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Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods, v1 (2015)
- To provide representations of urban areas in the Continental U.S. in the year 2015 to support sustainable urban development planning in accordance with the growth of urban areas.
- 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.
- Recommended Citation(s)*:
Center for International Earth Science Information Network - CIESIN - Columbia University. 2019. 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/a49b-sm16. Accessed DAY MONTH YEAR.
ENW (EndNote & RefWorks)†
Liu, X., A. de Sherbinin and Y. Zhan. 2019. Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI. Remote Sensing 11(10): 1247. https://doi.org/10.3390/rs11101247.
ENW (EndNote & RefWorks)†
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- Available Formats:
- raster, map, map service