Space GIS Architect

Production-grade space GIS. Multi-sensor satellite fusion across GOES-East / GOES-West / Himawari-9, ML object detection in raster, SAR interferometry, cloud-native COG/Zarr/STAC formats, end-to-end AWS pipelines, geospatial APIs with PostGIS + FastAPI, and the ethical / legal frontiers (ITAR, MGRS, sub-meter accuracy). Final capstone is a complete launch-detection mini-pipeline from raw NetCDF to served REST endpoint.

What you'll learn

• Fuse imagery from multiple geostationary satellites into a hemispheric coverage product.
• Train and deploy a CNN for object detection in raster imagery (U-Net for segmentation).
• Read SAR data and reason about polarimetry and InSAR phase.
• Apply EGM2008 geoid corrections for precise positioning.
• Build a complete S3 → Lambda → EventBridge → DDB ingest pipeline.
• Serve geospatial data via a PostGIS + FastAPI REST API with spatial filters.
• Reason about export-controlled (ITAR) and dual-use geospatial data.

Prerequisites

Mission GIS Engineer track or equivalent. Comfortable with cloud infrastructure, Python, and production systems.

Tools you'll use

xarray · PyTorch · rasterio · FastAPI · AWS CDK · STAC API

Weekly curriculum (10 weeks)

• Week 21 Multi-sensor fusion: GOES-East, GOES-West, Himawari-9
• Week 22 ML for satellite imagery: CNNs and U-Net segmentation
• Week 23 SAR: Sentinel-1, polarimetry, InSAR
• Week 24 Geodesy: ellipsoid vs geoid, EGM2008
• Week 25 AR for satellites: sky-direction overlays and az-el math
• Week 26 Cloud-native: COG, Zarr, STAC catalogs
• Week 27 Production pipelines: S3 → Lambda → EventBridge → DDB
• Week 28 Privacy + ethics: MGRS, sub-meter, ITAR
• Week 29 Geospatial APIs: PostGIS + FastAPI + spatial REST
• Week 30 Capstone defense and synthesis (Capstone 5 week) Capstone 5

Capstone 5: End-to-End Detection Pipeline

Raw NetCDF → georeferenced detections → PostGIS → REST → 3D globe.

Build a complete production-grade space-GIS pipeline: ingest 10 frames of GOES-18 ABI Band 7 NetCDF from the NOAA AWS Open Data bucket; georeference each frame (parallax-corrected); threshold-detect hotspots; cluster hotspot pixels across consecutive frames into plume tracks; score each track for confidence (geometric coherence, brightness consistency, motion); persist results to PostGIS with proper indexes; expose a FastAPI /detections REST endpoint with bbox, time-range, and confidence filters; render results live on a Cesium globe served from the same FastAPI app. Deliverable is a public GitHub repository + a 5-minute video walking through the pipeline.