\section{Discussion}
\label{sec:discussion}

\subsection{Foundation Model Limitations in Agricultural Boundary Detection}

SAM 3 achieves 95\% Building IoU but only 51\% Field IoU on identical imagery---a 44pp gap explained by boundary characteristics rather than model capacity.

% Buildings exhibit high-contrast edges matching SA-1B's training distribution. Agricultural parcels encode ownership relationships invisible to pixel-level analysis. Adjacent rice fields share spectral signatures and texture. Foundation models cannot recognize semantic boundaries defined by land tenure.

Buildings exhibit high-contrast edges matching SA-1B's training distribution. Agricultural parcels encode ownership invisible to pixel-level analysis. Adjacent fields share spectral signatures and texture. Foundation models cannot recognize semantic boundaries defined by tenure.

This asymmetry extends to data requirements. With 1,150 FIELD instances (44\%), domain-specific models learn robust representations foundation models cannot match. With 51 HOUSE instances (0.16\%), supervised learning fails while zero-shot succeeds.

\subsection{Domain Adaptation Outperforms Scale}

YOLOv11's +20.9pp improvement over DelineateAnything (92.9\% vs 72.0\% mAP@50) demonstrates 400 local annotated images outperform 22.9M global farm instances when resolution differs. DelineateAnything trained on 0.5-1.0 m/pixel satellite imagery; our 4.31 cm/pixel drone orthomosaics reveal detail invisible at coarser resolutions. Scale cannot substitute for domain alignment.


% \subsection{Practical Implications}

% For land validation in smallholder regions, hybrid pipelines are optimal. Domain-trained Mask2Former extracts field parcels at 97\% IoU with instance-level identification enabling per-parcel damage attribution. SAM 3 detects buildings at 95\% IoU without requiring scarce annotations. This achieves deployment-grade accuracy while minimizing annotation burden---annotate fields where foundation models fail, use zero-shot where foundation models excel.

% \subsection{Large-Scale Deployment Results}

% Figure~\ref{fig:large_scale_predictions} demonstrates deployment-grade performance on the held-out test region (9,089 $\times$ 7,297 pixels). Mask2Former panoptic extracts 2,646 parcel instances at 97\% Field IoU. The hybrid architecture combines trained model predictions with SAM 3 zero-shot building detection (95\% IoU), achieving high accuracy while minimizing annotation requirements.

% \subsection{Limitations}

% Several limitations constrain generalization. First, our dataset currently covers a single geographic region; although data from two additional project sites were collected during this deployment, their annotations are ongoing and will be incorporated in future validation. Second, Nepal spans highly heterogeneous terrain ranging from flat alluvial plains to steep hill and mountain regions, and the performance of the proposed models in complex hilly and terraced landscapes remains to be systematically evaluated. Third, the reported 97\% Field IoU for the Mask2Former panoptic formulation is derived from visual assessment; rigorous panoptic evaluation will require instance-level ground truth that is not yet available. Fourth, temporal analysis comparing pre-flood and post-flood conditions has not been explored. Finally, building detection currently relies on SAM-3 zero-shot inference; integration with fully trained models for unified end-to-end deployment will require additional data engineering.

\subsection{Deployment and Limitations}
Figure~\ref{fig:large_scale_predictions} demonstrates deployment-grade performance on the held-out test region (9,089 $\times$ 7,297 pixels). Our hybrid pipeline combines domain-trained Mask2Former (97\% Field IoU, 2,646 parcel instances) with SAM 3 zero-shot building detection (95\% IoU), achieving high accuracy while minimizing annotation burden.

Several limitations constrain generalization. Our dataset covers a single geographic region; annotations from two additional sites are ongoing. Performance on Nepal's heterogeneous hilly and terraced landscapes remains unevaluated. The reported 97\% Field IoU derives from visual assessment; rigorous panoptic evaluation requires instance-level ground truth not yet available. Temporal analysis comparing pre-flood and post-flood conditions has not been explored. Finally, building detection relies on SAM-3 zero-shot inference; integration with fully trained models for end-to-end deployment requires additional engineering.