Detect a real rocket plume from a real NOAA GOES NetCDF.
The brief
Build a Python tool that, given a GOES-18 ABI Band 7 NetCDF file and a known launch event (date, location, vehicle), outputs detection records `(timestamp_UTC, lat, lon, brightness_temp_K, area_km²)` for each detected plume pixel cluster. Apply parallax correction. Apply false-positive filtering (mask known wildfires from the FIRMS dataset). Produce a Folium heatmap visualization showing detected hotspots overlaid on a basemap, with hover-popups showing the (t, T_b) for each detection.
Rubric
Tool runs on the provided sample NetCDF and produces ≥1 valid plume detection record for the known launch
Detection timestamps are within 30 seconds of ignition time published by the launch operator
Coordinates are within 5 km of the known launch pad (after parallax correction)
Brightness temperatures are physically plausible (> 320 K for plume, > 290 K for background)
False-positive filtering rejects ≥90% of FIRMS-overlapping pixels in the same scene
Folium heatmap is interactive and correctly rendered
Deliverable
Python tool + Jupyter notebook walkthrough + Folium HTML output for the test event
Successful completion of this capstone (all rubric items met) mints the Certified Remote Sensing Specialist certificate at /academy/verify/{certId}/.
Submission. Certificate issuance is gated to LaunchDetect Gold ($9.99/month). Submit your capstone deliverable via the form at /academy/verify/ (coming soon — backend in v2). For now, build it, push to GitHub, and link it on your portfolio.