2020
DOI: 10.3390/rs12172832
|View full text |Cite
|
Sign up to set email alerts
|

Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform

Abstract: Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining multi-dimensional features derived from Sentinel-1/2 images, Visible Infrared Imaging Radiometer Suite supporting a Day-Night Band (VIIRS-DNB) dataset, and Digital Elevation Model (DEM) data u… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
13
0
1

Year Published

2020
2020
2025
2025

Publication Types

Select...
8
1
1

Relationship

0
10

Authors

Journals

citations
Cited by 32 publications
(15 citation statements)
references
References 57 publications
0
13
0
1
Order By: Relevance
“…To date, GEE has been widely used for different mapping purposes; primarily to exploit its massive catalogue of images to capture time-series data or derive useful information to analyse phenomena over a long period. The capabilities of GEE, jointly with external software and applications, have been widely explored using different data and algorithms for a wide range of applications, such as forest mapping [26,54,55], LU/LC classification [56,57], fire severity analysis [58], forest disturbance mapping [59], forest defoliation assessment [60], surface water detection [61], mine mapping [62], snow [63] and shoreline detection [64], urban and rural settlement mapping [65,66], and species habitat monitoring [67].…”
Section: Introductionmentioning
confidence: 99%
“…To date, GEE has been widely used for different mapping purposes; primarily to exploit its massive catalogue of images to capture time-series data or derive useful information to analyse phenomena over a long period. The capabilities of GEE, jointly with external software and applications, have been widely explored using different data and algorithms for a wide range of applications, such as forest mapping [26,54,55], LU/LC classification [56,57], fire severity analysis [58], forest disturbance mapping [59], forest defoliation assessment [60], surface water detection [61], mine mapping [62], snow [63] and shoreline detection [64], urban and rural settlement mapping [65,66], and species habitat monitoring [67].…”
Section: Introductionmentioning
confidence: 99%
“…The generated samples of 4500 points are “real” and are used for the classification and subsequent classification accuracy evaluation according to the 7:3 ratio [ 50 , 52 ] of training samples to test samples. We selected four kinds of accuracy evaluation indexes used in many studies to evaluate accuracy: overall accuracy, the Kappa coefficient, producer accuracy and user accuracy [ 52 , 53 ].…”
Section: Methodsmentioning
confidence: 99%
“…In contrast, the population datasets developed by the GHSL project of the European Commission based on the global human settlement areas extracted from multi-275 scale textures and morphological features are transparent and freely available. The built-up area in GHSL was built by combining the MODIS 500 Urban Land Cover (MODIS500) and the LandScan 2010 population layer and are among the best-known binary products based on remote sensing (Ji et al, 2020). Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing of Earth observation data (Lu et al, 2013;Pesaresi et al, 2016).…”
Section: Review/selection Of Ancillary Remote Sensing Data For Dasymementioning
confidence: 99%