\subsection{Ablation Studies}


\begin{figure*}[!t]
  \centering
  \includegraphics[width=\linewidth]{figures/fig7_large_scale_predictions.pdf}
  \caption{Large-scale deployment on test region (9,089 $\times$ 7,297 pixels, $\sim$12 ha). (Top) Ground truth: 2,646 parcels. (Middle) Mask2Former panoptic: 97\% Field IoU. (Bottom) Hybrid: trained model + SAM 3 (95\% Building IoU).}
  \label{fig:large_scale_predictions}
\end{figure*}

Table~\ref{tab:ablations} evaluates training data requirements and boundary loss contribution. Mask2Former Panoptic achieves 97\% Field IoU at 400 images, outperforming all alternatives across all data regimes. Custom boundary loss ($\lambda$=0.5) provides +1.52pp gain over baseline, applying morphological operations to generate boundary-only supervision for semantic parcel boundaries.
