    | (Returns 450 KB bundle)
                                                    v
                                            [ Local Device Storage ]
                                            (Zero-latency $O(1)$ lookups)