DIYlandcover: Crowdsourcing the creation of systematic, accurate landcover maps

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1 Woodrow Wilson School, Princeton University, Princeton, New Jersey, USA
2 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
3 Computational Science and Engineering Support, Office of Information Technology, Princeton University, Princeton, New Jersey, USA
4 Department of Geography, Indiana University, Bloomington, Indiana, USA
DOI
10.7287/peerj.preprints.1030v1
Published
Accepted
Subject Areas
Agricultural Science, Ecology, Environmental Sciences, Human-Computer Interaction, Coupled Natural and Human Systems
Keywords
remote sensing, landcover, crowd-sourcing, accuracy assessment, representative sampling
Copyright
© 2015 Estes et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Estes L, McRitchie D, Choi J, Debats SR, Evans T, Guthe W, Luo D, Ragazzo G, Zempleni R, Caylor K. 2015. DIYlandcover: Crowdsourcing the creation of systematic, accurate landcover maps. PeerJ PrePrints 3:e1030v1

Abstract

Accurate landcover maps are fundamental to understanding socio-economic and environmental patterns and processes, but existing datasets contain substantial errors. Crowdsourcing map creation may substantially improve accuracy, but the quality and representativeness of crowdsourced data is hard to verify. We present an open-sourced platform, DIYlandcover, that systematically serves samples of high resolution imagery to an online job market, where workers delineate landcover features of interest. Worker mapping skill is frequently assessed, providing estimates of overall map accuracy and a basis for performance-based payments. An initial trial of DIYlandcover showed that novice workers delineated South African cropland with 91% accuracy, which exceeds current generation global landcover products, while capturing important field geometry data. Given worker payment costs, large area, wall-to-wall mapping may be cost prohibitive, but a potentially promising use of DIYlandcover is to iteratively train and test emerging computer vision algorithms adapted for landcover mapping.

Author Comment

This manuscript has been submitted to another journal for review.

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