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    • 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods

    Metadata Updated: December 6, 2023

    The 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods data set models urban settlements in the Continental United States (CONUS) as of 2015. When applied to the combination of daytime spectral and nighttime lights satellite data, the machine learning methods achieved high accuracy at an intermediate-resolution of 500 meters at large spatial scales. The input data for these models were two types of satellite imagery: Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) data from the Day/Night Band (DNB), and Moderate Resolution Imaging Spectroradiometer (MODIS) corrected daytime Normalized Difference Vegetation Index (NDVI). Although several machine learning methods were evaluated, including Random Forest (RF), Gradient Boosting Machine (GBM), Neural Network (NN), and the Ensemble of RF, GBM, and NN (ESB), the highest accuracy results were achieved with NN, and those results were used to delineate the urban extents in this data set.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

    Downloads & Resources

    References

    https://doi.org/10.3390/rs11101247

    Dates

    Metadata Created Date November 12, 2020
    Metadata Updated Date December 6, 2023

    Metadata Source

    Harvested from NASA Data.json

    Other Data Resources

    Graphic Preview

    Maps Download Page

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date November 12, 2020
    Metadata Updated Date December 6, 2023
    Publisher SEDAC
    Maintainer
    Identifier C1648035940-SEDAC
    Data First Published 2019-10-10
    Language en-US
    Data Last Modified 2019-10-10
    Category URBANSPATIAL, geospatial
    Public Access Level public
    Bureau Code 026:00
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Metadata Catalog ID https://data.nasa.gov/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Citation Center for International Earth Science Information Network - CIESIN - Columbia University. 2019-10-10. 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods. Version 1.00. Palisades, NY. Archived by National Aeronautics and Space Administration, U.S. Government, NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/a49b-sm16. https://doi.org/10.7927/a49b-sm16.
    Creator Center for International Earth Science Information Network - CIESIN - Columbia University
    Graphic Preview Description Maps Download Page
    Graphic Preview File https://sedac.ciesin.columbia.edu/data/set/urbanspatial-urban-extents-viirs-modis-us-2015/maps
    Harvest Object Id 4a67d17e-9d72-44db-881f-25d0c8bede2a
    Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
    Harvest Source Title NASA Data.json
    Homepage URL https://doi.org/10.7927/a49b-sm16
    Metadata Type geospatial
    Old Spatial -180.0 -56.0 180.0 84.0
    Program Code 026:001
    Related Documents https://doi.org/10.3390/rs11101247
    Release Place Palisades, NY
    Source Datajson Identifier True
    Source Hash 3eac2b98d4c0cd88309c6786d7a1bb3164899bbd176cab0a8bc55e95e2c848a9
    Source Schema Version 1.1
    Spatial
    Temporal 2015-01-01T00:00:00Z/2015-12-31T00:00:00Z

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