San Juan Watershed Riparian Watch

What it is

A basin-scale monitoring system for riparian ecosystems in the San Juan River watershed (Colorado + New Mexico): satellite imagery in, health-graded map intelligence out. It learns where riparian vegetation actually is from satellite time series — instead of assuming a fixed distance from a stream — then scores its condition (including invasive tamarisk and Russian-olive cover) and tracks change across the Earth-observation record. It’s where my two careers meet: a decade of GIS engineering and an environmental-studies background on one side, modern data engineering and ML on the other.

The live site is an interactive story map of the corridor, a Random Forest vs. Ai2 OlmoEarth foundation-model field test, and a grounded RAG agent that streams cited answers about the watershed.

The pipeline

• Ingest — a STAC-based ETL pulls Sentinel-2, Sentinel-1, Landsat, and 3DEP DEM/LiDAR from the Planetary Computer STAC API, plus NLCD, LANDFIRE, NWI, and SSURGO — organized by HUC12 tiles and NHD reach segments. Python data engineering with rasterio, GeoPandas, and pytest.
• Learn — geospatial ML with Random Forest baselines, weak-label fusion, spatial cross-validation, and multitemporal feature engineering (NDVI phenology, change detection); scoped for comparison against the OlmoEarth foundation model.
• Serve — C#/.NET 10 minimal APIs with Dapper + Npgsql, orchestrated with .NET Aspire, instrumented with OpenTelemetry.
• Decide — a React 18 + MapLibre GL decision-support UI: MVT vector tiles, a timelapse slider, health-grade styling.
• Ask — the Quartzose retrieval harness is integrated for document intelligence over environmental datasets, with geo-resolved citations.

Why it’s interesting engineering

Most portfolio ML projects stop at a notebook. This one is a system: reproducible ETL over public satellite archives, models validated the way spatial data demands (spatial CV, not random splits), a typed API layer, and a UI a watershed manager could actually use — deployed with Docker and PostgreSQL, guarded in production by rate limits, input shields, and Cloudflare Turnstile.

And it holds itself to scientific standards: the repo carries a public engineering & methodology walkthrough inviting scrutiny of the weak points, and a RETRACTIONS.md — when a result’s interpretation didn’t survive re-verification, the correction was published, not buried. The headline finding stands either way: on an out-of-distribution reach, the pixel-wise Random Forest collapses to near-chance while the OlmoEarth foundation model holds — a measured demonstration of classical-ML brittleness to distribution shift.