# Golden Paths

Golden paths are protected workflows. Every major Codex pass must avoid breaking them.

## Golden Path 1 — Project Area Dataset Foundation

Goal: prove that the app can create a geospatial workspace.

Steps:

1. Create project.
2. Create an Area from GeoJSON.
3. Upload/register a vector dataset.
4. Extract metadata.
5. Persist dataset and geometry metadata.
6. Display project, area and dataset in frontend.

Pass criteria:

- project exists via API;
- area geometry validates;
- dataset reaches `READY` or honest failure state;
- frontend can show the state.

## Golden Path 2 — Reference Building QA

Goal: prove core GeoAI value without requiring real model inference yet.

Steps:

1. Load demo reference buildings.
2. Load demo predicted buildings.
3. Run matching algorithm.
4. Produce precision, recall, F1 and IoU summary.
5. Store QA run.
6. Export matched/unmatched features as GeoJSON.

Pass criteria:

- metrics are deterministic on fixtures;
- thresholds are configurable;
- false positives/false negatives are visible;
- export is valid GeoJSON.

## Golden Path 3 — Raster Metadata and Tiling Readiness

Goal: prove raster handling foundation.

Steps:

1. Register/upload GeoTIFF.
2. Extract metadata.
3. Validate CRS/transform.
4. Generate tile manifest without necessarily running AI.
5. Store tile parameters.

Pass criteria:

- metadata is persisted;
- tile count is deterministic;
- georeferencing is preserved;
- unsupported rasters fail honestly.

## Golden Path 4 — Detection Result Lifecycle

Goal: prove detection outputs can travel through the system.

Steps:

1. Create or import detection run.
2. Store detections with class/confidence/geometry.
3. Render detections as layer.
4. Run QA against reference.
5. Export detections.

Pass criteria:

- no frontend-only detections;
- every detection belongs to analysis run;
- geometries are valid;
- export works.

## Golden Path 5 — V1 Recruiter Demo

Goal: show portfolio value.

Narrative:

> GeoIntel loads a Kempen area, compares predicted building detections with reference building polygons, reports QA metrics, shows spatial errors and exports usable GIS output.

Minimum demo artifacts:

- map layer with reference buildings;
- map layer with predicted detections;
- QA metrics panel;
- unmatched features layer;
- GeoJSON export;
- concise explanation of methods and limitations.