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Remote Sens. 2015, 7(3), 2334-2351; doi:10.3390/rs70302334

Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images

1
Department of Agricultural and Applied Economics, Virginia Tech, 208 Hutcheson Hall (0401), 250 Drillfield Drive, Blacksburg, VA 24060, USA
2
Department of Economics, Virginia Tech, 3016 Pamplin Hall (0316), Blacksburg, VA 24061, USA
3
Department of Forest Resources and Environmental Conservation, Virginia Tech, 310 West Campus Dr., Blacksburg, VA 24061, USA
4
Virginia Tech Information Technology, 1700 Pratt Drive (0214), Blacksburg, VA 24061, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Chandra Giri and Prasad S. Thenkabail
Received: 15 December 2014 / Revised: 19 January 2015 / Accepted: 10 February 2015 / Published: 26 February 2015
View Full-Text   |   Download PDF [935 KB, uploaded 26 February 2015]   |  

Abstract

We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is insensitive to wage, but deteriorates with the complexity of images and with time-on-task. In most instances, human interpretation of cloud impacted area using a majority rule was more accurate than an automated algorithm (Fmask) commonly used to identify clouds and cloud shadows. However, cirrus-impacted pixels were better identified by Fmask than by human interpreters. Crowd-sourced interpretation of cloud impacted pixels appears to be a promising means by which to augment or potentially validate fully automated algorithms. View Full-Text
Keywords: cloud interpretation; satellite images; Mechanical Turk; economic experiment cloud interpretation; satellite images; Mechanical Turk; economic experiment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yu, L.; Ball, S.B.; Blinn, C.E.; Moeltner, K.; Peery, S.; Thomas, V.A.; Wynne, R.H. Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images. Remote Sens. 2015, 7, 2334-2351.

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