\vspace{-10pt}
\section{Conclusion}
\label{sec:conclusion}
\vspace{-5pt}
We presented systematic evaluation of segmentation approaches for agricultural parcel delineation from ultra-high-resolution drone imagery, demonstrating that foundation models fail on subtle semantic boundaries while domain-specific training succeeds. SAM-family models achieved only 35-51\% Field IoU; DelineateAnything achieved 72\% mAP@50 despite 22.9M instance pre-training. Domain adaptation on 400 tiles yielded dramatic improvements: YOLOv11 achieved 92.9\% mAP@50 (+20.9pp), and our panoptic formulation achieved 97\% Field IoU while extracting 2,646 parcels from 10 km$^2$ of flood-affected farmland.

Our hybrid approach---domain-trained Mask2Former for fields, SAM 3 for buildings---achieves deployment-grade accuracy (97\% Field IoU, 95\% Building IoU). The THING/STUFF formulation treating fields as instances while landscape context remains semantic proves effective for parcel-level identification.

% Future work will extend annotations for cross-region validation, create instance-level ground truth for rigorous panoptic evaluation, and explore temporal change detection for flood damage assessment. We will release annotated datasets and trained models to support humanitarian applications.

Future work will extend annotations for cross-region validation, create instance-level ground truth for panoptic evaluation, and explore temporal change detection for flood damage assessment. We will release annotated datasets and trained models to support humanitarian applications.

\section*{Acknowledgements}
\vspace{-5pt}
% This research was conducted under a project funded by the Food and Agriculture Organization of the United Nations (FAO) in support of post-disaster agricultural assessment in Nepal. UAV and drone imagery support was provided by NAXA.

This research was funded by the Food and Agriculture Organization of the United Nations (FAO) for post-disaster agricultural assessment in Nepal, with UAV and drone imagery support from NAXA. This work was also partially supported by ONR Grant \#N00014-23-1-2119, U.S. Army Grants \#W911NF2120076 and \#W911NF2410367, NSF CAREER Award \#1750936, NSF CNS EAGER Grant \#2233879, and NSF IIS Grant \#2509680.
