Near duplicates in vessel detection train dataset

I created a func to visualise the labels:
```python
def display_annotations(folder):
"""
Display the RGB image with annotation points overlaid in red,
using map bounds from metadata to compute correct pixel scale.
Assumes bottom-left origin.
"""
# Paths
img_path = os.path.join(folder, 'layers', 'sentinel2', 'R_G_B', 'image.png')
meta_path = os.path.join(folder, 'metadata.json')
geojson_path = os.path.join(folder, 'layers', 'label', 'data.geojson')

# Load image
img = Image.open(img_path)
W, H = img.size

# Load spatial metadata
meta = json.load(open(meta_path))
xmin, ymin, xmax, ymax = meta['bounds']

# Derive resolutions from bounds and image size
x_res = (xmax - xmin) / float(W) # map units per pixel X
y_res = (ymax - ymin) / float(H) # map units per pixel Y

# Load annotations
gj = json.load(open(geojson_path))
features = gj.get('features', [])
if not features:
print("No features to display.")
return
print(f"Found {len(features)} features.")

# Convert geo coords -> pixel coords
xs, ys = [], []
for feat in features:
X_geo, Y_geo = feat['geometry']['coordinates']
col = (X_geo - xmin) / x_res
row = (Y_geo - ymin) / y_res
xs.append(col)
ys.append(row)

# Plot with origin='lower'
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(img, origin='lower')
ax.scatter(xs, ys, s=2, marker='o', c='red') # edgecolors='k'
ax.set_xlim(0, W)
ax.set_ylim(0, H)
ax.axis('off')
plt.show()
```
I've observed multiple instances of near duplicates in the train data, e.g.

- 2062196_1236837_85862 # Found 1 features.
- 2062196_1236837_86652 # Found 2 features.

They are the same scene, except the second has an additional feature, and the location of the first is slightly offset. Is this due to slightly different timings in AIS? Any thoughts about whether this negatively impacts training?

And comparison of the labels: