Projects / NASA International Space Apps Challenge 2025

Award winnerGalactic ImpactConfidence: high

Astro Sweepers

Orbital debris compliance and risk intelligence: automated Debris Assessment Software runs, ready-to-file disposal reports and a risk index for every object.

Original submission ↗Open demo ↗Watch video ↗Use as a reference →
Project facts
Track / challenge
Commercializing Low Earth Orbit (LEO)
Team
6 people
Build
software only
AI
None stated

Problem

Commercial activity in low Earth orbit multiplies debris risk and regulatory paperwork; assessing each object and producing compliance reports takes operators weeks.

Solution

A platform that ingests public orbital data (SPARK 2022, Space-Track TLEs and conjunction messages, ESA DISCOS), scores every object with a weighted risk index (debris legacy, compliance, collision-risk burden, sustainability), computes the Δv needed to lower perigee, re-runs NASA's Debris Assessment Software to show compliance before and after, and auto-fills disposal and end-of-mission plans — paired with a tiered SaaS and licensing business model and long-term debris population forecasts.

Demo moment

Not described in the sources.

Why it stood out

  • organizer statedWon the award for the most potential to improve life on Earth or in the universe.Source ↗
  • organizer statedAddresses the operational, regulatory and environmental sides of commercial spaceflight, not only orbital physics.Source ↗
  • team statedA worked case study makes the value concrete: a single ~22 m/s burn moves an 11-year-old rocket body from failing to compliant, with before-and-after assessment numbers and an auto-generated disposal plan.Source ↗
  • team statedAnswers the challenge's business requirement in full: pricing tiers, operator and insurer licenses, and a multi-year financial model.Source ↗
  • curator inferenceSpeaks the industry's compliance language, so the output is something an operator would file rather than a visualization.

Lessons for your next hackathon

  • Produce the artifact the real user must deliver (a compliance report), not only a dashboard.
  • Carry one worked, numeric case study from problem to fix; it proves the whole system in a minute.
  • When the challenge asks for a business model, bring pricing and a financial model, not a slide title.

Similar situations

Projects that share domains, mechanisms or judging lenses — often with a different problem and stack.Tune the comparison.

Grand winner #1 · 2026

Centinela

Autonomous agent that watches Colombian public procurement and issues opportunities and red flags with cited evidence.

Shared: domains due diligence risk · mechanisms dual use value, takes action · lenses impact, business potential

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Gaia+LEO

Optimization model that decides when and where orbital data centers beat terrestrial ones for AI and Earth-observation compute.

Shared: domains space operations · mechanisms visible engineering depth · lenses technical depth, impact

QUEÑARIS — video preview▶ Video

Award winner · 2025

QUEÑARIS

Smart reforestation of Arequipa's queñua forests against water scarcity, combining NASA vegetation indices, drone mapping, AI and native microorganisms.

Shared: mechanisms takes action, visible engineering depth · lenses impact, challenge relevance · stack vercel

PureFlow — video preview▶ Video

Award winner · 2025

PureFlow

A habitat layout tool that doubles as a survival simulator: model in 3D, compute crew needs live, and test the design against real solar-storm alerts.

Shared: domains space operations · mechanisms visible engineering depth · lenses challenge relevance

Sources

  1. https://www.nasa.gov/learning-resources/stem-engagement-at-nasa/nasa-announces-2025-international-space-apps-challenge-global-winners/ · official results · retrieved 2026-09-14
  2. https://www.spaceappschallenge.org/2025/awards/ · official event page · retrieved 2026-09-14 — Award definition.
  3. https://www.spaceappschallenge.org/2025/find-a-team/astro-sweepers-we-catch-what-space-leaves-behind/?tab=project · project page · retrieved 2026-09-14 — Team-written description, risk model, case study, business model, AI-use note and data.

Curator note. The team page mentions an exploratory CNN that was not part of the submission, so no AI pattern is recorded.