\section{Introduction}
\label{sec:intro}

In rural Nepal, smallholder farmers require accurate parcel delineation for land ownership verification, flood damage assessment, crop insurance, and government subsidies. These applications demand instance-level identification---a misidentified boundary can deny flood relief. However, over 80\% of South Asian farms are plots under 0.5 hectares, separated by subtle 30 cm earthen bunds that appear as faint textural changes in aerial imagery. At 4.31 cm/pixel drone resolution, adjacent rice fields exhibit identical visual features while representing distinct ownership units. This creates a fundamental challenge: parcel boundaries encode land tenure relationships rather than visual discontinuities. When adjacent fields contain identical crops, no visual feature distinguishes the ownership boundary between them.

\begin{figure}[t]
  \centering
  \includegraphics[width=\columnwidth]{figures/gff.pdf}
  \caption{Study area in Saptari district, Nepal. (a) Nepal overview. (b) Study area (10 km$^2$, 4.31 cm/pixel) along Koshi River.}
  \label{fig:study_area}
\end{figure}

Foundation models promise zero-shot deployment without local annotation, making them attractive for resource-constrained regions. This work (Figure~\ref{fig:study_area}) is grounded in a real-world United Nations--supported prototype development project conducted from August to December 2025 during the monsoon and post-monsoon period in a flood-vulnerable city of Nepal’s Terai region, implemented through a multidisciplinary, multinational academic--industry partnership, where parcel-level land validation is required for post-flood damage assessment and compensation. Models trained on datasets emphasizing high-contrast physical boundaries (e.g., SA-1B, COCO) may therefore struggle with agricultural boundaries defined by ownership rather than visual salience. We systematically evaluate whether foundation models and classical approaches achieve deployment-grade accuracy using ultra-high-resolution UAV orthomosaics covering 10~km$^2$ of Nepal’s Terai floodplain.


Our evaluation reveals fundamental limitations of zero-shot approaches for semantic boundary detection~\cite{tripathy2024sam}. Foundation models (SAM family) achieve only 35--51\% Field IoU—substantially lower than the 58\% reported on related smallholder datasets~\cite{tripathy2024sam}—despite successful zero-shot building detection at 80--95\% on identical imagery. This performance asymmetry arises because buildings exhibit high-contrast edges aligned with SA-1B training distributions, whereas agricultural parcels encode tenure-driven semantic boundaries requiring domain-specific understanding. Even \textit{DelineateAnything}, pre-trained on 22.9M global farm instances, achieves only 72\% mAP@50. Extensive post-processing pipelines (SAM2 with 4,096 prompts per tile, watershed segmentation, morphological operations, and geometric filtering) cannot bridge this gap.


In contrast, domain-specific training on local data effectively learns semantic boundary concepts~\cite{chang2024foundation}. We annotated 500 tiles (400 training, 100 validation) with 15-class taxonomy collapsed to seven operational classes. This targeted supervision enables models to achieve deployment-grade accuracy: UNet with MIT-B4 reaches 95.37\% Field IoU, YOLOv11m-seg achieves 92.9\% mAP@50 (+20.9pp over DelineateAnything zero-shot), demonstrating that 400 local images outperform 22.9M global instances when resolution and boundary characteristics differ. Our panoptic formulation treats fields as instance-level ``things'' while landscape context remains semantic ``stuff,'' achieving 97\% Field IoU with Mask2Former and extracting 2,646 individual parcel instances for per-parcel damage attribution.

\textbf{Contributions:} (1) Evaluation showing classical CV, foundation models (SAM: 35-51\%; DelineateAnything: 72\%), and extensive post-processing cannot achieve deployment accuracy on semantic boundaries while achieving 80-95\% on physical boundaries, demonstrating geometric limitations. (2) Benchmark dataset of 500 annotated tiles covering 10 km$^2$ with 15-class taxonomy. (3) Panoptic formulation achieving 97\% Field IoU, extracting 2,646 instances. (4) Comparison across semantic (95.37\%), instance (92.9\%), and panoptic paradigms with documented failures.
(5) Validation through deployment during 2025 monsoon in Nepal's Koshi River basin, enabling flood compensation, land validation, and agricultural damage assessment beyond lab-scale R\&D.