samapriya/awesome-gee-community-datasets: Community Catalog
Bibliographic record
Abstract
The awesome-gee-community-catalog consists of community-sourced geospatial datasets made available for use by the larger Google Earth Engine community and shared publicly as Earth Engine assets. The project was started with the idea that a lot of research datasets are often unavailable for direct use and require preprocessing before use. This catalog lives and serves alongside the Google Earth Engine data catalog and also houses datasets often requested by the community under a variety of open licenses. Go to the catalog to explore more: https://gee-community-catalog.org You can read about the history and how this project started in the Medium Post article here Release frequency will be monthly for now Changelog : You can find the running changelog here Updated 2023-03-09 Release 1.0.5 for awesome gee community catalog Added new dataset Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) database Added new dataset Mismanaged Plastic Waste Dataset in Rivers Weekly updates to USDM drought monitor Added new dataset category Analysis Ready Data Updated 2023-03-01 Added new dataset High-Res water body dataset for tundra and boreal forests North America Added new dataset Global Soil bioclimatic variables Added new dataset GFSAD Global Cropland Extent Product (GCEP) Added new dataset GFSAD Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) Updated USA Structures to include NY and WI Updated High Resolution Settlement Layer Weekly updates to USDM drought monitor Updated 2023-02-07 Release 1.0.4 for awesome gee community catalog Added new dataset Canada Landsat Derived Forest harvest disturbance 1985-2020 Added new dataset Canada Landsat Derived Forest fire disturbance 1985-2020 Added new dataset for insiders program EOG Annual VIIRS Night Time Light (2013-2021) Added new dataset for insiders program swissSURFACE3D Raster Digital Surface Model (DSM) Weekly updates to USDM drought monitor Mapbiomas updated Landsat tiles collections for collection 7 and general collection 7 updates
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.249 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".