diff --git a/scripts/prepare_USGS.py b/scripts/prepare_USGS.py
index b0809dd..f8b9c57 100644
--- a/scripts/prepare_USGS.py
+++ b/scripts/prepare_USGS.py
@@ -19,6 +19,9 @@
 and to updating labels (overwriting UBFAI from detection/crops). Use
 --no-generate-detection-crops or --no-update-labels to skip either step.
 
+Stage 0 parallelizes split_raster across images when multiple CPUs are available
+(set SLURM_CPUS_PER_TASK in your sbatch script, or PREPARE_USGS_CROP_WORKERS).
+
 Why new Label Studio annotations might not appear in classification:
 - Stage 0 (generate detection crops): runs by default; only regenerates a crop
   CSV when that image's annotation CSV is newer (or the crop is missing).
@@ -42,6 +45,7 @@
 import os
 import random
 import shutil
+from concurrent.futures import ProcessPoolExecutor, as_completed
 
 import numpy as np
 import pandas as pd
@@ -64,6 +68,37 @@
 PATCH_OVERLAP = 0
 
 
+def _detection_crop_parallel_workers() -> int:
+    """Workers for per-image split_raster in Stage 0.
+
+    Prefer SLURM allocation, then explicit override, then host CPU count.
+    """
+    for key in ("PREPARE_USGS_CROP_WORKERS", "SLURM_CPUS_PER_TASK"):
+        raw = os.environ.get(key)
+        if raw:
+            return max(1, int(raw))
+    return max(1, os.cpu_count() or 1)
+
+
+def _detection_crop_worker(task: tuple) -> str:
+    """Run split_raster for one image (pickled args; must stay top-level for spawn)."""
+    root_dir, save_dir, image_path, records, patch_size, patch_overlap = task
+    import pandas as pd
+    from src import data_processing
+
+    annotation_df = pd.DataFrame.from_records(records)
+    data_processing.process_image(
+        image_path=image_path,
+        annotation_df=annotation_df,
+        root_dir=root_dir,
+        save_dir=save_dir,
+        patch_size=patch_size,
+        patch_overlap=patch_overlap,
+        allow_empty=True,
+    )
+    return image_path
+
+
 def parse_args():
     parser = argparse.ArgumentParser(
         description="Prepare USGS detection data for training"
@@ -310,14 +345,38 @@ def generate_detection_crops():
             continue
 
         combined_refresh = combined[combined["image_path"].isin(images_to_refresh)]
-        data_processing.preprocess_images(
-            combined_refresh,
-            root_dir=root_dir,
-            save_dir=save_dir,
-            patch_size=PATCH_SIZE,
-            patch_overlap=PATCH_OVERLAP,
-            allow_empty=True,
-        )
+        n_workers = min(_detection_crop_parallel_workers(), len(images_to_refresh))
+        if n_workers <= 1:
+            data_processing.preprocess_images(
+                combined_refresh,
+                root_dir=root_dir,
+                save_dir=save_dir,
+                patch_size=PATCH_SIZE,
+                patch_overlap=PATCH_OVERLAP,
+                allow_empty=True,
+            )
+        else:
+            tasks = []
+            for image_path in images_to_refresh:
+                ann = combined_refresh[combined_refresh["image_path"] == image_path]
+                tasks.append(
+                    (
+                        root_dir,
+                        save_dir,
+                        image_path,
+                        ann.to_dict("records"),
+                        PATCH_SIZE,
+                        PATCH_OVERLAP,
+                    )
+                )
+            print(
+                f"  {flight_name}: parallel split_raster "
+                f"({len(tasks)} images, {n_workers} workers)"
+            )
+            with ProcessPoolExecutor(max_workers=n_workers) as pool:
+                futures = [pool.submit(_detection_crop_worker, t) for t in tasks]
+                for fut in as_completed(futures):
+                    fut.result()
         print(
             f"  {flight_name}: refreshed {len(images_to_refresh)} images (of "
             f"{combined['image_path'].nunique()} total) -> {save_dir}"
diff --git a/submit_prepare_annotations.sh b/submit_prepare_annotations.sh
new file mode 100755
index 0000000..4be0d66
--- /dev/null
+++ b/submit_prepare_annotations.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+# One SLURM job: request many CPUs; prepare_USGS.py parallelizes split_raster per image.
+# Run from repo root on the cluster: sbatch submit_prepare_annotations.sh
+
+#SBATCH --job-name=prep_ann
+#SBATCH --account=ewhite
+#SBATCH --partition=hpg-b200
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=32
+#SBATCH --mem=64GB
+#SBATCH --time=24:00:00
+#SBATCH --output=/home/b.weinstein/logs/prep_ann_%j.out
+#SBATCH --error=/home/b.weinstein/logs/prep_ann_%j.err
+
+cd "${SLURM_SUBMIT_DIR}"
+uv run python scripts/prepare_USGS.py