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

content/docs/d4c-pkgs/d4c-datapkg-field-imagery.md

@@ -1,5 +1,5 @@
 ---
-title: d4c-datapkg-field-imagery
+title: Field Imagery
 weight: 4
 next: /docs/d4c-infra-distribution/
 ---
@@ -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°, flat field imagery, and oblique imagery.
-
-* **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. 
+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.).

content/docs/d4c-pkgs/d4c-datapkg-foundation.md

@@ -1,5 +1,5 @@
 ---
-title: d4c-datapkg-foundation
+title: Foundation
 weight: 1
 ---
 

content/docs/d4c-pkgs/d4c-datapkg-orthoimagery.md

@@ -1,5 +1,5 @@
 ---
-title: d4c-datapkg-orthoimagery 
+title: Orthoimagery
 weight: 3
 prev: /docs/d4c-pkgs/d4c-datapkg-statistical/statistics_canada/census_data/
 next: /docs/d4c-pkgs/d4c-datapkg-field-imagery/

content/docs/d4c-pkgs/d4c-datapkg-statistical/_index.md

@@ -1,5 +1,5 @@
 ---
-title: d4c-datapkg-statistical
+title: Statistical
 weight: 2
 toc: true
 prev: /docs/d4c-pkgs/foundation/

high-level-overview-homepage.mermaid

@@ -9,9 +9,12 @@ flowchart TD
         Foundation@{ shape: lean-l}
         Orthoimagery@{ shape: lean-l}
         EnvironmentClimate@{ shape: lean-l, label: "Environment, Climate, & Health"}
+        style EnvironmentClimate fill:#FBC02D,stroke:#F9A825,color:#000000
         FieldImagery@{ shape: lean-l, label: "Field Imagery"}
-        Elevation@{ shape: lean-l}
+        style FieldImagery fill:#FBC02D,stroke:#F9A825,color:#000000
         WebCorpus@{ shape: lean-l, label: "Web Corpus"}
+        Elevation@{ shape: lean-l}
+        style WebCorpus fill:#66BB6A,stroke:#2E7D32,color:#000000
     end
 
     %% ---------------------------------------------------------
@@ -41,33 +44,46 @@ flowchart TD
         %% Box: Vector Tiles (Pastel Orange)
         subgraph vt [Vector Tiles]
             VectorTiles@{ shape: lean-l, label: "Mapbox Vector Tiles"}
+            style VectorTiles fill:#66BB6A,stroke:#2E7D32,color:#000000
             NextGenVectorTiles@{ shape: lean-l, label: "Next-Gen Vector Tiles"}
+            style NextGenVectorTiles fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
         end
 
         %% Box: Visuals (Pastel Blue - No Name)
         subgraph visuals [" "]
+            %% AVIF is not an option since QGIS does not seem to support it (I was surprised when my generated file was not compatible)
             WebP@{ shape: lean-l}
+            style WebP fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
             JPG@{ shape: lean-l}
+            style JPG fill:#66BB6A,stroke:#2E7D32,color:#000000
             PNG@{ shape: lean-l}
+            style PNG fill:#66BB6A,stroke:#2E7D32,color:#000000
         end
 
         %% Box: Portable Databases (Pastel Green)
         subgraph pkg [Portable Databases]
+            %% Good format, but has some issues. I encountered issues when merging multiple adjoining PMTile files, the author is aware of these issues
             PMTiles@{ shape: lean-l}
+            %% Compatability with every OS. 
+            %% Fun fact, you don't even need an OS for running SQLite 
+            %% https://sqlite.org/selfcontained.html
             SQLite@{ shape: lean-l}
         end
 
         %% Box: Enterprise (Pastel Purple)
         subgraph ent [Enterprise]
             FileGeodatabase@{shape: lean-l, label: "File Geodatabase"}
+            style FileGeodatabase fill:#fff,stroke:#2E7D32,color:#000000
         end
     end
 
     %% ---------------------------------------------------------
     %% 5. DISTRIBUTION INFRASTRUCTURE
     %% ---------------------------------------------------------
-    subgraph di [Distribution Infrastructure]
+    subgraph di [Distribution]
-        ObjectStorage@{ shape: bow-rect, label: "Object Storage"}
+        %% My goals involve going beyond object storage, so use generic term
+        ObjectStorage@{ shape: bow-rect, label: "Storage"}
+        style ObjectStorage fill:#FBC02D,stroke:#F9A825,color:#000000
         Metadata@{ shape: rect, label: "FAIR Data Catalogue"}
         HTTP@{ shape: rect, label: "Systems-Ready Data"}
         DecentralizedDistribution@{ shape: rect, label: "Decentralized Distribution"}
@@ -78,6 +94,8 @@ flowchart TD
     %% ---------------------------------------------------------
     subgraph ei [Experimental Infrastructure]
         GeoSpatialServices@{ shape: rect, label: "Geospatial Services"}
+        %% Does not make sense. Focus on delivering the data 
+        style GeoSpatialServices fill:#66BB6A,stroke:#2E7D32,color:#000000
     end
 
     %% ---------------------------------------------------------
@@ -178,6 +196,10 @@ style DecentralizedDistribution fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
 style HTTP fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
 style Systems fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
 style Metadata fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
+style Statistical fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
+style Foundation fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
+style Orthoimagery fill:#B71C1C,stroke:#7F0000,color:#FFFFFF
+style Elevation fill:#66BB6A,stroke:#2E7D32,color:#000000
 
 %%style df fill:#D32F2F,stroke:#8E0000,color:#FFFFFF
 style sot fill:#EF9A9A,stroke:#C62828,color:#000000
@@ -202,10 +224,9 @@ style visuals fill:#FBC02D,stroke:#F9A825,color:#000000
 
 style ent fill:#66BB6A,stroke:#2E7D32,color:#000000
 style DataSci fill:#D32F2F,stroke:#8E0000,color:#FFFFFF
-style GeoSpatialServices fill:#FFCC80,stroke:#FB8C00,color:#000000
 
 
-class Foundation,Statistical,Orthoimagery,FieldImagery,EnvironmentClimate,Elevation,WebCorpus linkNode
+class FieldImagery linkNode
 class Parquet,FlatGeoBuf,SQLite,FileGeodatabase,VectorTiles,NextGenVectorTiles,GeoTIFF,Zarr,WebP,PMTiles,JPEGXL,AV1,WARC linkNode
 
     %% =========================================================