Geography
Document Type
Article
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, particularly for discrete cover types, but the quality and representativeness of crowdsourced data is hard to verify. We present an open-sourced platform, DIYlandcover, that serves representative samples of high resolution imagery to an online job market, where workers delineate individual landcover features of interest. Worker mapping skill is frequently assessed, providing estimates of overall map accuracy and a basis for performance-based payments. A trial of DIYlandcover showed that novice workers delineated South African cropland with 91% accuracy, exceeding the accuracy of current generation global landcover products, while capturing important geometric data. A scaling-up assessment suggests the possibility of developing an Africa-wide vector-based dataset of croplands for $2-3 million within 1.2-3.8 years. DIYlandcover can be readily adapted to map other discrete cover types.
Publication Title
Environmental Modelling and Software
Publication Date
6-1-2016
Volume
80
First Page
41
Last Page
53
ISSN
1364-8152
DOI
10.1016/j.envsoft.2016.01.011
Keywords
accuracy assessment, crowd-sourcing, landcover, object extraction, remote sensing, representative sampling
Repository Citation
Estes, Lyndon; McRitchie, D.; Choi, J.; Debats, S.; Evans, T.; Guthe, W.; Luo, D.; Ragazzo, G.; Zempleni, R.; and Caylor, K. K., "A platform for crowdsourcing the creation of representative, accurate landcover maps" (2016). Geography. 73.
https://commons.clarku.edu/faculty_geography/73
Copyright Conditions
The available download of this work is a preprint copy.
