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A crowdsourced global data set for validating built-up surface layers.

Authors :
See, Linda
Georgieva, Ivelina
Duerauer, Martina
Kemper, Thomas
Corbane, Christina
Maffenini, Luca
Gallego, Javier
Pesaresi, Martino
Sirbu, Flavius
Ahmed, Rekib
Blyshchyk, Kateryna
Magori, Brigitte
Blyshchyk, Volodymyr
Melnyk, Oleksandr
Zadorozhniuk, Roman
Mandici, Marian-Traian
Su, Yuan-Fong
Rabia, Ahmed Harb
Pérez-Hoyos, Ana
Vasylyshyn, Roman
Source :
Scientific Data; 1/20/2022, Vol. 9 Issue 1, p1-14, 14p
Publication Year :
2022

Abstract

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas. Measurement(s) built-up areas Technology Type(s) visual interpretation of satellite imagery Factor Type(s) geographic location Sample Characteristic - Environment land • area of developed space Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17068160 [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20524463
Volume :
9
Issue :
1
Database :
Complementary Index
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
154792816
Full Text :
https://doi.org/10.1038/s41597-021-01105-4