📸 Field Imagery

The Mission

Commercial street-level imagery is often locked behind restrictive licenses, proprietary viewers, and paywalls, making it inaccessible to our target audience. We are building a sovereign, open-source alternative for Canada.

By self-hosting a Panoramax instance, we provide a decentralized platform where field imagery is treated as a public utility: fully downloadable, API-accessible, and privacy-compliant.

The Infrastructure

Our field imagery pipeline is built on the Panoramax ecosystem, a federated open-source alternative to Google Street View that guarantees data permanence and open access.

• Storage: High-performance object storage backend for hosting terabytes of 360° and flat field imagery.
• Federation: Our instance connects to the global Panoramax federation, ensuring that while the data is hosted in Canada, it is discoverable worldwide through the global panoramax catalog.

The Processing Pipeline

We treat field imagery as a data engineering challenge, ensuring “time-to-insight” is minimized for downstream users.

1. Ingestion: Raw imagery is captured using diverse hardware (ex. 360° cameras, mobile rigs, meta glasses, etc.) and ingested by our systems.
2. Privacy & Anonymization: Before publication, all imagery undergoes a rigorous privacy scrub. We utilize automated detection pipelines to blur faces and license plates, ensuring compliance with Canadian privacy standards while maintaining data utility.
3. Standardization: Images are processed into systems-ready formats, making it ready for analysis.
4. Metadata Extraction: We extract and normalize/strip identifiying information (ex. EXIF and GPS telemetry), indexing it into a FAIR Catalog.

Data Products

Unlike commercial platforms that only offer a “view” of the data, we provide the data itself.

• API Access: Full programmatic access via the Panoramax REST API for querying imagery by location, date, or sequence.
• Bulk Datasets: Curated dumps of street-level imagery available for computer vision training, asset management, and change detection models.
• FAIR Data Catalog Integration: Seamless integration with geospatial workflows (ex. DuckDB, QGIS, Python, R, Julia, etc.).