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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| Spatial Extent: | Bounding Rectangle: N: 84.0 S: -56.0 E: 180.0 W: -180.0 |
Data Format(s): | Distribution: GeoTIFF, PDF, PNG, WMS |
| Temporal Extent: | Platform(s): | SUOMI-NPP, Terra | |
| Data Center(s): | SEDAC | Instrument(s): | VIIRS, MODIS |
| Version: | 1.0 | ||
- Title: 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods
- Creator: Center for International Earth Science Information Network - CIESIN - Columbia University
- Publisher: NASA Socioeconomic Data and Applications Center (SEDAC)
- Release Date: 2019-10-10T00:00:00.000Z
- Linkage: https://doi.org/10.7927/a49b-sm16
- GENERAL DOCUMENTATION: https://sedac.ciesin.columbia.edu/data/set/urbanspatial-urban-extents-viirs-modis-us-2015/docsd, Documentation Page
Collection Information
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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. -
DOI
10.7927/a49b-sm16
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2
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Complete
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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.
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Title: Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data
Publication Date: 2019-05-27
Author(s): Liu, X., A. de Sherbinin and Y. Zhan.
Series: Remote Sensing
Volume: 11
Issue: 10
Pages: 1247
DOI: 10.3390/rs11101247
Online Resource: https://doi.org/10.3390/rs11101247
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Other Descriptive Keywords
ISO Topic Categories
- Society
Acquisition Information
Projects
| Project Short Name | Campaigns | Project Dates |
|---|---|---|
| URBANSPATIAL | No campaigns listed. | No dates provided. |
Temporal Information
Spatial Information
| Coverage Type | Zone Identifier | Geometry | Granule Representation |
|---|---|---|---|
| HORIZONTAL | CARTESIAN | CARTESIAN |
Spatial Information
Location Keywords
- CONTINENT
- NORTH AMERICA
- UNITED STATES OF AMERICA
Data Centers
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SEDAC
DISTRIBUTOR, ARCHIVERSocioeconomic Data and Applications Center
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Data Contacts
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CIESIN METADATA ADMINISTRATION
Metadata AuthorCIESIN METADATA ADMINISTRATION
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CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
Palisades, NY 10964
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SEDAC USER SERVICES
Technical ContactSEDAC USER SERVICES
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CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
Palisades, NY 10964
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SEDAC USER SERVICES
Data Center ContactSEDAC USER SERVICES
SEDAC-
CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
Palisades, NY 10964
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Format: GeoTIFF
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