September 2019

Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods, v1 (2015)

PURPOSE

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.


DESCRIPTION

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.

The VIIRS NTL is provided by NOAA, while the MODIS NDVI is available via the Google Earth Engine. 


ACCESSING THE DATA

The data may be downloaded at https://sedac.ciesin.columbia.edu/data/set/urbanspatial-urban-extents-viirs-modis-us-2015/data-download


DATA FORMAT

This archive contains GIS data in the GeoTIFF format.

The data file is a compressed zipfile. Downloaded files need to be uncompressed in a single folder using either WinZip (Windows file compression utility) or similar application. Users should expect an increase in the size of downloaded data after decompression. 


DATA VALUES

Data values are binary where 0 is Non-Urban Extent and 1 is Urban Extent.


SPATIAL EXTENT

Continental United States (CONUS) at 500 meter resolution.

Bounding Box:
West  -124.732769    East  -66.969271 
North  49.371730    South  24.956376 

The data are provided in the World Geodetic System 1984 (WGS84) Geographic Coordinate System.


DISCLAIMER

CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.


USE CONSTRAINTS

This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.


RECOMMENDED CITATION(S)

Data Set:

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, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/a49b-sm16. Accessed DAY MONTH YEAR.

Larger Work Publication: 

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 Data. Remote Sensing 11(10): 1247. https://doi.org/10.3390/rs11101247.