Slow manual asteroid detection — automated image-analysis pipeline

Executive Summary

Astrometric surveys, university observatories, and commercial space firms are drowning in image streams where faint moving objects still require slow, manual vetting; roughly 6,000 observatories and research institutions represent an addressable market of about $1.5B (at ~$250K ACV per organization). That human-heavy workflow leads to missed detections, slow follow-up, inconsistent classification, and high labor costs—especially for low signal-to-noise near-Earth objects and debris. The product would be an end-to-end automated image-analysis pipeline combining modern deep-learning detection, robust image differencing, tracklet linking, probabilistic scoring, and MPC-ready reporting, delivered as software, processing credits, and integration services. Operational targets would be to cut human vetting time by a factor of 5–10x for typical survey loads, extend sensitivity toward lower SNRs, and optimize cost with a hybrid edge/cloud processing model; the $250K ACV bundle covers deployment, validation, and support. This is a timely opportunity: modern AI computer-vision models materially improve faint-object detection, small telescopes and surveys are proliferating (creating a data deluge), and planetary-defense funding increases willingness to pay for validated automation. To win, the product must prove low false-positive rates and reproducible sensitivity on real survey data, offer tight observatory integration and provenance for regulatory acceptance, and address clear challenges—building labeled training sets, passing MPC validation cycles, and managing compute and operational costs—even though direct competition today is relatively light.