Capstone defense and synthesis (Capstone 5 week)
Track 5 culminates here: a complete end-to-end pipeline from raw GOES NetCDF to served REST endpoint, visualized on a Cesium globe. This is the bar for the highest LaunchDetect Academy credential.
You've come 29 weeks. You can build a satellite-imagery pipeline that detects events on Earth in real-time. What will you point it at?
Capstone 5 is the synthesis. Everything you've learned, in one pipeline. The technical work is teachable. The harder question is the one you've been carrying: what would you build, and who would it be for?
Learning objectives
- Synthesize learnings across all 30 weeks
- Present a working production-grade pipeline
- Defend design choices
- Identify the next problem in space GIS
Primer
Thirty weeks. Five tracks. You've moved from "what is a coordinate system" to building a production AWS pipeline that ingests real geostationary thermal imagery and serves geocoded detections over a REST API. This week is the synthesis: stitch everything together into one end-to-end deliverable that demonstrates expert-level competence in space GIS.
What you've learned
- Track 1 (Weeks 1–4): GIS foundations — coordinate systems, vector vs raster, QGIS, mapping global launch sites. Spatial literacy.
- Track 2 (Weeks 5–10): Spatial analysis + orbital mechanics — PostGIS, SGP4, ground tracks, ground-station visibility, spaceport-to-orbit matching. Geometric reasoning.
- Track 3 (Weeks 11–15): Remote sensing — EM spectrum, Landsat / Sentinel-2 / GOES-R, thermal IR, plume detection, parallax correction. Sensor physics.
- Track 4 (Weeks 16–20): Web + real-time — Leaflet / MapLibre / OpenLayers, vector tiles, CesiumJS, WebSockets, change detection. Delivery.
- Track 5 (Weeks 21–29): Production + expert — multi-sensor fusion, ML for raster, SAR, geodesy, AR, cloud-native formats, AWS pipelines, ethics, geospatial APIs. Production scale + responsibility.
The capstone deliverable
Capstone 5: End-to-End Detection Pipeline. A complete production-style pipeline running every layer of the course:
- Ingest — 10 frames of real GOES-18 ABI Band 7 NetCDF from the NOAA AWS Open Data bucket, spanning a known launch event.
- Georeference — convert fixed-grid scan angles to lat/lon (Week 15) with parallax correction applied.
- Detect — convert radiance to brightness temperature, threshold-detect hotspots (Week 14), apply morphological cleaning (Week 20).
- Cluster — group hotspot pixels across consecutive frames into plume tracks. A real plume appears in 3–5 consecutive frames; isolated single-frame hotspots are noise.
- Score — apply a simple confidence heuristic: spatial coincidence with a known spaceport (within 50 km), temporal pattern matching (the track rises then falls), brightness profile.
- Persist — write final detections to PostGIS with proper GIST indexes (Week 6).
- Serve — expose
/detectionsREST endpoints via FastAPI (Week 29) with OpenAPI docs. - Visualize — render detections on a Cesium globe (Week 18) loaded directly from the REST API.
The deliverables
- Public GitHub repo — clear README, setup instructions, license, working code. Anyone with Docker should be able to
docker compose upand see it run. - 5-minute video — walk through the architecture: what each component does, why you made the design choices you did, what would change at 100× scale.
- Detection log on a real launch — sample output JSON showing your pipeline correctly identified one known launch event.
Why 5 minutes
The video constraint is deliberate. Five minutes is enough to explain the architecture and the key design decisions; it's not enough to dwell on every detail. The skill is communication under constraint — a skill every senior engineer needs.
What comes next
You've completed the LaunchDetect Academy. What's the next problem in space GIS? The honest answer: many. A non-exhaustive list:
- Orbital traffic management — as the LEO catalog grows past 100,000 objects, conjunction analysis at scale is unsolved.
- Climate monitoring from GEO — using GOES-R for sub-daily climate variables, not just weather.
- Autonomous Earth observation — onboard ML deciding what to image, when, with what bands.
- Open data infrastructure — keeping STAC catalogs and COG/Zarr archives sustainable as data volumes 10×.
- SAR for climate — InSAR-derived deformation as a climate-change indicator (subsidence, sea-level rise impacts).
- Mars and lunar GIS — coordinate systems, datums, basemap layers for off-Earth bodies.
The instinct you've built — "this is a spatial problem; I know how to set it up rigorously, run it, serve it, verify it" — is the most portable thing in the curriculum. Apply it everywhere.
The capstone
Week 30 is the start of Capstone 5: End-to-End Detection Pipeline, the final credential. The full rubric is on the capstone page; finishing it earns the Certified Space GIS Architect credential — the highest LaunchDetect Academy designation, and a real signal to peers and employers that you can build production-grade space-domain geospatial systems end-to-end.
Ship it. Tag us when you do.
Connecting to Hawaiʻi: What you carry forward
You started Week 1 with a place-based question: how do your kupuna give directions? You've finished with the tools to detect rocket plumes from geostationary thermal imagery. Both are forms of knowing where you are. Both came from people who knew the sky. The course was built by LaunchDetect — a production space-GIS company — but the curriculum was always for you. Whatever you build next, build it with the kuleana (responsibility) the place you come from has given you. That's not extra; that's the work.
Hands-on lab: End-to-End Detection Pipeline (capstone start)
Ingest 10 frames of GOES-18 Band 7 from S3. Georeference. Threshold-detect hotspots. Cluster across frames. Score. Persist to PostGIS. Expose /detections REST endpoint. Render on Cesium globe. Public GitHub repo + 5-min video. This is the deliverable for Capstone 5.
Quiz — click an answer to check it
No grade, no shame. Tap any option; you'll see if it's right plus the answer if not. The point is to notice what you already know and what's still settling.
- Ingest, processing, persistence, serving, visualization, monitoring
- Just processing
- Just visualization
- Just storage
- Forces concise explanation of design decisions and architecture
- Required by spec
- Hard to make
- Easier than writing
- Throughput, error rates, latency, data quality (false positive rate, etc.)
- Just uptime
- Just disk space
- Nothing
- Independent verification + portfolio + reproducibility
- Marketing only
- Required by law
- Nothing
- Open-ended — emerging areas include orbit congestion, climate monitoring, autonomous decisions
- All solved
- Only debris
- Only Mars
Reflection
Take five minutes with this. Write your answer somewhere. Carry it into next week.