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GEOAI Challenge for Cropland Mapping in Dry Environments by ITU

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False Negatives in Training Data
Help · 14 Aug 2025, 18:08 · 4

Hola Amigos

While looking through the data I found some potentially mis-labeled data in the training set. Check out the IDs 451, 464, 473 and 477 from Orenburg_training_samples. The file labels them as 0 but from my view they are cropland. I crosschecked using ESAs WorldCover 10m v200 maps (for 2021).

I just checked a handfull of samples but this makes me wonder if there are also mislabeled datapoints in the test set.

Here an example:

ID: 451

Cropland: 0

POINT: (54.83862 52.03939)

Google Maps:

WorldCover cropland mask:

Discussion 4 answers
User avatar
CodeJoe

Doesn't really look like a cropland to me😅. How are you able to identify a cropland?

Upvotes 0
User avatar

In ChatGPT we trust:

What’s Visible in the Image

  • Agricultural Field: The large, centrally placed area looks like a cultivated field—perhaps recently plowed soil. Its irregular brown tones and area patterns suggest farmland.
  • Surrounding Vegetation and Settlement: To the left side, there’s a built-up or residential area (visible as small clustered structures and roads). On the right, a band of greener landscape appears—a mixture of vegetation, possibly along a watercourse or riverbank.
  • Linear Features: A thin, perhaps vegetated strip or pathway (maybe a hedgerow, irrigation channel, or small tree line) divides the central field from the greener zone to the right.
  • Natural Terrain Variation: The field shows subtle tonal variations—lighter and darker patches—likely reflecting minor changes in soil moisture, texture, or tillage patterns.
Upvotes 1
User avatar
CodeJoe

I haven't done any form eda yet but are there several images that look like the mislabelled image?

Which IDs then if that's the case?

Upvotes 0
User avatar
CodeJoe

In ChatGPT we trust🤣

Upvotes 0