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.
Science Keywords: |
|
||
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 |
10.7927/a49b-sm16
Creation | |
Last Revision |
2
Complete
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.
None
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
Project Short Name | Campaigns | Project Dates |
---|---|---|
URBANSPATIAL | No campaigns listed. | No dates provided. |
Coverage Type | Zone Identifier | Geometry | Granule Representation |
---|---|---|---|
HORIZONTAL | CARTESIAN | CARTESIAN |
Geodetic Model:
Datum Name | Ellipsoid Name | Semi Major Axis | Flattening Ratio Denominator |
---|---|---|---|
WGS84 | WGS84 | 6378137.0 | 298.257224 |
0.00833 Not provided
0.00833 Not provided
This data center does not have any addresses listed.
This data center does not have any contact mechanisms listed.
CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
Palisades,
NY
10964
CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
Palisades,
NY
10964
CIESIN, Columbia University, 61 Route 9W, P.O. Box 1000
Palisades,
NY
10964
Format: GeoTIFF
Format Type: Native
Fees: 0
Format: PDF
Format Type: Native
Fees: 0
Format: PNG
Format Type: Native
Fees: 0
Format: WMS
Format Type: Native
Fees: 0