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 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

Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data

Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the accuracy with which they can label cases. Here, the potential to increase the accuracy of crowdsourced data on land cover identified from satellite remote sensor images through the use of weighted voting strategies is explored. Critically, the information used to weight contributions based on the accuracy with which a contributor labels cases of a class and the relative abundance of class are inferred entirely from the contributed data only via a latent class analysis. The results show that consensus approaches do yield a classification that is more accurate than that achieved by any individual contributor. Here, the most accurate individual could classify the data with an accuracy of 73.91% while a basic consensus label derived from the data provided by all seven volunteers contributing data was 76.58%. More importantly, the results show that weighting contributions can lead to a statistically significant increase in the overall accuracy to 80.60% by ignoring the contributions from the volunteer adjudged to be the least accurate in labelling

Analysing Slavery through Satellite Technology: How Remote Sensing Could Revolutionise Data Collection to Help End Modern Slavery

An estimated 40.3 million people are enslaved globally across a range of industries. Whilst these industries are known, their scale can hinder the fight against slavery. Some industries using slave labour are visible in satellite imagery, including mining, brick kilns, fishing and shrimp farming. Satellite data can provide supplementary details for large scales which cannot be easily gathered on the ground. This paper reviews previous uses of remote sensing in the humanitarian and human rights sectors and demonstrates how Earth Observation as a methodology can be applied to help achieve the United Nations Sustainable Development Goal target 8.7

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Slavery from space: an analysis of the modern slavery-environmental degradation nexus using remote sensing data

Modern slavery has been connected to degradation of the environment, and has been found to contribute to anthropogenic climate change. Three sectors have been investigated using satellite Earth Observation (EO) data in order to provide a unique insight into the modern slavery-environmental degradation nexus. Remote sensing affords a unique ability to measure and understand these ecological changes over large timescales, and vast geographical areas. A local, regional, and global assessment of sectors known to heavily use modern slavery practices within their workforce has been undertaken using a variety of remotely sensed data sources and products. Fish-processing, brick kilns, and tree loss associated with multiple sectors, have all been analysed. Levels of environmental damage in the affected sectors have been noted, and measured using satellite EO data. These effects have included: tree loss of mangroves and tropical forests for fish-processing camps and oil palm plantations; the emission of pollutants which contribute to atmospheric climate change; the extraction of resources, such as groundwater and good-quality topsoil; and changes to landcover and land-use in areas that are important for production of food and economic support for large populations. Over the course of this investigation, ten post-harvest fish-processing camps have been located, and the first replicable methodology for estimating the number of brick kilns in the South Asian ‘Brick Belt’ region has been provided – where open access satellite EO data enabled the estimation of 55,387 brick kilns. The latter has since enabled machine learning methodologies to provide accurate locations and kiln ages which have assisted in the environmental assessment of this large-scale transnational industry. Furthermore, if modern slavery practices were eliminated from this industry, the environmental impact of the brick-making could be reduced by the equivalent of almost 10,000 kilns. Finally, tree loss has been quantified and the policy implications of deforestation and forest degradation as a result of modern slavery have been explored in four countries. Ultimately, there are a large variety of environmentally degrading activities known to use modern slavery practices that may be explored using satellite EO data. Remote sensing throughout this thesis has enabled the exploration of these implications for some sectors, and proved the proof of concept that additional data acquisition from remotely sensed sources, can support in the overall goal of assisting in the understanding and eradication of modern slavery. Satellite EO is an underutilised methodology within the antislavery community and, as shown within this thesis, there is the power to investigate the environmental implications of these sectors which have had numerous documented cases of modern slavery. In order to achieve the Sustainable Development Goals (SDGs) – particularly target 8.7 which aims to end modern slavery by 2030 – multiple avenues of investigation are required to understand, locate, and eradicate modern slavery. Applying remote sensing to assess the ecological impact of these cases is one such avenue that can provide information to assist in this achievement, and support the success of multiple SDGs. The author would like to acknowledge that they have written the thesis from the starting point of being a non-survivor

Slavery from space: an analysis of the modern slavery-environmental degradation nexus using remote sensing data

Modern slavery has been connected to degradation of the environment, and has been found to contribute to anthropogenic climate change. Three sectors have been investigated using satellite Earth Observation (EO) data in order to provide a unique insight into the modern slavery-environmental degradation nexus. Remote sensing affords a unique ability to measure and understand these ecological changes over large timescales, and vast geographical areas. A local, regional, and global assessment of sectors known to heavily use modern slavery practices within their workforce has been undertaken using a variety of remotely sensed data sources and products. Fish-processing, brick kilns, and tree loss associated with multiple sectors, have all been analysed. Levels of environmental damage in the affected sectors have been noted, and measured using satellite EO data. These effects have included: tree loss of mangroves and tropical forests for fish-processing camps and oil palm plantations; the emission of pollutants which contribute to atmospheric climate change; the extraction of resources, such as groundwater and good-quality topsoil; and changes to landcover and land-use in areas that are important for production of food and economic support for large populations. Over the course of this investigation, ten post-harvest fish-processing camps have been located, and the first replicable methodology for estimating the number of brick kilns in the South Asian ‘Brick Belt’ region has been provided – where open access satellite EO data enabled the estimation of 55,387 brick kilns. The latter has since enabled machine learning methodologies to provide accurate locations and kiln ages which have assisted in the environmental assessment of this large-scale transnational industry. Furthermore, if modern slavery practices were eliminated from this industry, the environmental impact of the brick-making could be reduced by the equivalent of almost 10,000 kilns. Finally, tree loss has been quantified and the policy implications of deforestation and forest degradation as a result of modern slavery have been explored in four countries. Ultimately, there are a large variety of environmentally degrading activities known to use modern slavery practices that may be explored using satellite EO data. Remote sensing throughout this thesis has enabled the exploration of these implications for some sectors, and proved the proof of concept that additional data acquisition from remotely sensed sources, can support in the overall goal of assisting in the understanding and eradication of modern slavery. Satellite EO is an underutilised methodology within the antislavery community and, as shown within this thesis, there is the power to investigate the environmental implications of these sectors which have had numerous documented cases of modern slavery. In order to achieve the Sustainable Development Goals (SDGs) – particularly target 8.7 which aims to end modern slavery by 2030 – multiple avenues of investigation are required to understand, locate, and eradicate modern slavery. Applying remote sensing to assess the ecological impact of these cases is one such avenue that can provide information to assist in this achievement, and support the success of multiple SDGs. The author would like to acknowledge that they have written the thesis from the starting point of being a non-survivor