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+# ethiopia_crops
+
+The Ethiopian Crop Type 2020 (EthCT2020) dataset is a benchmark for
+environmental and agricultural remote sensing applications in complex Ethiopian
+smallholder wheat-based farming systems (1). The dataset consists of harmonized,
+quality-controlled, and georeferenced in-situ samples of annual crop types (2).
+Like the Canada crops inventory, these in situ samples represent an important
+class of sparse-but-high-quality data, and we take additional steps to process
+this dataset for consistency with our evaluation dataset protocols and
+standards.
+
+We downloaded the dataset from Mendeley (2). Per the dataset description, this
+shapefile contains the delimitation of 2,793 circular plots (10-meter radius)
+located in cultivated fields, and the crop information (crop group and crop
+class) of the 2020/21 main Meher season (June 2020 to February 2021) for each
+field plot. Given close proximity of many of the interpreted sites, we do not
+simply remove points to satisfy minimum distance criteria. Rather, we create
+connected components by joining points within 1.28 km radii, and assign points
+to the train or test split based on their component membership. This avoids the
+scenario where a train and test point are in the same spatial neighborhood.
+Component membership assignment is performed to minimize the number of points
+off from which all classes have allocated 20% of their points to their train
+split, though the large size of some components lead to an imbalanced result.
+Given our evaluation protocol sub-samples to the minimum class size this was not
+problematic. We lastly remove all data points with crop classes that have a
+total of <49 train points. The valid time is treated as instantaneous
+(single-date) and set to the data collection timestamp (`"sub_dat"`) from the
+original dataset. Our final `ethiopia_crops` evaluation has a total of 873
+training points and 1,657 test points after pre-processing and spatial proximity
+filtering.
+
+**Table: `ethiopia_crops` classes and sample counts by split**
+
+label | label\_name | train\_count | test\_count
+----: | :---------- | -----------: | ----------:
+0     | wheat       | 377          | 1700
+1     | barley      | 82           | 20
+2     | maize       | 66           | 30
+3     | teff        | 49           | 206
+
+## License
+
+Ethiopian Crop Type 2020 is licensed under the Creative Commons Attribution 4.0
+International License (CC-BY). You may obtain a copy of the CC-BY license at:
+https://creativecommons.org/licenses/by/4.0/legalcode. You can obtain a copy of
+the dataset at https://data.mendeley.com/datasets/mfpvmk8cnm/1. This version of
+the dataset is modified as described in this document.
+
+For the dataset citation, please see (2) in the “References” section below.
+
+## References
+
+1.  G. Blasch, Y. Alemayehu, L. Lesne, J. Wolter, M. Taymans, T. Tesfaye, T.
+    Negash, M. Andulalem, K. Gutu, M. Debela, Z. Eshetu, K. Tesfaye, K.
+    Mottaleb, P. Defourny, D. P. Hodson, Ethiopian Crop Type 2020 (EthCT2020)
+    dataset: Crop type data for environmental and agricultural remote sensing
+    applications in complex Ethiopian smallholder wheat-based farming systems
+    (Meher season 2020/21). Data Brief 54, 110427 (2024).
+2.  G. Blasch, Ethiopian Crop Type 2020 (EthCT2020) dataset, Mendeley Data
+    (2024); https://doi.org/10.17632/MFPVMK8CNM.1.

raw/deepmind_eval/evaluation/ethiopia_crops/ethiopia_crops_trial_groups.json

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+[
+  {
+    "id": "ethiopia_crops_knn1_1000x1",
+    "repeats_n": 1000,
+    "samples_n": 1,
+    "model_type": "knn",
+    "metrics_type": "classification",
+    "model_config": {"k": 1}
+  },
+  {
+    "id": "ethiopia_crops_knn1_500x10",
+    "repeats_n": 500,
+    "samples_n": 10,
+    "model_type": "knn",
+    "metrics_type": "classification",
+    "model_config": {"k": 1}
+  },
+  {
+    "id": "ethiopia_crops_knn1_310x49",
+    "repeats_n": 310,
+    "samples_n": 49,
+    "model_type": "knn",
+    "metrics_type": "classification",
+    "model_config": {"k": 1}
+  },
+  {
+    "id": "ethiopia_crops_knn3_500x10",
+    "repeats_n": 500,
+    "samples_n": 10,
+    "model_type": "knn",
+    "metrics_type": "classification",
+    "model_config": {"k": 3}
+  },
+  {
+    "id": "ethiopia_crops_knn3_310x49",
+    "repeats_n": 310,
+    "samples_n": 49,
+    "model_type": "knn",
+    "metrics_type": "classification",
+    "model_config": {"k": 3}
+  },
+  {
+    "id": "ethiopia_crops_linear_1000x1",
+    "repeats_n": 1000,
+    "samples_n": 1,
+    "model_type": "linear",
+    "metrics_type": "classification"
+  },
+  {
+    "id": "ethiopia_crops_linear_500x10",
+    "repeats_n": 500,
+    "samples_n": 10,
+    "model_type": "linear",
+    "metrics_type": "classification"
+  },
+  {
+    "id": "ethiopia_crops_linear_310x49",
+    "repeats_n": 310,
+    "samples_n": 49,
+    "model_type": "linear",
+    "metrics_type": "classification"
+  }
+]

raw/deepmind_eval/evaluation/glance/README.md

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+# glance
+
+The NASA-funded Global Land Cover Estimation (GLanCE) project seeks to provide
+high-quality long-term records of land cover and land cover change at a 30 m
+spatial resolution for the 21st century (2001 to present) (1). The GLanCE
+training dataset was designed for regional-to-global land cover and land cover
+change analyses (2, 3). Similar to LCMAP, the dataset legend is general-purpose
+and intended to support a broader community of end-users; however, the GLaNCE
+dataset has a global (as opposed to national) scope.
+
+Our `glance` evaluation dataset is derived from the GLanCE training dataset in
+the GEE Community Catalog
+(`"projects/sat-io/open-datasets/GLANCE/GLANCE_TRAINING_DATA_V1"`) (4). Though
+published GLaNCE data products use Level 1 labels (5), we use the Level 2 of the
+labeling hierarchy (`"Glance_Class_ID_level2"`) as a test of maximizing thematic
+detail. Given that GLaNCE includes a number of other datasets, some of which
+overlap with other evaluation datasets, e.g., LCMAP, we select a subset of
+sources, specifically the MODIS STEP dataset (STEP), results of
+spectral-temporal clustering (CLUSTERING), a labeled dataset from the NASA
+Arctic-Boreal Vulnerability Experiment (ABoVE), and a set of annotations
+collection by the project team (`"Dataset_Code"` = `1`, `2`, `4`, or `704`).
+GLaNCE labels are associated with time segments, i.e., labels have a start and
+end date similar to our use of a valid period. We select only labeled segments
+with an end year after 2017 ("End_Year" greater than or equal to 2017). We
+remove null values as well as the `"ice_and_snow"` and `"moss"` categories,
+which have fewer than 500 samples per class. This results in a final dataset
+with eleven classes, and we select 300 training points per class with the
+remainder allocated to the test split. Though we note that segments could be
+converted to a series of annual labels for each location, this approach would be
+subject to greater temporal autocorrelation across labels for the same location;
+instead, we sample a random year between segment start and end dates as an
+annual valid period to ensure more independent sampling of the time domain. Our
+final GLaNCE land cover evaluation has a total of 3,300 training points and
+31,585 test points after pre-processing and spatial proximity filtering.
+
+label | label\_name | train\_count | test\_count
+----: | :---------- | -----------: | ----------:
+0     | water       | 300          | 1167
+1     | developed   | 300          | 878
+2     | soil        | 300          | 291
+3     | rock        | 300          | 921
+4     | sand        | 300          | 1195
+5     | deciduous   | 300          | 2700
+6     | evergreen   | 300          | 5959
+7     | mixed       | 300          | 2425
+8     | shrub       | 300          | 2118
+9     | grassland   | 300          | 6952
+10    | agriculture | 300          | 6979
+
+## License
+
+GLanCE: A Global Land Cover Training Dataset from 1984 to 2020, from Boston
+University Global Land Cover Estimation (GLanCE), is licensed under the Creative
+Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of
+the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode. You
+can obtain a copy of the dataset at
+https://source.coop/repositories/boston-university/bu-glance/access and/or
+https://gee-community-catalog.org/projects/glance_training/?h=glance. This
+version of the dataset is modified as described above.
+
+For the dataset citation, please see (3) in the “References” section below.
+
+## References
+
+1.  M. A. Friedl, C. E. Woodcock, P. Olofsson, Z. Zhu, T. Loveland, R.
+    Stanimirova, P. Arevalo, E. Bullock, K.-T. Hu, Y. Zhang, K. Turlej, K.
+    Tarrio, K. McAvoy, N. Gorelick, J. A. Wang, C. P. Barber, C. Souza Jr,
+    Medium spatial resolution mapping of global land cover and land cover change
+    across multiple decades from Landsat. Front. Remote Sens. 3 (2022).
+2.  R. Stanimirova, K. Tarrio, K. Turlej, K. McAvoy, S. Stonebrook, K.-T. Hu, P.
+    Arévalo, E. L. Bullock, Y. Zhang, C. E. Woodcock, P. Olofsson, Z. Zhu, C. P.
+    Barber, C. M. Souza Jr, S. Chen, J. A. Wang, F. Mensah, M. Calderón-Loor, M.
+    Hadjikakou, B. A. Bryan, J. Graesser, D. L. Beyene, B. Mutasha, S. Siame, A.
+    Siampale, M. A. Friedl, A global land cover training dataset from 1984
+    to 2020. Sci. Data 10, 879 (2023).
+3.  R. Stanimirova, K. Tarrio, K. Turlej, K. McAvoy, S. Stonebrook, K.-T. Hu, P.
+    Arévalo, E. L. Bullock, Y. Zhang, C. E. Woodcock, P. Olofsson, Z. Zhu, C. P.
+    Barber, C. M. Souza Jr, S. Chen, J. A. Wang, F. Mensah, M. Calderón-Loor, M.
+    Hadjikakou, B. A. Bryan, J. Graesser, D. L. Beyene, B. Mutasha, S. Siame, A.
+    Siampale, M. A. Friedl, "A Global Land Cover Training Dataset from 1984 to
+    2020", Version 1.0, Radiant MLHub (2023);
+    https://doi.org/10.34911/rdnt.x4xfh3.
+4.  S. Roy, T. Swetnam, A. Saah, Samapriya/awesome-Gee-Community-Datasets:
+    Community Catalog, Zenodo (2025); http://dx.doi.org/10.5281/ZENODO.14757583.
+5.  P. Arevalo, R. Stanimirova, E. Bullock, Y. Zhang, K. Tarrio, K. Turlej, K.
+    Hu, K. McAvoy, V. Pasquarella, C. Woodcock, P. Olofsson, Z. Zhu, N.
+    Gorelick, T. Loveland, C. Barber, M. Friedl, Global Land Cover Mapping and
+    Estimation Yearly 30 m V001. NASA EOSDIS Land Processes Distributed Active
+    Archive Center (2022); https://lpdaac.usgs.gov/products/glance30v001/.