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This competition to do land cover classification seems a great opportunity to test our model openly.
It requiers ingestion on non-Sentinel data only in RGB, so we are not there yet.
* 7 regions from 44 countries across 6 continents at a spatial resolution of 0.25–0.5m ground sampling distance for global high-resolution land cover mapping
> * The 408 samples are also split into 258 as trainset, 50 as valset, and 100 as testset.
> File Structure and Content (All files are in `.tif` format):
-----------------------------------------------------------
1. **trainset.zip**:
- Contains `images` and `labels` folders
- `images` folder: 258 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.
- `labels` folder: 258 segmentation masks of the images in the `images` folder.
2. **valset.zip**:
- Contains `images` and `labels` folders
- `images` folder: 50 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.
- `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for
the 30 ``query set`` images in the `images` folder are withheld.
3. **testset.zip**:
- Contains `images` and `labels` folders
- `images` folder: 100 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.
- `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for
the 80 ``query set`` images in the `images` folder are withheld.
4. **train.txt**:
- Contains a list of file names in the `trainset.zip`.
3. **val.json** and **test.json**:
- Contains a list of file names the in the `valset.zip` and `testset.zip`, respectively. Below is
the structure of the `val.json` and `test.json` files.
- fnames = {
{"support_set": {8: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],
9: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],
10: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],
11: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"]},
{"query_set": ["filename_1.tif", "filename_2.tif", "filename_3.tif", ...
....,
"filename_n.tif"]}
}
Land Cover Mapping Classes Strucure:
------------------------------------
1. **The `trainset`:
classId2className = {
# ***Base classes***
1: 'tree',
2: 'rangeland',
3: 'bareland',
4: 'agric land type 1',
5: 'road type 1',
6: 'sea, lake, & pond',
7: 'building type 1'
}
2. **The `valset` and `testset`:
classId2className = {
# ***Base classes***
1: 'tree',
2: 'rangeland',
3: 'bareland',
4: 'agric land type 1',
5: 'road type 1',
6: 'sea, lake, & pond',
7: 'building type 1'
# ***Novel classes***
8: '',
9: '',
10: '',
11: ''
}
- The class names for the ***Novel classes*** depends on the data set.
For the `valset`, the class names can be updated as:
{
8: 'road type 2',
9: 'river',
10: 'boat & ship',
11: 'agric land type 2'
}
The text was updated successfully, but these errors were encountered:
This competition to do land cover classification seems a great opportunity to test our model openly.
It requiers ingestion on non-Sentinel data only in RGB, so we are not there yet.
https://cliffbb.github.io/OEM-Fewshot-Challenge/
The text was updated successfully, but these errors were encountered: