This isn't a simulation. This is every AI detection from every flight over San Francisco — 700+ flights across five neighborhoods, from December 2021 to April 2024.
The AI model analyzed every photo — classifying what it saw as trash or not trash. Early on, the detection rate was low. The model was learning. By mid-2023, it was finding dumpsites in 40-50% of targeted flight photos.
Two things happened simultaneously: the AI model improved through training on real Bayview data, and the flight paths were refined to focus on confirmed dumping corridors. The combination — a smarter model flying smarter routes — is why the detection rate increased 15× over the program's lifetime.
Every detection has a GPS coordinate. 9,590 confirmed trash detections, each one placed on the map of Bayview-Hunters Point. The pattern is unmistakable — the same streets, the same corners, the same blocks.
The densest clusters aren't random. They correspond to specific infrastructure: dead-end streets, highway underpasses, vacant lots adjacent to commercial zones, and corners where DPW trucks have limited access. These aren't sites where people dump once — they're sites where people dump repeatedly, because the conditions that enable dumping persist. Detection identifies the pattern. Enforcement changes the conditions.
Detection without action is surveillance. Detection with action is infrastructure. Every confirmed dumpsite was automatically filed as a 311 cleanup request — no human intervention required.
Not every detection became a report. Only sites meeting three criteria: (1) large enough to warrant a crew dispatch, (2) not already reported in the prior 48 hours, and (3) on the public right-of-way, not touching buildings. Each report included GPS coordinates, timestamped aerial photo, and AI-classified waste type. Intelligent triage — not 311 spam.
Behind every data point is a flight. Some mornings, one quick pass. Some days, three separate flights — morning, midday, evening — covering different routes. The operational tempo tells its own story.
Every flight was planned, executed, and processed by a single operator. The drone launched from neighborhood streets. Photos were analyzed in real time. 311 reports were filed automatically — but only for sites that were large enough, not recently reported, and on the public right-of-way. No city department, no fleet of vehicles, no team of inspectors — one person with a drone and an AI model covered more ground than the city's entire enforcement apparatus.
This dataset is the proof. Not a demo, not a projection, not a pitch deck statistic. 298,800 photos. 700+ flights. 9,590 confirmed dumpsites. 4,801 cleanup requests — each one triaged for size, recency, and location before filing. All from five neighborhoods in one city.
Now imagine an entire city.