Slow and ambiguous source loading
Terrain, buildings, vegetation and airspace could fail or arrive out of sequence without a dependable project-level truth state.
Product and engineering case study
SighThor began as an AI-generated idea for two hard planning problems: drone route visibility and terrain-aware camera coverage. The modernisation focused on calculation truth, explicit uncertainty, accountable review and a professional working experience.
The original product could draw useful-looking cones and routes, but data failures, inconsistent terrain contexts, weak report lineage and generic navigation made the result hard to trust or explain.
Terrain, buildings, vegetation and airspace could fail or arrive out of sequence without a dependable project-level truth state.
Camera, route, 3D and export paths could interpret a different scene or make hidden flat-world assumptions.
The public page advertised atmosphere while the app exposed dense options without a clear professional next action.
UK aviation requirements, terrain and LiDAR access, camera-design practice, source licensing and competitive workflows informed a phased architecture around frozen scenes and human-reviewed evidence.
The centered dark hero and passive project gallery are replaced by a paper survey brief, separate edition doors, frozen product proof, an operations register and persistent workflow readiness.
Five route points · two observers · 80 m AGL
Six cameras · 60,000 m² synthetic site · nominal pixel density
Wales Enhanced remains blocked pending authoritative datum evidence. Paid OSNI data remains disabled. Geometric VLOS and nominal DORI remain inputs to practitioner review, not automatic professional decisions.
Consulting capability
This work demonstrates research, domain modelling, data provenance, deterministic analysis, security, workflow design, report architecture, testing and deployment verification. A consultation can explore a similar product or a private deployment requirement without implying regulatory authority.