AI lunar surface mapping and an accessible air-taxi — the strongest capstone in 15 years.
NASA NCAS
Machine Learning
Remote Sensing
Aerospace
Accessibility
//FIG.15 — stratify
NASA NCAS Scholar work: an AI-enabled lunar surface-mapping concept and an accessible air-taxi design, cited as the strongest capstone in the program’s 15-year history. Continuing in a NASA think tank applying ML to lunar mapping.
//Role
NCAS Scholar
//Status
NASA think tank — ongoing
//Access
Live / public
//Problem
Lunar surface mapping and accessible aerial mobility are hard, high-stakes design problems. The NCAS Mission III capstone asked for a concept that could carry real engineering weight.
//Architecture
An AI-enabled lunar surface-mapping concept paired with an accessible air-taxi design — work recognized as the strongest capstone in the program’s fifteen-year history.
//Role
NASA NCAS Scholar — the lunar-mapping and air-taxi concepts, now continued in a NASA think tank.
//Outcome
Cited as the strongest capstone in 15 years; ongoing in a NASA think tank applying ML to lunar surface mapping.
What it took
Technical proof.
▸Designed an AI-enabled lunar surface-mapping concept.
▸Designed an accessible air-taxi concept.
▸Cited as the strongest capstone in the program’s 15-year history.
▸Continuing in a NASA think tank applying ML to lunar surface mapping.
//Private system — no live link. Architecture and status only; never data, positions, or credentials.←Back to all work