\section{Experiments and Results}
\label{sec:experiments}

\subsection{Experimental Setup}

\textbf{Dataset.} 500 annotated tiles (400 training, 100 validation) at 1024$\times$1024 covering 10 km$^2$. FIELD (1,150 instances, 44.28\% of pixels) and VEGETATION (726 instances, 41.83\% of pixels) dominate; HOUSE contains only 51 instances (0.16\%). A held-out test set of 47 tiles evaluates generalization.

\textbf{Training Configuration.} All models trained on NVIDIA A100 with mixed precision. Semantic models use 512$\times$512 input, 50 epochs, batch size 16. YOLOv11m-seg uses 1024$\times$1024 for 100 epochs. Mask2Former trains at 1024$\times$1024 for 50 epochs with differential learning rates and custom boundary loss. Foundation models operate in zero-shot mode with 4,096 prompts per tile.

\textbf{Evaluation Metrics.} We report per-class IoU, mean IoU, Field IoU, Building IoU, and mAP@50.

\subsection{Main Results}

Table~\ref{tab:model_comparison} presents comprehensive results across foundation models, semantic segmentation, instance segmentation, and our panoptic formulation.

\textbf{Foundation Models Fail on Fields Despite Massive Scale.} SAM (636M parameters), SAM 2.1 (224M), and SAM 3 (848M) achieve only 35-51\% Field IoU. These models systematically merge adjacent parcels sharing identical visual features---two rice fields at the same growth stage appear as one continuous region to models trained on SA-1B's high-contrast boundaries. Remote sensing adaptations (RingMo-SAM, SAMRS) perform similarly (44-47\% Field IoU), demonstrating pre-training domain alone does not overcome semantic boundary limitations.

\textbf{Task-Dependent Performance Reveals Data Scarcity vs. Boundary Type.} Foundation models excel at building detection: SAM 3 achieves 95\% Building IoU despite houses representing 0.16\% of pixels (51 instances). Buildings exhibit high-contrast edges matching SA-1B's training distribution. Conversely, trained models achieve 95.37\% Field IoU (UNet MIT-B4) but only 57-71\% Building IoU due to insufficient examples. This motivates a \textbf{hybrid architecture}: trained models for fields (1,150 instances), SAM 3 for buildings (51 instances, zero-shot better).

\textbf{Domain Adaptation Outperforms Massive Pre-Training.} DelineateAnything, pre-trained on 22.9M farm instances at 0.5-1.0 m/pixel, achieves 72.0\% mAP@50 zero-shot on our 4.31 cm/pixel imagery. YOLOv11m-seg, trained on 400 images, reaches 92.9\% mAP@50 (+20.9pp). Resolution mismatch and boundary characteristics cannot be overcome by scale alone.


\textbf{Panoptic Formulation Achieves Best Overall Performance.} Mask2Former panoptic treats FIELD as instance-level ``things'' while landscape context remains semantic ``stuff.'' This achieves 97\% Field IoU and 86.5\% mean IoU---outperforming the best semantic model (SegFormer: 84.77\%) by +1.7pp and enabling extraction of 2,646 individual parcel instances.



\subsection{Per-Class Performance Analysis}

Table~\ref{tab:per_class_iou} presents per-class IoU for top paradigm models.

\textbf{Class Imbalance Effects.} HOUSE achieves only 57-71\% IoU with trained semantic models despite 50 epochs. With 51 instances (0.16\%), insufficient supervision prevents robust building representations. Mask2Former instance achieves 95.32\% Building IoU but catastrophically fails on BACKGROUND (0.38\% IoU) due to class weight miscalibration, demonstrating trained models cannot handle minority classes with $<$0.16\% representation.

\textbf{Model Specialization Trade-offs.} UNet MIT-B4 achieves highest Field IoU (95.37\%) with 97.36\% precision, optimal for field applications. However, SegFormer MIT-B2 achieves superior mIoU (84.77\% vs 83.98\%) via class balance: 67.95\% Background IoU (+10.07pp) and 92.04\% Road IoU (+14.18pp). With 58\% fewer parameters (27M vs 64M), SegFormer provides better accuracy-efficiency for multi-class deployment.

\textbf{Panoptic Advantage.} Mask2Former panoptic achieves highest performance across all classes: 97\% Field IoU (+1.63pp over UNet), 90\% Road IoU, 93\% Water IoU, 97.5\% Sand IoU, 94\% Vegetation IoU. By treating FIELD as instance-level ``things'' and context as semantic ``stuff,'' instance queries capture parcel boundaries while semantic queries capture landscape categories. Native 1024$\times$1024 resolution \& boundary-aware loss improve edge precision.

\begin{table}[t]
\centering
\caption{Per-Class IoU Performance Matrix (7-Class Models)}
\label{tab:per_class_iou}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l r r r r r r r r}
\toprule
Model & BG & Field & House & Road & Water & Sand & Veg & Mean \\
\midrule
UNet MIT-B4 & 57.88 & \textbf{95.37} & 70.82 & 77.86 & 94.14 & 96.85 & 94.97 & 83.98 \\
SegFormer MIT-B2 & \textbf{67.95} & 93.47 & 57.27 & \textbf{92.04} & 91.72 & 96.99 & 93.94 & 84.77 \\
Mask2Former Instance & 0.38$^\dagger$ & 85.61 & \textbf{95.32} & 77.45 & 92.58 & 96.45 & 82.91 & 78.50 \\
\midrule
\textbf{Mask2Former Panoptic} & \textbf{55.0} & \textbf{97.0} & 79.0 & \textbf{90.0} & \textbf{93.0} & \textbf{97.5} & \textbf{94.0} & \textbf{86.5} \\
\bottomrule
\end{tabular}%
}
\\[0.5em]
\raggedright
\footnotesize{$^\dagger$Mask2Former Instance suffers catastrophic Background failure (0.38\% IoU) due to class imbalance.}
\end{table}

\subsection{Qualitative Comparison}

Figure~\ref{fig:model_comparison} presents qualitative comparison across seven representative test tiles: dense smallholder parcels (tiles 1-3), sparse large fields (tiles 4-5), mixed land use (tile 6), and boundary ambiguity cases (tile 7).

\textbf{Semantic vs Instance Trade-offs.} SegFormer produces clean semantic masks but cannot distinguish parcel instances. YOLOv11m-seg separates parcels with distinct instance IDs, enabling per-parcel damage assessment, but occasionally misses parcels $<$100 pixels.


\textbf{Consistent Performance Across Scenarios.} Both models perform well across diverse tiles. Dense smallholder regions (8-12 parcels/tile) and large fields are correctly segmented. Learned representations outperform foundation models' geometric heuristics.

\textbf{Failure Modes.} Common failures: (1) vegetation bleeding into field boundaries, (2) road-adjacent parcels merged, (3) small irregular parcels missed, (4) boundary uncertainty when adjacent fields contain identical crops. Despite these, our models achieve deployment-grade accuracy (92.9-97\%) versus foundation model failures (35-51\%).

\begin{figure*}[t]
  \centering
  \includegraphics[width=\linewidth]{figures/fddcdscds.pdf}
  \caption{Qualitative comparison on seven test tiles. Input, ground truth, SegFormer MIT-B2 (84.77\% mIoU, 93.47\% Field IoU), YOLOv11m-seg (92.9\% mAP@50). Tiles: (1-3) dense parcels, (4-5) large fields, (6) mixed land use, (7) boundary ambiguity.}
  \label{fig:model_comparison}
\end{figure*}

\subsection{Hybrid Architecture Analysis}

The performance gap reveals a critical design principle: \textbf{match model selection to data availability per class}.

\textbf{When Trained Models Excel.} FIELD class with 1,150 instances (44.28\%) provides abundant supervision. Trained models learn semantic boundary concepts: UNet MIT-B4 achieves 95.37\% Field IoU with 97.36\% precision. Foundation models fail (SAM: 35\%, SAM 3: 51\%) because they operate on geometric heuristics.

\textbf{When Foundation Models Excel.} HOUSE class with 51 instances (0.16\%) provides insufficient supervision. Trained models achieve only 57-71\% Building IoU, while SAM 3 achieves 95\% zero-shot. Buildings exhibit high-contrast edges matching SA-1B's distribution. The 24-38pp gap justifies using foundation models for minority classes.


\begin{table}[t]
\centering
\caption{Ablation studies: (Top) Dataset size. (Bottom) Boundary loss.}
\label{tab:ablations}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l c c c c c c c c}
\toprule
\multicolumn{9}{c}{\textit{Dataset Size Ablation}} \\
\midrule
Train & \multicolumn{2}{c}{UNet B4} & \multicolumn{2}{c}{SegFormer B2} & \multicolumn{2}{c}{M2F Instance} & \multicolumn{2}{c}{M2F Panoptic} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9}
Images & Field & mIoU & Field & mIoU & Field & mIoU & Field & mIoU \\
\midrule
100 & 47.06 & 37.23 & 65.78 & 57.18 & -- & 60.60 & 94.76 & 84.5 \\
200 & 63.82 & 51.61 & 74.01 & 65.27 & -- & 75.11 & 95.51 & 85.2 \\
300 & 73.34 & 61.79 & 87.78 & 77.00 & -- & 77.75 & 96.87 & 86.4 \\
400 & 95.37 & 83.98 & 93.47 & 84.77 & 85.61 & 78.50 & \textbf{97.00} & \textbf{86.5} \\
\midrule
\multicolumn{9}{c}{\textit{Boundary Loss Ablation}} \\
\midrule
$\lambda$ & \multicolumn{2}{c}{Field IoU} & \multicolumn{2}{c}{mIoU} & \multicolumn{4}{c}{Improvement (Field / mIoU)} \\
\midrule
0.0 & \multicolumn{2}{c}{95.48} & \multicolumn{2}{c}{85.53} & \multicolumn{4}{c}{baseline} \\
\textbf{0.5} & \multicolumn{2}{c}{\textbf{97.00}} & \multicolumn{2}{c}{\textbf{86.5}} & \multicolumn{4}{c}{\textbf{+1.52 / +0.97}} \\
1.0 & \multicolumn{2}{c}{96.78} & \multicolumn{2}{c}{86.38} & \multicolumn{4}{c}{+1.30 / +0.85} \\
\bottomrule
\end{tabular}%
}
\end{table}

\textbf{Hybrid Deployment Strategy.} For production systems:
\begin{itemize}
\item \textbf{Field extraction}: Mask2Former panoptic (97\% Field IoU, 2,646 instances) or YOLOv11m-seg (92.9\% mAP@50, faster)
\item \textbf{Building detection}: SAM 3 zero-shot (95\% Building IoU) or GroundingDINO+SAM (93\% IoU)
\item \textbf{Context segmentation}: SegFormer MIT-B2 (84.77\% mIoU) for roads, water, vegetation
\end{itemize}

This achieves $>$90\% accuracy across all classes while minimizing annotation burden---train on abundant classes, use zero-shot on rare classes.

