\section{Related Work}
\label{sec:related_work}

\subsection{Agricultural Field Boundary Detection}

Persello et al.~\cite{persello2019delineation} pioneered deep learning for smallholder field detection, demonstrating semantic boundaries require learned representations rather than geometric heuristics. Regional work confirms local training outperforms global pre-training: Yang et al.~\cite{yang2020bangladesh} emphasized curated local datasets for Bangladesh farms, while Wang et al.~\cite{wang2022india} achieved 0.85 median IoU on Indian fields through transfer learning with 5-10$\times$ less data. Large-scale datasets---AI4SmallFarms~\cite{persello2023ai4smallfarms} (439K polygons), Fields of The World~\cite{kerner2024fields} (1.63M polygons across 24 countries)---rely on 0.5-10 m/pixel satellite imagery, coarser than required for Nepal's 0.24 ha average parcels. Joshi et al.~\cite{joshi2023nepal} confirmed satellites prove ``inappropriate'' for Nepalese smallholdings, necessitating UAV imagery for 30cm earthen bunds.

\subsection{Foundation Models and Zero-Shot Approaches}

Foundation models promise zero-shot deployment but show task-dependent performance. Osco et al.~\cite{osco2023sam} found SAM struggles with slender farm boundaries; Lefebvre and Lalibert\'e~\cite{lefebvre2025tree} documented ``gridded'' artifacts on 4 cm/pixel drone imagery. Chen et al.~\cite{chen2024rsprompter} achieved state-of-the-art through learned prompts, but Moghimi et al.~\cite{moghimi2024sam} showed fine-tuned SAM requires substantial supervision to match U-Net. DelineateAnything~\cite{lavreniuk2025delineate}, pre-trained on 22.9M farm instances, achieves only 72\% mAP@50 on our 4.31 cm/pixel orthomosaics, demonstrating resolution mismatch limits transfer despite massive scale.

\subsection{Multi-Paradigm Segmentation Architectures}

Weyler et al.~\cite{weyler2024phenobench} benchmarked semantic, instance, and panoptic paradigms on 100-megapixel UAV sugar beet imagery, showing treating crops as ``things'' while background remains ``stuff'' improves boundary precision. Sprute et al.~\cite{sprute2025lulc} evaluated 29 models on 5 cm GSD imagery, achieving 91.1\% accuracy with CF-Net. Spasev et al.~\cite{spasev2024segformer} showed SegFormer maintains accuracy-efficiency trade-offs for UAV. Zhao et al.~\cite{zhao2025landunet} introduced edge-sensing attention for fuzzy rural boundaries; Dua et al.~\cite{dua2024india} scaled panoptic segmentation to 151M hectares in India.

Our work systematically compares foundation models, semantic, instance, and panoptic paradigms on identical ultra-high-resolution imagery, documenting zero-shot limitations while demonstrating domain-specific training with panoptic formulations achieves deployment-grade accuracy for land validation.
