Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space
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 accuratel...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/11/3/266 |
id |
doaj-116a93284c914bb2a114ad3884528068 |
---|---|
record_format |
Article |
spelling |
doaj-116a93284c914bb2a114ad38845280682020-11-24T23:56:42ZengMDPI AGRemote Sensing2072-42922019-01-0111326610.3390/rs11030266rs11030266Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from SpaceGiles M. Foody0Feng Ling1Doreen S. Boyd2Xiaodong Li3Jessica Wardlaw4School of Geography, University of Nottingham, Nottingham NG7 2RD, UKKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, ChinaSchool of Geography, University of Nottingham, Nottingham NG7 2RD, UKSchool of Geography, University of Nottingham, Nottingham NG7 2RD, UKSchool of Geography, University of Nottingham, Nottingham NG7 2RD, UKA 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.https://www.mdpi.com/2072-4292/11/3/266slaverymappingcrowdsourcingobject-based target detectionconvolutional neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Giles M. Foody Feng Ling Doreen S. Boyd Xiaodong Li Jessica Wardlaw |
spellingShingle |
Giles M. Foody Feng Ling Doreen S. Boyd Xiaodong Li Jessica Wardlaw Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space Remote Sensing slavery mapping crowdsourcing object-based target detection convolutional neural network |
author_facet |
Giles M. Foody Feng Ling Doreen S. Boyd Xiaodong Li Jessica Wardlaw |
author_sort |
Giles M. Foody |
title |
Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space |
title_short |
Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space |
title_full |
Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space |
title_fullStr |
Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space |
title_full_unstemmed |
Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space |
title_sort |
earth observation and machine learning to meet sustainable development goal 8.7: mapping sites associated with slavery from space |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-01-01 |
description |
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. |
topic |
slavery mapping crowdsourcing object-based target detection convolutional neural network |
url |
https://www.mdpi.com/2072-4292/11/3/266 |
work_keys_str_mv |
AT gilesmfoody earthobservationandmachinelearningtomeetsustainabledevelopmentgoal87mappingsitesassociatedwithslaveryfromspace AT fengling earthobservationandmachinelearningtomeetsustainabledevelopmentgoal87mappingsitesassociatedwithslaveryfromspace AT doreensboyd earthobservationandmachinelearningtomeetsustainabledevelopmentgoal87mappingsitesassociatedwithslaveryfromspace AT xiaodongli earthobservationandmachinelearningtomeetsustainabledevelopmentgoal87mappingsitesassociatedwithslaveryfromspace AT jessicawardlaw earthobservationandmachinelearningtomeetsustainabledevelopmentgoal87mappingsitesassociatedwithslaveryfromspace |
_version_ |
1725457085486858240 |