Fixes. Updated high-level overview diagram with priorities: red is highest, then there's orange, and then green. If a shade is darker, it means it is higher priority

2026-06-13 14:00:51 +02:00 · 2026-02-21 16:22:38 -05:00
parent db8a1f3b71
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6 changed files with 34 additions and 17 deletions
@@ -1,5 +1,5 @@
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-title: d4c-datapkg-field-imagery
+title: Field Imagery
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 next: /docs/d4c-infra-distribution/
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@@ -8,20 +8,17 @@ next: /docs/d4c-infra-distribution/

 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](https://panoramax.fr/)** instance, we provide a decentralized platform where field imagery is treated as a public utility: fully downloadable, API-accessible, and privacy-compliant.
+We will be using **[Panoramax](https://panoramax.fr/)** as a base, but will be expanding upon the project to provide a decentralized platform where field imagery is treated as a public utility: fully downloadable, API-accessible, privacy-compliant, and resilient to shifting priorities.

 ## 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.
+* **Storage**: High-performance object storage backend for hosting terabytes of 360°, flat field imagery, and oblique imagery.

 ## 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. 
+1. **Ingestion**: Raw imagery is captured using diverse hardware (ex. 360° cameras, mobile rigs, meta glasses, drones, 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 Catalogue](https://stac-utils.github.io/stac-geoparquet/latest/spec/stac-geoparquet-spec/)**.
@@ -30,6 +27,5 @@ We treat field imagery as a data engineering challenge, ensuring "time-to-insigh

 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 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.).
@@ -1,5 +1,5 @@
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-title: d4c-datapkg-foundation
+title: Foundation
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@@ -1,5 +1,5 @@
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-title: d4c-datapkg-orthoimagery 
+title: Orthoimagery
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 prev: /docs/d4c-pkgs/d4c-datapkg-statistical/statistics_canada/census_data/
 next: /docs/d4c-pkgs/d4c-datapkg-field-imagery/
@@ -1,5 +1,5 @@
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-title: d4c-datapkg-statistical
+title: Statistical
 weight: 2
 toc: true
 prev: /docs/d4c-pkgs/foundation/