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ADR-044: Geospatial Satellite Integration

Status

Accepted

Context

RuView generates real-time 3D point clouds from camera + WiFi CSI, but these exist in a local coordinate frame with no geographic reference. Integrating free satellite imagery, terrain elevation, and map data provides environmental context that enables the ruOS brain to reason about the physical world beyond the room.

Decision

Data Sources (all free, no API keys)

Source	Data	Resolution	Update	Format
EOX Sentinel-2 Cloudless	Satellite tiles	10m	Static mosaic	XYZ/JPEG
SRTM GL1 (NASA)	Elevation/DEM	30m (1-arcsec)	Static	Binary HGT
Overpass API (OSM)	Buildings, roads	Vector	Real-time	JSON
ip-api.com	IP geolocation	~1km	Per-request	JSON
Sentinel-2 STAC	Temporal satellite	10m	Every 5 days	COG/STAC
Open Meteo	Weather	Point	Hourly	JSON

Architecture

Pure Rust implementation in wifi-densepose-geo crate. No GDAL/PROJ/GEOS — coordinate transforms implemented directly (~250 LOC). Tile caching on disk at ~/.local/share/ruview/geo-cache/.

Coordinate System

WGS84 for geographic coordinates
ENU (East-North-Up) as the bridge between local sensor frame and world
Local sensor frame: camera origin, +Z forward, +Y up

Temporal Awareness

Nightly scheduled fetch of Sentinel-2 latest imagery + OSM diffs + weather. Changes detected via image comparison and stored as brain memories for contrastive learning.

Brain Integration

Geospatial context stored as brain memories:

spatial-geo: location, elevation, nearby landmarks
spatial-change: detected changes in satellite/OSM data
spatial-weather: current conditions + forecast
spatial-season: vegetation index, snow cover, seasonal patterns
spatial-local: hyperlocal web context from Common Crawl WET

Extended Data Sources (via ruvector WET/Common Crawl)

Source	Data	Use
Common Crawl WET	Web text near location	Local business info, reviews, events
Wikidata	Structured knowledge	Building names, POI descriptions
NASA FIRMS	Active fire (3-hour)	Safety alerts
USGS Earthquakes	Seismic events	Safety context
OpenAQ	Air quality (PM2.5)	Environmental health
Overture Maps	Building footprints (Meta/MS)	Higher quality than OSM

The ruvector brain server has existing web_ingest + Common Crawl support. WET files filtered by geographic URL patterns provide hyperlocal context.

Consequences

Positive

Agent gains environmental awareness beyond the room
Temporal data enables seasonal calibration of CSI sensing
Change detection finds construction, vegetation, weather effects
All data sources are genuinely free with no API keys

Negative

Initial data fetch requires internet (~2MB tiles + ~25MB DEM)
Cached data becomes stale (mitigated by nightly refresh)
IP geolocation has ~1km accuracy (mitigated by manual override)

3.0 KiB Raw Blame History