diff --git a/CHANGELOG.md b/CHANGELOG.md
index dc63552f..23b5afb4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1414,3 +1414,4 @@ Added:
 - Added `scripts/audit_operator_yolo_dataset_quality.py`, an operator-only YOLO tile dataset quality audit that produces JSON and Markdown reports for sample coverage, split coverage, repeated hard-negative pressure and label-size integrity before further training runs.
 - Added pytest coverage and readiness syntax checking for the new operator YOLO dataset audit script.
 - Recorded live Tower audit results showing `yolo-building-tile-expanded160` as the clean current baseline and r4/r8 hard-negative datasets as repeat-heavy evidence sets that need more unique background AOIs before further hard-negative training.
+- Expanded the explicit operator background-candidate AOI registry from 3 to 9 unique hard-negative locations and added tests for diversity/spread before further YOLO training.
diff --git a/backend/tests/test_sprint131_operator_sample_expansion.py b/backend/tests/test_sprint131_operator_sample_expansion.py
index dc699fa9..9295a5c9 100644
--- a/backend/tests/test_sprint131_operator_sample_expansion.py
+++ b/backend/tests/test_sprint131_operator_sample_expansion.py
@@ -25,7 +25,16 @@ def test_operator_sample_registry_includes_kempen_reference_and_background_candi
     module = load_sample_preparer()
 
     expected_reference_slugs = {"geel", "mol", "turnhout", "herentals", "balen", "retie", "westerlo"}
-    expected_background_slugs = {"postel_bos", "lommel_heide", "kasterlee_bos"}
+    expected_background_slugs = {
+        "postel_bos",
+        "lommel_heide",
+        "kasterlee_bos",
+        "dessel_heide",
+        "ravels_bos",
+        "meerhout_bos",
+        "geel_bel",
+        "arendonk_heide",
+    }
 
     assert expected_reference_slugs.issubset(module.SAMPLES)
     assert expected_background_slugs.issubset(module.SAMPLES)
@@ -34,6 +43,26 @@ def test_operator_sample_registry_includes_kempen_reference_and_background_candi
     assert all(module.SAMPLES[slug].sample_role == "background_candidate" for slug in expected_background_slugs)
 
 
+def test_operator_background_candidates_are_unique_enough_for_hard_negative_training() -> None:
+    module = load_sample_preparer()
+
+    background_samples = [
+        sample
+        for sample in module.SAMPLES.values()
+        if sample.sample_role == "background_candidate"
+    ]
+    centers = {(round(sample.center_lon, 4), round(sample.center_lat, 4)) for sample in background_samples}
+    half_sizes = {sample.half_size_m for sample in background_samples}
+
+    assert len(background_samples) >= 8
+    assert len(centers) == len(background_samples)
+    assert min(sample.center_lon for sample in background_samples) < 4.85
+    assert max(sample.center_lon for sample in background_samples) > 5.25
+    assert min(sample.center_lat for sample in background_samples) < 51.18
+    assert max(sample.center_lat for sample in background_samples) > 51.33
+    assert half_sizes == {260.0}
+
+
 def test_background_candidate_can_write_empty_reference_geojson(tmp_path: Path, monkeypatch) -> None:
     module = load_sample_preparer()
 
diff --git a/docs/CODEX_EXECUTION_LOG.md b/docs/CODEX_EXECUTION_LOG.md
index 48175065..955e0784 100644
--- a/docs/CODEX_EXECUTION_LOG.md
+++ b/docs/CODEX_EXECUTION_LOG.md
@@ -5670,3 +5670,25 @@ Open:
 ## Next recommended pass
 
 - Add more unique hard-negative/background AOIs before another hard-negative training run. The current label files are clean, so the bottleneck is dataset diversity and balance rather than label-file corruption.
+
+# Sprint 147 - Unique hard-negative AOI expansion
+
+## What changed
+
+- Expanded `scripts/prepare_operator_real_data_samples.py` with six additional explicit background-candidate AOIs: Dessel-heide, Ravels-bos, Meerhout-bos, Geel-Bel, Arendonk-heide and Herenthout-bos.
+- Background candidates remain operator/runtime samples only. They are not product providers, not fixtures and not automatic app fetches.
+- Added sample-registry test coverage for minimum unique background count, unique centers and regional spread.
+- Updated operator documentation with the expanded default corpus and the next required Tower regeneration step.
+
+## What was tested
+
+- `python -m pytest backend\tests\test_sprint131_operator_sample_expansion.py -q`
+
+## Known limitations
+
+- The new AOIs still need to be prepared on Tower before they affect the live operator YOLO tile exports.
+- GRB may return sparse buildings in some background candidates; they remain valid hard-negative candidates only after the manifest and tile audit confirm their actual labels.
+
+## Next recommended pass
+
+- Pull this commit on Tower, rerun `prepare_operator_real_data_samples.py`, export a new hard-negative tile dataset without excessive repeat pressure and audit it before training.
diff --git a/docs/TODO.md b/docs/TODO.md
index 5ff894a3..76c01f32 100644
--- a/docs/TODO.md
+++ b/docs/TODO.md
@@ -442,5 +442,13 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Report sample coverage, validation coverage, repeated hard-negative pressure and YOLO label area integrity.
 - [x] Wire the audit script into the readiness syntax gate.
 - [x] Use live audit output to decide whether the next model pass needs more positive AOIs, label cleanup or unique hard negatives.
-- [ ] Add more unique background/hard-negative AOIs before repeating hard-negative-balanced YOLO training.
+- [x] Add more unique background/hard-negative AOIs before repeating hard-negative-balanced YOLO training.
 - [ ] Keep `yolo-building-tile-expanded160` as the clean current training baseline; avoid promoting r4/r8 repeat-heavy datasets as defaults.
+- [ ] Regenerate Tower operator samples, export a new unique-hard-negative tile dataset and rerun the dataset audit before training.
+
+# Sprint 147 - Unique hard-negative AOI expansion
+
+- [x] Expand the documented operator background candidates from 3 to 9 unique AOIs.
+- [x] Keep every new background AOI explicit, `allow_empty_reference=True`, and `sample_role='background_candidate'`.
+- [x] Add test coverage for minimum background candidate count, unique centers and regional spread.
+- [ ] Prepare the new samples on Tower and build a fresh hard-negative tile dataset.
diff --git a/scripts/README.md b/scripts/README.md
index 575d1c4a..0f9241bb 100644
--- a/scripts/README.md
+++ b/scripts/README.md
@@ -182,13 +182,14 @@ This writes GeoTIFF/GeoJSON pairs and `operator_samples_manifest.json` under
 `/app/storage/operator-data` inside the container, which maps to
 `storage/operator-data` in the Tower appdata checkout. The default corpus
 contains reference AOIs for Geel, Mol, Turnhout, Herentals, Balen, Retie and
-Westerlo plus background candidates for Postel-bos, Lommel-heide and
-Kasterlee-bos. Normal reference AOIs still fail when GRB returns no buildings;
-background candidates are explicitly marked with `sample_role` and may write an
-empty reference FeatureCollection for negative-tile training. The helper fetches
-only the explicit documented AOIs, records Digitaal Vlaanderen attribution and
-reuses existing files by default. Use `--force` only when the local runtime
-artifacts should be regenerated.
+Westerlo plus background candidates for Postel-bos, Lommel-heide,
+Kasterlee-bos, Dessel-heide, Ravels-bos, Meerhout-bos, Geel-Bel,
+Arendonk-heide and Herenthout-bos. Normal reference AOIs still fail when GRB
+returns no buildings; background candidates are explicitly marked with
+`sample_role` and may write an empty reference FeatureCollection for
+negative-tile training. The helper fetches only the explicit documented AOIs,
+records Digitaal Vlaanderen attribution and reuses existing files by default.
+Use `--force` only when the local runtime artifacts should be regenerated.
 
 The real-data smoke is intentionally mutating and refuses to run without
 operator-supplied files. Current V1 upload support expects a georeferenced
@@ -351,6 +352,8 @@ Current Tower audit status:
 - `yolo-building-tile-hardneg160r4` and `yolo-building-tile-hardneg160r8`:
   repeat-heavy hard-negative variants; useful evidence, but add more unique
   background AOIs before training another hard-negative-balanced candidate.
+- Regenerate `operator_samples_manifest.json` after pulling Sprint 147+ so the
+  expanded unique background AOI set is available for the next tile export.
 
 For hard-negative-balanced experiments, repeat only train-split negative tiles
 from samples marked `sample_role=background_candidate`:
@@ -415,7 +418,7 @@ before changing model defaults:
 
 ```bash
 OPERATOR_SAMPLE_MANIFEST_PATH=storage/operator-data/operator_samples_manifest.json \
-OPERATOR_BACKGROUND_SAMPLE_SLUGS="postel_bos lommel_heide kasterlee_bos" \
+OPERATOR_BACKGROUND_SAMPLE_SLUGS="postel_bos lommel_heide kasterlee_bos dessel_heide ravels_bos meerhout_bos geel_bel arendonk_heide herenthout_bos" \
 QUALITY_MODEL_ASSET_IDS="geointel-building-yolov8n-expanded160e50-pt geointel-building-yolov8n-tile30-pt yolov8s-building-segmentation-pt" \
 QUALITY_TILE_SIZES="640" \
 QUALITY_TILE_OVERLAPS="64" \
diff --git a/scripts/prepare_operator_real_data_samples.py b/scripts/prepare_operator_real_data_samples.py
index e3efb84e..277ce166 100644
--- a/scripts/prepare_operator_real_data_samples.py
+++ b/scripts/prepare_operator_real_data_samples.py
@@ -114,6 +114,60 @@ SAMPLES: dict[str, OperatorSample] = {
         sample_role="background_candidate",
         allow_empty_reference=True,
     ),
+    "dessel_heide": OperatorSample(
+        slug="dessel_heide",
+        display_name="Dessel heath background candidate",
+        center_lon=5.092,
+        center_lat=51.235,
+        half_size_m=260.0,
+        sample_role="background_candidate",
+        allow_empty_reference=True,
+    ),
+    "ravels_bos": OperatorSample(
+        slug="ravels_bos",
+        display_name="Ravels forest background candidate",
+        center_lon=4.977,
+        center_lat=51.384,
+        half_size_m=260.0,
+        sample_role="background_candidate",
+        allow_empty_reference=True,
+    ),
+    "meerhout_bos": OperatorSample(
+        slug="meerhout_bos",
+        display_name="Meerhout forest background candidate",
+        center_lon=5.069,
+        center_lat=51.115,
+        half_size_m=260.0,
+        sample_role="background_candidate",
+        allow_empty_reference=True,
+    ),
+    "geel_bel": OperatorSample(
+        slug="geel_bel",
+        display_name="Geel-Bel rural background candidate",
+        center_lon=5.046,
+        center_lat=51.137,
+        half_size_m=260.0,
+        sample_role="background_candidate",
+        allow_empty_reference=True,
+    ),
+    "arendonk_heide": OperatorSample(
+        slug="arendonk_heide",
+        display_name="Arendonk heath background candidate",
+        center_lon=5.238,
+        center_lat=51.334,
+        half_size_m=260.0,
+        sample_role="background_candidate",
+        allow_empty_reference=True,
+    ),
+    "herenthout_bos": OperatorSample(
+        slug="herenthout_bos",
+        display_name="Herenthout forest background candidate",
+        center_lon=4.781,
+        center_lat=51.143,
+        half_size_m=260.0,
+        sample_role="background_candidate",
+        allow_empty_reference=True,
+    ),
 }