Deflock: Open-Source Mapping of Automated License Plate Readers

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Deflock: Open-Source Mapping of Automated License Plate Readers · Adafruit · 2026-06-10

Deflock is an open-source project designed to map the locations of Automated License Plate Readers (ALPRs or LPRs). These AI-powered cameras capture and analyze images of all passing vehicles, storing location and timestamp data to enable widespread vehicle tracking. The project aims to increase transparency and public awareness regarding the physical footprint and proliferation of this surveillance infrastructure.

Context

Automated License Plate Readers represent a significant expansion of passive, AI-driven surveillance networks. Unlike targeted investigations, ALPRs operate continuously, aggregating massive datasets of civilian movement patterns over time. Open-source mapping initiatives like Deflock emerge as a civic countermeasure, leveraging community-driven data collection to expose the physical deployment of these systems, which are frequently installed by private entities or municipalities with minimal public oversight, regulatory constraint, or accessible audit mechanisms.

Relevance

This project aligns with the broader infrastructural imperative for transparency and civic resilience against opaque surveillance systems. By making the locations of ALPRs visible and queryable, Deflock empowers communities, journalists, and privacy advocates to audit the deployment of these technologies. It bridges the critical gap between hidden physical infrastructure and public accountability, treating surveillance mapping as a foundational layer for informed civic discourse.

Current State

Deflock operates as an open-source mapping initiative, aggregating user-submitted reports, public records, and crowdsourced data to chart ALPR deployments. The project provides a foundational, evolving dataset for understanding the geographic distribution of vehicle tracking networks. Comprehensive coverage remains dependent on continued community participation, data verification processes, and the development of standardized schemas for reporting hardware locations.

Open Questions

• How can community-sourced ALPR mapping scale effectively to match the rapid, often covert deployment of these systems by private and municipal entities?
• What legal, technical, or operational pushback might arise from systematically mapping and publishing the locations of active surveillance infrastructure?
• How can this geospatial data be integrated with broader civic technology tooling to enable automated alerts, policy advocacy, or localized privacy impact assessments?

Connections

• Relates to CellGuard (cellguard) in its shared objective of exposing and auditing hidden surveillance infrastructure, shifting the analytical focus from cellular baseband monitoring to physical, AI-powered optical tracking networks.