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

Data:

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)†
RIS (Others)

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)†
RIS (Others)

* When authors make use of data they should cite both the data set and the scientific publication, if available. Such a practice gives credit to data set producers and advances principles of transparency and reproducibility. Please visit the data citations page for details. Users who would like to choose to format the citation(s) for this dataset using a myriad of alternate styles can copy the DOI number and paste it into Crosscite's website.

† For EndNote users, please check the Research Note field for issues with importing authors that are organizations when using the ENW file format.

The data are available for download in GeoTIFF (.tif) format here [645 KB zip file].