God's Eye View: One Globe for All Public Geospatial Data

Intelligence analysts tracking a humanitarian crisis typically have a dozen browser tabs open at once. One for FlightRadar24 to watch cargo flights. Another for MarineTraffic to monitor relief ships. A third for satellite passes. Separate windows for seismic activity, weather systems, and traffic cameras near ports. The data exists—all of it public—but the context doesn't emerge until you mentally overlay the patterns yourself.

God's Eye View consolidates those signals onto a single 3D globe: aircraft positions, vessel AIS tracks, satellite orbits, earthquakes, weather overlays, traffic cameras, and infrastructure, all in one interface. The project doesn't introduce new datasets. It unifies access to existing public feeds so spatial relationships become visible.

The dozen-tab problem

OSINT researchers and geospatial analysts work across fragmented tools because commercial services optimize for single domains. FlightRadar24 excels at aviation. MarineTraffic provides real-time AIS vessel data and satellite-based ocean coverage. Weather services layer atmospheric data. Each tool does its job, but correlation happens in the analyst's head, not on the screen.

Pattern recognition depends on spatial overlap. A cargo plane deviating from its route means more when you see it descending near a port where vessel traffic just spiked. Earthquake epicenters become actionable when traffic cameras near the affected zone go dark. Those connections don't surface when signals live in separate tabs.

What God's Eye View consolidates

The project layers multiple public data streams onto a 3D globe built with CesiumJS, an open-source JavaScript library for high-precision 3D visualization. Aircraft positions, ship tracks, satellite orbits, seismic events, weather patterns, traffic cameras, and infrastructure markers all appear in the same view.

Bilawal Sidhu and Sameh Khamis at Halfpixel maintain the project, which pulls real public data rather than simulated feeds. The interface handles time-dynamic visualization—you can scrub backward to see where a vessel was six hours ago while keeping satellite passes and weather systems synchronized to the same timestamp.

The gap between commercial trackers and enterprise GIS

Commercial tracking services provide deep functionality within their domains but don't cross-layer. Enterprise GIS platforms like ArcGIS handle multi-signal analysis but come with licensing costs and learning curves that keep them out of reach for independent researchers, newsrooms, or hobbyists.

God's Eye View sits in the middle: multi-signal correlation without enterprise budgets or server dependencies. The tool is useful for intelligence analysts correlating vessel movements with flight patterns, journalists tracking conflict zones or disaster response logistics, and technical hobbyists exploring how public infrastructure responds to weather events.

Local-first and open-source by design

The project was open-sourced in August 2024 under an MIT license, designed to run entirely in-browser with local compute. No server dependency means no data leaves your machine, which matters for OSINT practitioners who value operational security and transparency.

The tradeoff: running in a browser doesn't eliminate computational requirements, so smooth performance still depends on capable GPU hardware. Some early users reported installation issues through the official workflow on Windows and Arch Linux in some configurations, with Vite resolving paths through cross-drive junctions. Typical friction for a project growing this fast.

Reception and momentum

The repository hit number one on GitHub Trending in both daily and weekly rankings during August 2024. Adoption has been strongest among OSINT researchers and conflict journalists who need spatial context that commercial tools don't provide in aggregate.

The project respects the infrastructure it builds on. CesiumJS handles the globe rendering. Public APIs supply the data. God's Eye View assembled them into a workflow that treats geospatial intelligence as a correlation problem rather than a collection of isolated feeds. For analysts who've spent years mentally stitching together separate tracking tools, that consolidation fills a real gap.