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Remote Sens. 2019, 11(3), 266; https://doi.org/10.3390/rs11030266

Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space

1
School of Geography, University of Nottingham, Nottingham NG7 2RD, UK
2
The Rights Lab, Highfield House, University of Nottingham, Nottingham NG7 2RD, UK
3
Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
*
Author to whom correspondence should be addressed.
Received: 14 January 2019 / Accepted: 24 January 2019 / Published: 29 January 2019
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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Abstract

A large proportion of the workforce in the brick kilns of the Brick Belt of Asia are modern-day slaves. Work to liberate slaves and contribute to UN Sustainable Development Goal 8.7 would benefit from maps showing the location of brick kilns. Previous work has shown that brick kilns can be accurately identified and located visually from fine spatial resolution remote-sensing images. Furthermore, via crowdsourcing, it would be possible to map very large areas. However, concerns over the ability to maintain a motivated crowd to allow accurate mapping over time together with the development of advanced machine learning methods suggest considerable potential for rapid, accurate and repeatable automated mapping of brick kilns. This potential is explored here using fine spatial resolution images of a region of Rajasthan, India. A contemporary deep-learning classifier founded on region-based convolution neural networks (R-CNN), the Faster R-CNN, was trained to classify brick kilns. This approach mapped all of the brick kilns within the study area correctly, with a producer’s accuracy of 100%, but at the cost of substantial over-estimation of kiln numbers. Applying a second classifier to the outputs substantially reduced the over-estimation. This second classifier could be visual classification, which, as it focused on a relatively small number of sites, should be feasible to acquire, or an additional automated classifier. The result of applying a CNN classifier to the outputs of the original classification was a map with an overall accuracy of 94.94% with both low omission and commission error that should help direct anti-slavery activity on the ground. These results indicate that contemporary Earth observation resources and machine learning methods may be successfully applied to help address slavery from space. View Full-Text
Keywords: slavery; mapping; crowdsourcing; object-based target detection; convolutional neural network slavery; mapping; crowdsourcing; object-based target detection; convolutional neural network
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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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Foody, G.M.; Ling, F.; Boyd, D.S.; Li, X.; Wardlaw, J. Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space. Remote Sens. 2019, 11, 266.

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