Built an open source tool that can find precise coordinates of any picture
A college student's open-source AI pipeline can pinpoint exact GPS coordinates from a single street-level image.
A college student developer, known as Sparkyniner, has publicly released the source code for Netryx, a next-generation street-level geolocation tool. The system employs a custom machine learning and AI pipeline designed to analyze visual clues within a single photograph—such as architecture, signage, vegetation, and road layouts—to deduce and output precise latitude and longitude coordinates. The project's open-source release on GitHub makes this advanced capability, often associated with intelligence and investigative work, accessible to a broader developer community for experimentation and integration.
A demonstration video shows Netryx successfully geolocating video footage related to the Qatar strikes, proving its practical application in analyzing real-world media. The developer is actively seeking to connect with other developers and companies in the geospatial and AI sectors to foster collaboration. By open-sourcing the tool, Sparkyniner aims to accelerate innovation in automated geolocation, allowing others to build upon the custom ML pipeline for applications in journalism, open-source intelligence (OSINT), digital forensics, and augmented reality.
- Open-source AI tool 'Netryx' uses visual analysis to find exact GPS coordinates from street photos.
- Built by a solo college student developer using a custom machine learning pipeline.
- Proven in a demo geolocating footage of the Qatar strikes; code is now on GitHub.
Why It Matters
Democratizes advanced geolocation tech for OSINT, journalism, and forensics, moving it from proprietary silos to open development.