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

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.

Data and Resources

Additional Info

Field Value
Maintainer undefined
Last Updated March 8, 2021, 02:46 (EST)
Created March 8, 2021, 02:46 (EST)
Identifier C1648035940-SEDAC
Issued 2019-10-10
Language {en-US}
Modified 2019-10-10
Theme {URBANSPATIAL,geospatial}
accessLevel public
bureauCode {026:00}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_@id https://data.nasa.gov/data.json
catalog_conformsTo 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. 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
encoding utf8
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_url http://catalog.data.gov/dataset/307b7c09-4e6a-49bb-b2e2-26f334425f8d
landingPage https://doi.org/10.7927/a49b-sm16
metadata_type geospatial
programCode {026:001}
publisher SEDAC
references {https://doi.org/10.3390/rs11101247}
release-place Palisades, NY
resource-type Dataset
source_datajson_identifier true
source_hash 291b4bc89d2fb90140d2f7b9096a2a94795dbaaa
source_schema_version 1.1
spatial -180.0 -56.0 180.0 84.0
temporal 2015-01-01T00:00:00Z/2015-12-31T00:00:00Z