---

name: data-preparation description: "Prepare and validate GeoSeg LoveDA masks and Vaihingen, Potsdam, and UAVid image-mask patches with deterministic, path-explicit workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

GeoSeg data preparation

Use this sub-skill to stage externally acquired datasets, convert LoveDA labels, split ISPRS/UAVid imagery into loader-compatible patches, and diagnose pairing, encoding, suffix, and shape failures. This skill does not download data, train a model, load checkpoints, evaluate predictions, or run inference.

Route first

• Read data-formats.md for source trees, suffixes, dimensions, class values, palettes, and output schemas.
• Follow workflows.md for concrete commands and preflight/validation order.
• Use troubleshooting.md for dependency, data/config, CLI, backend, and workflow failures.
• Run the bundled scripts from any current working directory with explicit input and output paths:
  • convert_loveda_masks.py
  • split_vaihingen_patches.py
  • split_potsdam_patches.py
  • split_uavid_patches.py

Conceptually hand processed directories to the sibling training sub-skill only after paired-file and label checks. Route model/backbone and config choices to model-and-config, and route checkpoint evaluation or inference rendering to evaluation-inference.

Operating contract

1. Confirm dataset acquisition and preserve a read-only raw tree. Choose a separate processed output tree; never mix dataset families or eroded and non-eroded masks.
2. Inspect source stems and suffixes before running a splitter. The bundled scripts pair files by exact stem, sort them, validate all expected pairs, and fail loudly on missing partners rather than pairing by directory order.
3. Validate image/mask height and width and reject unsupported mask colors or label values. Mask resampling is nearest-neighbor; image resampling is bicubic. Never use bilinear/bicubic interpolation on class IDs.
4. Treat output directories as immutable by default. Existing outputs require a new destination or explicit --overwrite; writes use temporary files and atomic replacement. Do not point a training loader at a --gt RGB visualization directory.
5. Record dataset name/domain/split, raw and processed roots, selected suffix flags, tile and stride sizes, mode, label encoding, script command, and validation result in the handoff. Do not claim the dataset is valid based on file counts alone.

Fast commands

LoveDA conversion (repeat for each Urban/Rural Train or Val mask directory):

python /path/to/convert_loveda_masks.py \
  --mask-dir /abs/data/LoveDA/Train/Rural/masks_png \
  --output-mask-dir /abs/data/LoveDA/Train/Rural/masks_png_convert

Vaihingen 1024 training patches:

python /path/to/split_vaihingen_patches.py \
  --img-dir /abs/data/vaihingen/train_images \
  --mask-dir /abs/data/vaihingen/train_masks \
  --output-img-dir /abs/data/vaihingen/train/images_1024 \
  --output-mask-dir /abs/data/vaihingen/train/masks_1024 \
  --mode train --split-size 1024 --stride 512

Potsdam RGB 1024 patches:

python /path/to/split_potsdam_patches.py \
  --img-dir /abs/data/potsdam/train_images \
  --mask-dir /abs/data/potsdam/train_masks \
  --output-img-dir /abs/data/potsdam/train/images_1024 \
  --output-mask-dir /abs/data/potsdam/train/masks_1024 \
  --mode train --split-size 1024 --stride 1024 --rgb-image

UAVid nested sequence patches:

python /path/to/split_uavid_patches.py \
  --input-dir /abs/data/uavid/uavid_train_val \
  --output-img-dir /abs/data/uavid/train_val/images \
  --output-mask-dir /abs/data/uavid/train_val/masks \
  --mode train --split-size-h 1024 --split-size-w 1024 \
  --stride-h 1024 --stride-w 1024

Use --help on each script to confirm defaults and flags. All four scripts require explicit paths in this adapted runtime; this avoids silently writing relative to an arbitrary checkout. --mode is retained for compatibility and output semantics. Vaihingen/Potsdam train mode emits deterministic source, horizontal-flip, and vertical-flip variants; validation/test emits one. UAVid emits no augmentation, as in the source utility. The adaptations intentionally do not reproduce source multiprocessing or random crop/car augmentation: they prioritize reproducible, auditable patch production while preserving the source flags and loader file contracts.

Acceptance checks

A successful preparation handoff must include:

• source layout and external acquisition status;
• exact command and effective absolute input/output paths;
• counts of input pairs and output patches;
• image/mask stem equality in each processed directory;
• sample shapes and numpy.unique/palette validation;
• LoveDA indexed-vs-RGB distinction, or ISPRS/UAVid class mapping;
• tile/stride/padding and mode details;
• any skipped edge regions or rejected files;
• unresolved dataset or backend limitations.

The checkout had no bundled datasets, tests, docs, or examples beyond the README/configs/tools/root scripts. LoveDA's validation dataset is constructed at import and therefore requires an external Val layout. The verified inspection environment was Python 3.8 with OpenCV/Pillow-compatible tooling; this sub-skill scripts use only Python plus Pillow and NumPy. Do not infer that training dependencies or optional mamba_ssm are installed from preprocessing success.