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--git a/raw/deepmind_eval/evaluation/ethiopia_crops/README.md b/raw/deepmind_eval/evaluation/ethiopia_crops/README.md new file mode 100644 index 0000000000000000000000000000000000000000..34bb3b666fb5a6ba08cb8f67b6fef0567baee508 --- /dev/null +++ b/raw/deepmind_eval/evaluation/ethiopia_crops/README.md @@ -0,0 +1,60 @@ +# 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. 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"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" + } +] diff --git a/raw/deepmind_eval/evaluation/glance/README.md b/raw/deepmind_eval/evaluation/glance/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f2d47dc444e4eda662d503ab5f4eda00817a2c49 --- /dev/null +++ b/raw/deepmind_eval/evaluation/glance/README.md @@ -0,0 +1,89 @@ +# 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. 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"metrics_type": "classification" + } +] diff --git a/raw/deepmind_eval/evaluation/lcmap/lcmap_lc/lcmap_lc.png b/raw/deepmind_eval/evaluation/lcmap/lcmap_lc/lcmap_lc.png new file mode 100644 index 0000000000000000000000000000000000000000..527e999f38ff5823fe3b2652572892295bf03236 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lcmap/lcmap_lc/lcmap_lc.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64137bd4e580453979f541ed50257fb88aa7b9dd303fde9cb1c7df6daaa39d67 +size 103883 diff --git a/raw/deepmind_eval/evaluation/lcmap/lcmap_lc/lcmap_lc_trial_groups.json b/raw/deepmind_eval/evaluation/lcmap/lcmap_lc/lcmap_lc_trial_groups.json new file mode 100644 index 0000000000000000000000000000000000000000..20fc29089d93a9e8924ae29b14f682e460e65a9a --- /dev/null +++ b/raw/deepmind_eval/evaluation/lcmap/lcmap_lc/lcmap_lc_trial_groups.json @@ -0,0 +1,63 @@ +[ + { + "id": "lcmap_lc_knn1_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "knn", + "metrics_type": "classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_lc_knn1_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_lc_knn1_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_lc_knn3_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": {"k": 3} + }, + { + "id": "lcmap_lc_knn3_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": {"k": 3} + }, + { + "id": "lcmap_lc_linear_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "linear", + "metrics_type": "classification" + }, + { + "id": "lcmap_lc_linear_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "linear", + "metrics_type": "classification" + }, + { + "id": "lcmap_lc_linear_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "linear", + "metrics_type": "classification" + } +] diff --git a/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/README.md b/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/README.md new file mode 100644 index 0000000000000000000000000000000000000000..263a34252b3e6c3bbc981e4d3158d8daefbeb83e --- /dev/null +++ b/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/README.md @@ -0,0 +1,39 @@ +# lcmap_lcc + +Though the source LCMAP dataset (1) includes change process labels, we generated +new land use change labels directly from the `"dominant_landcover"` and +`"image_year"` fields for parity with our `lcmap` classification evaluations. We +subset the full reference dataset to 2017-2021 to overlap with our period of +interest. We then label pairs of sequential years where the label is not +consistent (i.e., yeart and yeart+1 have different labels) as “change” and years +with consistent labels as “no change”. We select only one year-pair for each +reference point, and we sample a total of 300 points per class for land cover +change. Because we want to compare embeddings for two annual labels, we set a +valid time start and end for both the before and after periods, where both +periods are one year in length from January 1 to January 1 of the following year +and assigned based on the `"image_year"` property in the source dataset. Our +final `lcmap_lcc` evaluation dataset has a total of 600 training points and +1,720 test points. + +**Table: `lcmap_lcc` classes and sample counts by split** + +label | label\_name | train\_count | test\_count +----: | ----------: | -----------: | ----------: +0 | no change | 300 | 1315 +1 | change | 300 | 405 + +## License + +This data is in the public domain and available at +https://www.usgs.gov/special-topics/lcmap/lcmap-conus-reference-data and/ or +https://gee-community-catalog.org/projects/lcmap/#lcmap-level-1-land-cover-classes. +Details about USGS data in the public domain can be found here: +https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits. +This version of the dataset is modified as described above. + +## References + +1. B. Pengra, S. Stehman, J. A. Horton, R. F. Auch, S. Kambly, M. L. Knuppe, D. + Sorenson, C. J. Robison, J. Taylor, LCMAP CONUS Reference Data Product + 1984-2021 land cover, land use and change process attributes, U.S. + Geological Survey (2023); https://doi.org/10.5066/P933Z1TK. diff --git a/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/lcmap_lcc.png b/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/lcmap_lcc.png new file mode 100644 index 0000000000000000000000000000000000000000..bfb35345adc87160d719009ec6d033b0f49bc273 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/lcmap_lcc.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92ecf46e0d8a3306bc7a2d67b5dac97d2ffc73e6835017f1d34d243762236821 +size 45219 diff --git a/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/lcmap_lcc_trial_groups.json b/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/lcmap_lcc_trial_groups.json new file mode 100644 index 0000000000000000000000000000000000000000..b2649fd8088a0b2b7eb8b8cc5e63fee751101dd7 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lcmap/lcmap_lcc/lcmap_lcc_trial_groups.json @@ -0,0 +1,70 @@ +[ + { + "id": "lcmap_lcc_change_dot_product_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "change_dot_product", + "metrics_type": "classification" + }, + { + "id": "lcmap_lcc_change_knn1_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_lcc_change_knn1_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_lcc_change_knn1_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_lcc_change_knn3_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 3} + }, + { + "id": "lcmap_lcc_change_knn3_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 3} + }, + { + "id": "lcmap_lcc_change_linear_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "change_linear", + "metrics_type": "classification" + }, + { + "id": "lcmap_lcc_change_linear_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "change_linear", + "metrics_type": "classification" + }, + { + "id": "lcmap_lcc_change_linear_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "change_linear", + "metrics_type": "classification" + } +] diff --git a/raw/deepmind_eval/evaluation/lcmap/lcmap_lu/lcmap_lu.png b/raw/deepmind_eval/evaluation/lcmap/lcmap_lu/lcmap_lu.png new file mode 100644 index 0000000000000000000000000000000000000000..b1c9068efc16d09fec1e0133b6dcf4a999c348a0 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lcmap/lcmap_lu/lcmap_lu.png @@ -0,0 +1,3 @@ +version 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"classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_luc_change_knn1_1x150", + "repeats_n": 1, + "samples_n": 150, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 1} + }, + { + "id": "lcmap_luc_change_knn3_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 3} + }, + { + "id": "lcmap_luc_change_knn3_1x150", + "repeats_n": 1, + "samples_n": 150, + "model_type": "change_knn", + "metrics_type": "classification", + "model_config": {"k": 3} + }, + { + "id": "lcmap_luc_change_linear_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "change_linear", + "metrics_type": "classification" + }, + { + "id": "lcmap_luc_change_linear_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "change_linear", + "metrics_type": "classification" + }, + { + "id": "lcmap_luc_change_linear_1x150", + "repeats_n": 1, + "samples_n": 150, + "model_type": "change_linear", + "metrics_type": "classification" + } +] diff --git a/raw/deepmind_eval/evaluation/lucas/lucas_lc/lucas_lc.png b/raw/deepmind_eval/evaluation/lucas/lucas_lc/lucas_lc.png new file mode 100644 index 0000000000000000000000000000000000000000..d4a5a289369762ff5387969be4cd42141c146f02 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lucas/lucas_lc/lucas_lc.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad7ccce60cbe27d9ebab949288ec809ff5d517932fb14591fb19551bd047c984 +size 79328 diff --git a/raw/deepmind_eval/evaluation/lucas/lucas_lc/lucas_lc_trial_groups.json b/raw/deepmind_eval/evaluation/lucas/lucas_lc/lucas_lc_trial_groups.json new file mode 100644 index 0000000000000000000000000000000000000000..21344bb9c98ad8956cd546e8ef96c319d6e686bb --- /dev/null +++ b/raw/deepmind_eval/evaluation/lucas/lucas_lc/lucas_lc_trial_groups.json @@ -0,0 +1,63 @@ +[ + { + "id": "lucas_lc_knn1_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 1 } + }, + { + "id": "lucas_lc_knn1_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 1 } + }, + { + "id": "lucas_lc_knn1_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 1 } + }, + { + "id": "lucas_lc_knn3_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 3 } + }, + { + "id": "lucas_lc_knn3_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 3 } + }, + { + "id": "lucas_lc_linear_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "linear", + "metrics_type": "classification" + }, + { + "id": "lucas_lc_linear_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "linear", + "metrics_type": "classification" + }, + { + "id": "lucas_lc_linear_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "linear", + "metrics_type": "classification" + } +] diff --git a/raw/deepmind_eval/evaluation/lucas/lucas_lu/README.md b/raw/deepmind_eval/evaluation/lucas/lucas_lu/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f59a513582f469a7ac82ee41cb7398c684660c12 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lucas/lucas_lu/README.md @@ -0,0 +1,53 @@ +# lucas_lu + +Our `lucas_lu` evaluation uses the `"lu1"` label from the LUCAS Harmonized +(Theoretical Location, 2006-2018) V1 dataset (1) sourced from the Earth Engine +Data Catalog (`"JRC/LUCAS_HARMO/THLOC/V1"`). We apply the same initial filtering +described in the top-level `lucas` README. We also check that the `"lu1"` label +is not null, and that percent cover for the `"lu1"` label (`"lu1_perc"`) is +classified as `"50 - 75 %"`, `"75 - 90 %"`, `"> 90 %"`. This results in a label +dataset with 15 land use classes. We assume land use represents an integrated +observation of state (e.g., "forestry" may include periods of tree cover, +clearing, and regrowth), so we set the valid period to a one-year window +centered on the time at which the land use observation was made, i.e., six +months prior, six months after. As with land cover, we select 300 points per +class for training and assign the remainder to the test split (Table S9). Our +final `lucas_lu` evaluation dataset has a total of 4,500 training points and +222,358 test points after pre-processing and spatial proximity filtering. + +**Table: `lucas_lu` classes and sample counts by split** + +label | label\_name | train\_count | test\_count +----: | :------------------------------------------------------------ | -----------: | ----------: +0 | agriculture\_(excluding\_fallow\_land\_and\_kitchen\_gardens) | 300 | 121309 +1 | semi-natural\_and\_natural\_areas\_not\_in\_use | 300 | 24198 +2 | forestry | 300 | 48649 +3 | road\_transport | 300 | 7037 +4 | amenities\_museums\_leisure | 300 | 1273 +5 | other\_abandoned\_areas | 300 | 1460 +6 | residential | 300 | 9937 +7 | kitchen\_garden | 300 | 824 +8 | logistics\_and\_storage | 300 | 206 +9 | fallow\_land | 300 | 5272 +10 | community\_services | 300 | 902 +11 | sport | 300 | 454 +12 | electricity\_gas\_and\_thermal\_power\_distribution | 300 | 163 +13 | commerce | 300 | 417 +14 | mining\_and\_quarrying | 300 | 257 + +## License + +LUCAS Harmonized (Theoretical Location, 2006-2018) V1, from the Joint Research +Centre of the European Commission, 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://developers.google.com/earth-engine/datasets/catalog/JRC_LUCAS_HARMO_THLOC_V1#terms-of-use. +This version of the dataset is modified as described above. + +## References + +1. R. d’Andrimont, M. Yordanov, L. Martinez-Sanchez, B. Eiselt, A. Palmieri, P. + Dominici, J. Gallego, H. I. Reuter, C. Joebges, G. Lemoine, M. van der + Velde, Harmonised LUCAS in-situ land cover and use database for field + surveys from 2006 to 2018 in the European Union. Sci. Data 7, 352 (2020). diff --git a/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu.csv b/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu.csv new file mode 100644 index 0000000000000000000000000000000000000000..0e00a1fafddb64f0e04df1ad8c84a05f0dea1c28 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:049d95fcded4057531a4637a1cb3953f00e3a721f5f9c2cf267f251da56e1718 +size 36893606 diff --git a/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu.png b/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu.png new file mode 100644 index 0000000000000000000000000000000000000000..f8515fe7a3c3c239b9be692c2cfa3db90257248e --- /dev/null +++ b/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aa7c8f5540515e666c3db4b0a094a8db2c54d40cbfa9b80984aba055447d409d +size 88124 diff --git a/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu_trial_groups.json b/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu_trial_groups.json new file mode 100644 index 0000000000000000000000000000000000000000..333b1cfe1d41b52972da873c0dad00a13f3cc8f0 --- /dev/null +++ b/raw/deepmind_eval/evaluation/lucas/lucas_lu/lucas_lu_trial_groups.json @@ -0,0 +1,63 @@ +[ + { + "id": "lucas_lu_knn1_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 1 } + }, + { + "id": "lucas_lu_knn1_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 1 } + }, + { + "id": "lucas_lu_knn1_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 1 } + }, + { + "id": "lucas_lu_knn3_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 3 } + }, + { + "id": "lucas_lu_knn3_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { "k": 3 } + }, + { + "id": "lucas_lu_linear_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "linear", + "metrics_type": "classification" + }, + { + "id": "lucas_lu_linear_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "linear", + "metrics_type": "classification" + }, + { + "id": "lucas_lu_linear_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "linear", + "metrics_type": "classification" + } +] diff --git a/raw/deepmind_eval/evaluation/openet/README.md b/raw/deepmind_eval/evaluation/openet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..56bf19e2e1a372ef503cff098702d962c275b969 --- /dev/null +++ b/raw/deepmind_eval/evaluation/openet/README.md @@ -0,0 +1,67 @@ +# openet_ensemble + +The OpenET project aims to make satellite-based estimates of the total amount of +water that is transferred from the land surface to the atmosphere through the +process of evapotranspiration (ET) available for improved water management (1, +2). OpenET datasets include ET estimates from six different satellite-driven ET +models as well as an ensemble product, which is calculated as the mean of the +ensemble after filtering and removing outliers using the median absolute +deviation approach. All models currently use 30-meter Landsat data to produce ET +estimates, and the monthly ET dataset provides data on total ET by month as an +equivalent depth of water in millimeters. + +Our `openet_ensemble` evaluation dataset is derived from the OpenET monthly +total ensemble product in Earth Engine. This dataset is designed not to measure +performance on ET estimation directly. Rather, it characterizes performance on +proxying the OpenET model ensemble, given that ensemble approaches are +inherently computationally intensive and challenging to scale and have therefore +historically limited OpenET ensemble coverage. Thus accurate proxy models could +be a more viable means of scaling ensemble results over larger extents. + +We construct our `openet_ensemble` evaluation by first tiling CONUS in 35km grid +cells in the Albers conic projection with EPSG code 5070. For each grid cell, we +select a random month from all possible months mapped in the source OpenET +ensemble product, and sample 2 locations for each of 10 equally spaced 20mm bins +between 0mm and 200mm. Locations with ET values > 200mm were assigned to the +highest bin. We ignore locations where less than 5 models in the ensemble ran, +or the disagreement between the minimum and maximum model estimates exceeded +10mm. To each sample we assigned a valid period of the entire month from which +it was drawn, and a support period ending with the end of the valid period, and +extending 1 year prior: this was chosen to emulate the realistic scenario where +evapotranspiration estimates are desired at the conclusion of a given calendar +month. We selected 300 train points per bin, and allocated the remainder to +test. Our final `openet_ensemble` evaluation dataset has a total of 3,000 +training points and 32,683 test points after pre-processing and spatial +proximity filtering. + +## License + +OpenET Ensemble Monthly Evapotranspiration v2.0 from OpenET, Inc. 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://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0#description. +This version of the dataset is modified as described above. + +For the dataset citation, please see (1) in the “References” section below. + +## References + +1. F. S. Melton, J. Huntington, R. Grimm, J. Herring, M. Hall, D. Rollison, T. + Erickson, R. Allen, M. Anderson, J. B. Fisher, A. Kilic, G. B. Senay, J. + Volk, C. Hain, L. Johnson, A. Ruhoff, P. Blankenau, M. Bromley, W. Carrara, + B. Daudert, C. Doherty, C. Dunkerly, M. Friedrichs, A. Guzman, G. Halverson, + J. Hansen, J. Harding, Y. Kang, D. Ketchum, B. Minor, C. Morton, S. + Ortega-Salazar, T. Ott, M. Ozdogan, P. M. ReVelle, M. Schull, C. Wang, Y. + Yang, R. G. Anderson, OpenET: Filling a critical data gap in water + management for the western United States. J. Am. Water Resour. Assoc. 58, + 971–994 (2022). +2. J. M. Volk, J. L. Huntington, F. S. Melton, R. Allen, M. Anderson, J. B. + Fisher, A. Kilic, A. Ruhoff, G. B. Senay, B. Minor, C. Morton, T. Ott, L. + Johnson, B. Comini de Andrade, W. Carrara, C. T. Doherty, C. Dunkerly, M. + Friedrichs, A. Guzman, C. Hain, G. Halverson, Y. Kang, K. Knipper, L. + Laipelt, S. Ortega-Salazar, C. Pearson, G. E. L. Parrish, A. Purdy, P. + ReVelle, T. Wang, Y. Yang, Assessing the accuracy of OpenET satellite-based + evapotranspiration data to support water resource and land management + applications. 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"model_config": {"k": 1} + }, + { + "id": "openet_ensemble_knn1_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "regression", + "model_config": {"k": 1} + }, + { + "id": "openet_ensemble_knn3_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "regression", + "model_config": {"k": 3} + }, + { + "id": "openet_ensemble_knn3_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "regression", + "model_config": {"k": 3} + }, + { + "id": "openet_ensemble_linear_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "linear", + "metrics_type": "regression" + }, + { + "id": "openet_ensemble_linear_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "linear", + "metrics_type": "regression" + }, + { + "id": "openet_ensemble_linear_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "linear", + "metrics_type": "regression" + } +] diff --git a/raw/deepmind_eval/evaluation/us_trees/README.md b/raw/deepmind_eval/evaluation/us_trees/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2b557cc6bbc37b66d2083eb1e875655f33d41231 --- /dev/null +++ b/raw/deepmind_eval/evaluation/us_trees/README.md @@ -0,0 +1,85 @@ +# us_trees + +To evaluate performance on biodiversity-related applications, we leveraged the +Global Biodiversity Information Facility (GBIF) records, specifically +research-grade observations from the iNaturalist citizen science repository (1). +GBIF is a comprehensive collection of species occurrence records. We focus on +genus-level taxonomic labels in the United States given interest in forest +species composition mapping across a diversity of forest types. + +We select GBIF records for the period Jan 1 2017 to Jan 1 2023 and filter to +just observations where the genus label is found in a list of tree genera +sourced from the US Forest Service and country code is set to the US. +Observations must be labeled as human or machine observations, and have a +maximum spatial uncertainty of 10 meters. From this initial list of tree genera +observations, we get the five most frequently observed species for each of the +US states, including Alaska and Hawaii, then combine into a single deduplicated +list of common US tree genera. We select these genera for further processing. + +Of the remaining observations across common tree genera, we drop any that have +less than 500 samples per class, resulting in a final set of 39 genera. We +allocate 300 samples to the train split and the rest to the test split. We treat +observations as instantaneous labels, using the date of the observation record +(`"eventdate"`) as a single-date valid period. Our final `us_trees` evaluation +dataset has a total of 11,700 training points and 33,682 test points after +pre-processing and spatial proximity filtering. + +**Table: `us_trees` classes and sample counts by split** + +label | label\_name | train\_count | test\_count +----: | :----------- | -----------: | ----------: +0 | abies | 300 | 827 +1 | acer | 300 | 1296 +2 | aesculus | 300 | 665 +3 | ailanthus | 300 | 857 +4 | alnus | 300 | 220 +5 | amelanchier | 300 | 203 +6 | asimina | 300 | 614 +7 | betula | 300 | 917 +8 | carya | 300 | 628 +9 | cercis | 300 | 1296 +10 | cornus | 300 | 1296 +11 | diospyros | 300 | 934 +12 | elaeagnus | 300 | 1296 +13 | fagus | 300 | 1187 +14 | gleditsia | 300 | 353 +15 | ilex | 300 | 1296 +16 | juglans | 300 | 620 +17 | juniperus | 300 | 1296 +18 | liquidambar | 300 | 1296 +19 | liriodendron | 300 | 1173 +20 | maclura | 300 | 317 +21 | magnolia | 300 | 874 +22 | morus | 300 | 387 +23 | picea | 300 | 621 +24 | pinus | 300 | 1296 +25 | populus | 300 | 1296 +26 | prosopis | 300 | 1057 +27 | prunus | 300 | 1296 +28 | pseudotsuga | 300 | 602 +29 | quercus | 300 | 1296 +30 | sabal | 300 | 272 +31 | salix | 300 | 957 +32 | sassafras | 300 | 1006 +33 | taxodium | 300 | 271 +34 | thuja | 300 | 474 +35 | triadica | 300 | 300 +36 | tsuga | 300 | 1190 +37 | ulmus | 300 | 604 +38 | yucca | 300 | 1296 + +## License + +iNaturalist is licensed under a Creative Commons Attribution Non-Commercial 4.0 +License (CC-BY-NC). You may obtain a copy of the CC-BY-NC license at +https://creativecommons.org/licenses/by-nc/4.0/deed.en. You can obtain a copy of +the dataset at: +https://www.gbif.org/dataset/50c9509d-22c7-4a22-a47d-8c48425ef4a7#description. +This version of the dataset has been modified as described above. + +For the dataset citation, please see the “References” section below. + +## References + +1. iNaturalist contributors, iNaturalist Research-grade Observations, + iNaturalist.org (2025); https://doi.org/10.15468/ab3s5x diff --git a/raw/deepmind_eval/evaluation/us_trees/us_trees.png b/raw/deepmind_eval/evaluation/us_trees/us_trees.png new file mode 100644 index 0000000000000000000000000000000000000000..1a4ecae8469f7dd393e3bc2ce289dcc849641184 --- /dev/null +++ b/raw/deepmind_eval/evaluation/us_trees/us_trees.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5808fc83756517ec96d9be6c711488cb18085cc23fafeb122143c81e1ceff1a +size 80804 diff --git a/raw/deepmind_eval/evaluation/us_trees/us_trees_trial_groups.json b/raw/deepmind_eval/evaluation/us_trees/us_trees_trial_groups.json new file mode 100644 index 0000000000000000000000000000000000000000..0e67fd873d419a452158ad4550bf6cf5469b8942 --- /dev/null +++ b/raw/deepmind_eval/evaluation/us_trees/us_trees_trial_groups.json @@ -0,0 +1,87 @@ +[ + { + "id": "us_trees_knn1_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { + "k": 1, + "topk_accuracy": [3] + } + }, + { + "id": "us_trees_knn1_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { + "k": 1, + "topk_accuracy": [3] + } + }, + { + "id": "us_trees_knn1_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { + "k": 1, + "topk_accuracy": [3] + } + }, + { + "id": "us_trees_knn3_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { + "k": 3, + "topk_accuracy": [3] + } + }, + { + "id": "us_trees_knn3_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "knn", + "metrics_type": "classification", + "model_config": { + "k": 3, + "topk_accuracy": [3] + } + }, + { + "id": "us_trees_linear_1000x1", + "repeats_n": 1000, + "samples_n": 1, + "model_type": "linear", + "metrics_type": "classification", + "model_config": { + "topk_accuracy": [3] + } + }, + { + "id": "us_trees_linear_500x10", + "repeats_n": 500, + "samples_n": 10, + "model_type": "linear", + "metrics_type": "classification", + "model_config": { + "topk_accuracy": [3] + } + }, + { + "id": "us_trees_linear_1x300", + "repeats_n": 1, + "samples_n": 300, + "model_type": "linear", + "metrics_type": "classification", + "model_config": { + "topk_accuracy": [3] + } + } +] diff --git a/raw/ecoregions2017/Ecoregions2017.zip b/raw/ecoregions2017/Ecoregions2017.zip new file mode 100644 index 0000000000000000000000000000000000000000..1c2bbc754ec324f75bbdf7f6dae3a938b0bc3cb0 --- /dev/null +++ b/raw/ecoregions2017/Ecoregions2017.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be36d6209e443038d02e309f0447c6e7f2a62f5fe60c605ffe90d064952f2a60 +size 149248653 diff --git a/raw/sustainbench/dhs_trainval_labels.csv b/raw/sustainbench/dhs_trainval_labels.csv new file mode 100644 index 0000000000000000000000000000000000000000..f2f78fabfc19758ef35f163a48cabe81878b9998 --- /dev/null +++ b/raw/sustainbench/dhs_trainval_labels.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fcd6db1b9ec2891fb8e82d1a5bb00bec30f09b6748d555eb787b4c5e105899cb +size 31621430 diff --git a/raw/usavars/usavars_roads.csv b/raw/usavars/usavars_roads.csv new file mode 100644 index 0000000000000000000000000000000000000000..b1a63524608c428f967ea2252124884e25523fdf --- /dev/null +++ b/raw/usavars/usavars_roads.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6636c0c4044a2c03404f57690530be714776db6f120f360918cf1c311255d953 +size 13242388