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Awesome GEE Community Datasets

repository·master·Indexed 22 days ago

https://github.com/samapriya/awesome-gee-community-datasets

A community-driven catalog of geospatial datasets shared as public Google Earth Engine (GEE) assets. Designed to complement the official Google Earth Engine data catalog, it provides research-grade data to reduce manual preprocessing. The project includes a visual browser for data discovery, a community forum for collaboration, and guidelines for contributing new datasets and tutorials.

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What's inside awesome-gee-community-datasets

  1. Overview of the Geocoded Disasters (GDIS) Dataset

    master

    The GDIS dataset is a geocoded extension of the CRED Emergency Events Database (EM-DAT).

    Key Details:

    • Temporal Coverage: 1960 to 2018.
    • Spatial Resolution: Varies from administrative level 1 (state/province/region) to administrative level 3 (district/commune/village) based on GADM 2018.
    • Disaster Types: Floods, storms (typhoons, monsoons, etc.), earthquakes, landslides, droughts, volcanic activity, and extreme temperatures.
    • Data Source: NASA Socioeconomic Data and Applications Center (SEDAC).
    • License: Creative Commons Attribution 4.0 International License (CC BY 4.0).

  2. Overview of Canada Forest Canopy Height Datasets

    master

    This project provides two spatially continuous canopy height maps at 250m resolution for Canadian forested ecosystems, created using random forest regression models that combine LiDAR observations with ancillary variables (Sentinel-1, Sentinel-2, and PALSAR-2).

    Comparison of GEDI vs ICESat-2

    • GEDI Map: Generally more accurate for vegetation information. It has a mean difference (MD) of 0.9 m and an RMSE of 4.2 m relative to ALS validation data.
    • ICESat-2 Map: Provides better coverage for tall canopy heights in hemi-boreal forests where GEDI and ALS coverage is limited. It has a mean difference (MD) of 2.9 m and an RMSE of 5.2 m relative to ALS validation data.

    Dataset Details

    • Spatial Resolution: 250m
    • Temporal Context: Data derived from mid-growing season (June and August 2020).
    • License: Creative Commons BY-4.0
    • External Download: 4TU.ResearchData

  3. Overview of the GEE Community Catalog

    master

    The awesome-gee-community-catalog is an open-source, grassroots project dedicated to collecting and making community-sourced and community-generated geospatial datasets accessible. It is designed to complement the Google Earth Engine data catalog by providing a centralized repository for community-driven data, aiming to reduce the digital divide in geospatial analysis.

    Key Resources

    • Browse the Catalog: Access the collection of community datasets via the official website.
    • Community Forum: Connect with other Google Earth Engine (GEE) users, ask questions, and share insights at forum.gee-community-catalog.org.
    • Stay Updated: Sign up for email updates via the project's Substack to receive the latest catalog news.

  4. Overview of GPWv4 Administrative Unit Center Points

    master

    The GPWv4 Administrative Unit Center Points dataset provides vector (point) representations of administrative units used in the Gridded Population of the World, Version 4.

    Key Data Components:

    • Population Estimates & Densities: Available for the years 2000, 2005, 2010, 2015, and 2020 (based on UN WPP adjustments).
    • Demographics: Age and sex characteristics for the year 2010.
    • Administrative Metadata: Includes administrative names, land and water area, and data context for each centroid location.

    Purpose: Designed for data integration by providing a point-based version of administrative units with associated population and demographic data.

  5. Overview of awesome-gee-community-datasets

    master
    The awesome-gee-community-datasets project provides a catalog of community-sourced geospatial datasets that are shared publicly as Google Earth Engine (GEE) assets. These datasets are intended to reduce the need for manual preprocessing by making research-grade data directly available for use within the Google Earth Engine ecosystem. The catalog serves as a supplement to the official Google Earth Engine data catalog.

  6. Overview of Global Monthly Satellite-derived PM2.5 Dataset

    master

    The Global Monthly Satellite-derived PM2.5 dataset provides estimates of ground-level fine particulate matter (PM2.5) from 2000 to 2022 (in version V6.GL.02).

    Data Characteristics

    • AOD Sources: MODIS, MISR, SeaWIFS, and VIIRS.
    • Methodology: Integrates Aerosol Optical Depth (AOD) retrievals with the GEOS-Chem chemical transport model, calibrated using a residual Convolutional Neural Network (CNN) against global ground-based observations.
    • Format: NetCDF (.nc) for direct downloads; gridded files in Google Earth Engine use the WGS84 projection.
    • Resolution: High-resolution datasets are provided at 0.01° × 0.01°.
    • License: Creative Commons Attribution 4.0 International.

    Version V6.GL.02 Updates

    • Updated ground-based observations for the entire time series.
    • Inclusion of SNPP VIIRS retrievals.
    • Extended temporal coverage through 2022.

  7. Overview of Global Natural and Planted Forests dataset

    master

    The Global Natural and Planted Forests dataset provides a high-resolution (30-meter) global map as of 2021 that distinguishes natural forests from planted (artificial) forests.

    Key Specifications

    • Resolution: 30 meters
    • Temporal Coverage: Map output is for 2021 (training data used Landsat imagery from 1985–2021).
    • Classification Logic: Uses a time-series change detection method based on disturbance frequency via a locally adaptive random forest (RF) classifier.
    • Accuracy: 85% overall accuracy validated against independent reference data.
    • Visual Representation:
      • Green: Natural forests
      • Yellow: Planted (artificial) forests
      • Other colors: Non-forest areas

    Data Sources and Citation

  8. Overview of the Global Fungi Database

    master

    The Global Fungi Database is an extensive atlas of global fungal distribution, compiled from high-throughput-sequencing metabarcoding studies. It contains over 600 million observations of fungal sequences (ITS1 and ITS2) derived from more than 17,000 samples across 178 original studies.

    Key Details:

    • Primary Interface: https://globalfungi.com
    • Data Content: Millions of unique nucleotide sequences of fungal internal transcribed spacers (ITS) 1 and 2.
    • Taxonomy: Based on UNITE version 8.2.
    • License: Creative Commons Attribution 4.0 International License.
    • Use Case: Facilitating the integration of third-party data to explore fungal biogeography and environmental drivers.

  9. Overview of US EPA Total Deposition Layers (TDEP Layers)

    master
    The US EPA Total Deposition Layers (TDEP Layers) dataset provides estimates of total nitrogen and sulfur deposition fluxes across the United States. These estimates are used for critical loads and ecological assessments, specifically regarding ecosystem acidification and eutrophication. The dataset combines wet and dry deposition contributions.

  10. Access the Peat-ML Global Peatland Fractional Coverage dataset

    master

    The Peat-ML dataset provides a spatially continuous global map of peatland fractional coverage. It was generated using machine learning models trained on climate, geomorphological, soil data, and remotely-sensed vegetation indices.

    Dataset Details:

    • Format: NetCDF
    • Release Year: 2021
    • Accuracy: R² = 0.73, RMSE = 9.11%, MBE = -0.36%
    • License: Creative Commons Attribution 4.0

    You can download the original NetCDF files from Zenodo: https://zenodo.org/records/7352284.

  11. Browse datasets by thematic groups

    master

    Datasets in the Awesome GEE Community Catalog are organized into thematic groups to improve findability. Users can explore datasets based on specific research domains. Common thematic groups include:

    • Population and Socioeconomic Datasets: Demographics, economic activities, and social indicators.
    • Hydrology Datasets: Water bodies, hydrological cycles, and water quality.
    • Global Land Use and Land Cover Datasets: Land use patterns and land cover changes over time.
    • Climate and Weather Datasets: Historical and real-time weather patterns, temperature, and precipitation.

    Note: Some datasets may belong to multiple categories. If your research spans different areas, it is recommended to explore multiple themes.

  12. Global Aridity Index dataset details and citation

    master

    The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 3 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period. It is based on the implementation of a Penman Monteith Evapotranspiration equation for reference crop and follows the development of WorldClim 2.1.

    License: CC BY 4.0 Attribution 4.0 International (Non-commercial use).

    Citations: