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.

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Maintainer undefined
Last Updated July 17, 2025, 14:47 (UTC)
Created April 23, 2025, 22:41 (UTC)
accessLevel public
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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 e098d9b0-cf82-4985-a50e-eda68d600647
harvest_source_id 61638e72-b36c-4866-9d28-551a3062f158
harvest_source_title DNG Legacy Data
identifier C1648035940-SEDAC
issued 2019-10-10
language {en-US}
metadata_type geospatial
modified 2019-10-10
programCode {026:001}
publisher SEDAC
references {https://doi.org/10.3390/rs11101247}
release-place Palisades, NY
resource-type Dataset
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source_hash 6501b1f1342ef7239f9551abeb5322567659f18db9508abb5023e17b43f3a332
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
theme {URBANSPATIAL,geospatial}