In an era where NASA releases terabytes of astronomical data daily, extracting meaningful insights from the cosmic noise requires sophisticated analytical tools. CosmicWatch bridges this gap by creating a unified intelligence platform that transforms raw space observations into actionable scientific insights, bringing together exoplanet discovery data, near-Earth object tracking, and satellite-based Earth monitoring under one comprehensive dashboard.
The platform’s machine learning pipeline is where the real magic happens. It computes Earth Similarity Index (ESI) scores across 5,000+ confirmed exoplanets, applies KMeans clustering to identify potentially habitable worlds, and uses DBSCAN to flag astronomical anomalies that might represent new discovery opportunities. For planetary defense, the asteroid tracker combines JPL’s Close Approach and Sentry Risk APIs with Isolation Forest models to detect trajectory anomalies, while ARIMA forecasting predicts wildfire behavior from NASA satellite imagery. All of this is wrapped in an interactive Plotly Dash interface that makes complex astronomical data exploration feel intuitive.
What sets CosmicWatch apart is its practical approach to space science computing. Whether you’re a researcher hunting for the next Earth-like exoplanet, a planetary defense analyst tracking potentially hazardous asteroids, or a climate scientist monitoring global fire patterns, this platform provides the computational infrastructure to turn NASA’s open datasets into scientific discoveries. With its modular FastAPI backend and extensible ML pipeline architecture, CosmicWatch represents the future of collaborative space science—where powerful analysis tools are accessible to any researcher with Python skills and cosmic curiosity.
⭐ Stars: 12
💻 Language: Python
🔗 Repository: aymenhmaidiwastaken/cosmicwatch