Hey all. Thanks!

So,

I need to build an automated pipeline that takes a specific Latitude/Longitude and determines:

1. **Detection:** If solar panels are present on the roof.
2. **Quantification:** Accurately estimate the **total area ($m\^2$)** and **capacity ($kW$)**.
3. **Verification:** Generate a visual audit trail (overlay image) and reason codes.

**2. What I Have (The Inputs)**

* **Data:** A Roboflow dataset containing satellite tiles with **Bounding Box annotations** (Object Detection format, *not* semantic segmentation masks).
* **Input Trigger:** A stream of Lat/Long coordinates.
* **Hardware:** Local Laptop (i7-12650H, RTX 4050 6GB) + Google Colab (T4 GPU).

3. Expected Output (The Deliverables)

Per site, I must output a strict JSON record.

* **Key Fields:**
   * `has_solar`: (Boolean)
   * `confidence`: (Float 0-1)
   * `panel_count_Est`: (Integer)
   * `pv_area_sqm_est`: (Float) <--- *The critical metric*
   * `capacity_kw_est`: (Float)
   * `qc_notes`: (List of strings, e.g., "clear roof view")
* **Visual Artifact:** An image overlay showing the detected panels with confidence scores.

4. The Challenge & Scoring

The final solution is scored on a weighted rubric:

* **40% Detection Accuracy:** F1 Score (Must minimize False Positives).
* **20% Quantification Quality:** MAE (Mean Absolute Error) for Area. *This is tricky because I only have Bounding Box training data, but I need precise area calculations.*
* **20% Robustness:** Must handle shadows, diverse roof types, and look-alikes.
* **20% Code/Docs:** Usability and auditability.

5. My Proposed Approach (Feedback Wanted)

Since I have Bounding Box data but need precise area:

* **Step 1:** Train **YOLOv8** (Medium) on the Roboflow dataset for detection.
* **Step 2:** Pass detected boxes to **SAM (Segment Anything Model)** to generate tight segmentation masks (polygons) to remove non-solar pixels (gutters, roof edges).
* **Step 3:** Calculate area using geospatial GSD (Ground Sample Distance) based on the SAM pixel count.

Thanks again! 🙂

Training an AI model. The problem is a bit lengthy for the title pls read description..