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Update field_imagery.md

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Diego Ripley
2026-02-17 20:26:22 -05:00
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content/docs/processes/field_imagery.md
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@@ -24,7 +24,7 @@ We treat field imagery as a data engineering challenge, ensuring "time-to-insigh
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](https://stac-utils.github.io/stac-geoparquet/latest/spec/stac-geoparquet-spec/)**.
4. **Metadata Extraction**: We extract and normalize/strip identifiying information (ex. EXIF and GPS telemetry), indexing it into a **[FAIR Catalogue](https://stac-utils.github.io/stac-geoparquet/latest/spec/stac-geoparquet-spec/)**.
## Data Products
@@ -32,4 +32,4 @@ Unlike commercial platforms that only offer a "view" of the data, we provide the
* **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](https://stac-utils.github.io/stac-geoparquet/latest/spec/stac-geoparquet-spec/) Integration**: Seamless integration with geospatial workflows (ex. DuckDB, QGIS, Python, R, Julia, etc.).
* **[FAIR Data Catalogue](https://stac-utils.github.io/stac-geoparquet/latest/spec/stac-geoparquet-spec/) Integration**: Seamless integration with geospatial workflows (ex. DuckDB, QGIS, Python, R, Julia, etc.).