PolarWindow
PolarWindow is an independent lunar mission exploration project being developed for the NASA International Space Apps Challenge. It brings CLPS mission information, terrain-aware sunlight analysis, and Earth visibility into a visual planning interface. It is not a NASA product or an operational mission certification tool.
My contribution
My work focuses on research and mission-analysis inputs: wind checks, finer terrain for rover line of sight, and launch-window constraints from launch-pad direction limits. Supporting work involves SPENVIS and Copernicus Marine data access and ESA Lunar Pathfinder orbit documentation. These analysis areas are ongoing; frontend implementation is my next planned contribution.
The Problem
A promising lunar landing site is not defined by coordinates alone. Local terrain can block sunlight or Earth visibility, and a single combined score can hide long periods of darkness or lost contact. Comparing candidate sites requires the underlying assumptions and data sources to remain visible.
The Solution
Organize mission and landing-site exploration around separate sunlight, Earth-contact, longest-darkness, and longest-no-contact measures. Connect map and skyline views to source-grounded calculations, while clearly labeling saved data, fallbacks, and uncertainty.
System Architecture
A React and Three.js interface consumes typed FastAPI endpoints. Python geometry and ephemeris calculations are separated from API and application services, with PostgreSQL persistence and background workers for computation and data feeds. NASA LOLA terrain and JPL/NAIF SPICE data support the analysis; a labeled snapshot mode allows browsing a saved copy without the full backend.
Key Features
Innovative solutions developed to solve complex technical challenges.
Mission and site exploration
Browse CLPS deliveries and compare candidate landing locations through linked visual views.
Separate visibility measures
Inspect illumination and Earth contact independently, including the longest interruptions rather than only total available hours.
Terrain-aware skylines
Explore how the local horizon changes Sun and Earth visibility over a selected date range.
Traceable planning outputs
Inspect data provenance, saved-copy labels, and exportable planning reports and intervals.
Challenges & Breakthroughs
Challenge: Communicating the limits of a planning model
Growth: Expose source provenance and fallback assumptions alongside the visual result; illumination is not a thermal-survival or operational go/no-go guarantee.
Challenge: Keeping exploration separate from expensive calculations
Growth: Separate browser presentation, service orchestration, and pure geometry so computed results and saved snapshots have clear boundaries.
Next Project
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