 | Hey guys, I've been workin on something new to track logistical activity near military bases and other hubs. The core problem is that Google maps isn't updated that frequently even with sub meter res and other map providers such as maxar are costly for osint analysts. But there's a solution. Drish detects moving vehicles on highways using Sentinel-2 satellite imagery. The trick is physics. Sentinel-2 captures its red, green, and blue bands about 1 second apart. Everything stationary looks normal. But a truck doing 80km/h shifts about 22 meters between those captures, which creates this very specific blue-green-red spectral smear across a few pixels. The tool finds those smears automatically, counts them, estimates speed and heading for each one, and builds volume trends over months. It runs locally as a FastAPl app with a full browser dashboard. All open source. Uses the trained random forest model from the Fisser et al 2022 paper in Remote Sensing of Environment, which is the peer reviewed science behind the detection method. GitHub: https://github.com/sparkyniner/DRISH-X-Satellite-powered-freight-intelligence- [link] [comments] |
Made a tool to gather logistical intelligence from satellite data
Reddit r/artificial / 4/15/2026
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Key Points
- The post introduces “Drish,” a locally running FastAPI-based tool that detects moving vehicles near military bases and logistics hubs using Sentinel-2 satellite imagery.
- It relies on the physics of Sentinel-2 band capture timing, where fast-moving trucks create a characteristic blue-green-red spectral smear across pixels.
- The system automatically finds those smears, estimates speed and heading, counts detected movements, and aggregates trends over months.
- The dashboard is browser-based and the project is described as fully open source, enabling OSINT analysts to avoid costly commercial imagery updates.
- The method uses a trained random-forest model credited to the peer-reviewed Fisser et al. (2022) research in Remote Sensing of Environment.
