\section{Dataset and Annotation}
\label{sec:dataset}

\subsection{Study Area and Data Acquisition}

Our study area lies in Saptari district, Nepal---part of the Terai flood plain (Figure~\ref{fig:study_area}). This region exemplifies smallholder farming landscapes with densely subdivided parcels, seasonal flooding from the Koshi River, and earthen bund boundaries.

Drone surveys at 120 m altitude captured pre-flood and post-flood imagery. The orthomosaic spans 60,971 $\times$ 88,127 pixels at 4.31 cm ground sampling distance, covering 10 km$^2$. At this resolution, individual crop plants and 30cm earthen ridges become visible---detail satellite imagery cannot capture. The orthomosaic was georeferenced to WGS 84 / UTM zone 45N.


\subsection{Data Processing Pipeline}

We partitioned the orthomosaic into 1024$\times$1024 pixel tiles without overlap, exporting 8-bit PNG for neural network training. Tiles cover 44$\times$44 meters. Filtering tiles with $<$50\% valid content reduced 6,732 tiles to 3,425 clean tiles. We selected 500 tiles for dense annotation based on diverse land use.

\subsection{Annotation Taxonomy}

Land validation for insurance and disaster compensation requires \emph{parcel-level differentiation of field conditions}. For example, waterlogged parcels (\texttt{MARSH\_FIELD}) warrant different compensation than harvested land (\texttt{BARREN\_FIELD}) or actively cultivated fields (\texttt{GROWING\_FIELD}). To capture these distinctions, we developed a \textbf{15-class annotation taxonomy} reflecting real-world agricultural and flood-response needs.

\textbf{Original 15-Class Taxonomy.} 
Table~\ref{tab:class_mapping} illustrates the hierarchical structure of the taxonomy. It includes \textbf{four field subtypes}, \textbf{three vegetation classes}, \textbf{infrastructure classes} (\texttt{HOUSE}, \texttt{ROAD}, \texttt{BRIDGE}, \texttt{IRRIGATION\_CHANNEL}), and \textbf{context classes} (\texttt{WATER}, \texttt{SAND}, \texttt{GROUND}, \texttt{BACKGROUND}, \texttt{EDGECASE\_IGNORE}). \texttt{FIELD} and \texttt{VEGETATION} dominate the dataset, together accounting for approximately 85\% of labeled pixels, while classes such as \texttt{HOUSE}, \texttt{ROAD}, and \texttt{BACKGROUND} appear only as thin slivers, highlighting severe class imbalance.

\textbf{Collapsed 7-Class Training Taxonomy.} 
Training directly on the full 15-class taxonomy proved unstable due to extreme class imbalance---some categories contained fewer than ten labeled instances (e.g., \texttt{IRRIGATION\_CHANNEL}: 5, \texttt{EDGECASE\_IGNORE}: 7). To ensure robust learning, we collapsed semantically related categories into \textbf{seven training classes}: \texttt{BACKGROUND}, \texttt{FIELD} (all field subtypes merged), \texttt{HOUSE}, \texttt{ROAD}, \texttt{WATER}, \texttt{SAND}, and \texttt{VEGETATION}.


\begin{table}[t]
\centering
\caption{Class Mapping with Training Statistics}
\label{tab:class_mapping}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{llrrrr}
\toprule
Original Class & Final Class & Instances & Inst \% & Pixels & Pixel \% \\
\midrule
Growing Field & Field & 963 & 42.7\% & 190,095,729 & 37.5\% \\
Noncrop Field & Field & 115 & 5.1\% & 19,701,005 & 3.9\% \\
Marsh Field & Field & 55 & 2.4\% & 12,328,774 & 2.4\% \\
Barren Field & Field & 17 & 0.8\% & 2,062,537 & 0.4\% \\
\midrule
Vegetation & Vegetation & 526 & 23.3\% & 151,518,332 & 29.9\% \\
Marsh Vegetation & Vegetation & 106 & 4.7\% & 56,185,344 & 11.1\% \\
Trees & Vegetation & 94 & 4.2\% & 4,049,055 & 0.8\% \\
\midrule
Sand & Sand & 138 & 6.1\% & 41,637,327 & 8.2\% \\
\midrule
Water & Water & 124 & 5.5\% & 24,671,002 & 4.9\% \\
\midrule
House & House & 51 & 2.3\% & 833,756 & 0.16\% \\
\midrule
Ground & Background & 22 & 1.0\% & 651,780 & 0.1\% \\
Road & Background & 22 & 1.0\% & 1,670,985 & 0.3\% \\
Bridge & Background & 12 & 0.5\% & 679,262 & 0.1\% \\
Edgecase Ignore & Background & 7 & 0.3\% & 346,996 & 0.1\% \\
Irrigation Channel & Background & 5 & 0.2\% & 51,097 & 0.0\% \\
\midrule
\textbf{TOTAL} & --- & \textbf{2,257} & \textbf{100\%} & \textbf{506,482,979} & \textbf{100\%} \\
\bottomrule
\end{tabular}%
}
\end{table}

This collapse preserves fine-grained annotations for future crop-condition classification. Figure~\ref{fig:class_samples} presents representative samples. FIELD encompasses full agricultural condition range---from lush rice paddies to bare plots---unified by ownership rather than visual similarity.

\begin{figure}[t]
  \centering
  \includegraphics[width=\columnwidth]{figures/dataset_samples_7class_singleclass_grid2.pdf}
  \caption{Representative samples per class. (a) BACKGROUND. (b) FIELD: parcels unified by ownership. (c) HOUSE. (d) ROAD. (e) WATER. (f) SAND. (g) VEGETATION. (h) Composite.}
  \label{fig:class_samples}
\end{figure}

\begin{figure*}[!t]
  \centering
  \includegraphics[width=\linewidth]{figures/fig4_workflow_overview.pdf}
  \caption{Orthomosaic processing pipeline. 60,971 $\times$ 88,127 pixel orthomosaic (10 km$^2$, 4.31 cm/pixel) partitioned into 1024$\times$1024 tiles. Filtering yields 3,425 clean tiles, 500 annotated for training.}
  \label{fig:workflow_overview}
\end{figure*}

\begin{figure*}[t]
  \centering
  \includegraphics[width=\linewidth]{figures/fig5_boundary_challenges.pdf}
  \caption{Boundary detection failures. Red polygons: SAM2 + 5-stage post-processing. (1) merged parcels (tiles 1,2,6,7), (2) fragmentation (tile 3), (3) boundary blindness (tiles 4,5), (4) inconsistent detection. Geometric heuristics cannot replace learned representations.}
  \label{fig:boundary_challenges}
\end{figure*}

\subsection{Annotation Protocol}

Annotation used Roboflow~\cite{roboflow2024} with polygon-based labeling and consensus review. Annotators trained on semantic parcel boundaries---boundaries encode ownership rather than visual discontinuity. Ambiguous cases (40-50\%) where adjacent fields exhibited identical features used EDGECASE\_IGNORE class.

The final dataset comprises 500 tiles (400 training, 100 validation). We reserved 47 tiles outside the annotated region as held-out test set (9,089 $\times$ 7,297 pixels). Annotations were exported in multiple formats (YOLO, COCO, SAM2).
