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

2018 ◽  
Vol 4 (2) ◽  
pp. 169-199
Author(s):  
Bethany Jackson ◽  
Kevin Bales ◽  
Sarah Owen ◽  
Jessica Wardlaw ◽  
Doreen Boyd
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Human Rights ◽  
Earth Observation ◽  
Shrimp Farming ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Satellite Technology ◽  
Brick Kilns ◽  
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.

scholarly journals Informing action for United Nations SDG target 8.7 and interdependent SDGs: Examining modern slavery from space

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Doreen S. Boyd ◽  
Bertrand Perrat ◽  
Xiaodong Li ◽  
Bethany Jackson ◽  
Todd Landman ◽  
...  
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
South Asian ◽  
Sustainable Development Goals ◽  
Earth Observation ◽  
Spatiotemporal Patterns ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Modern Slavery ◽  
Development Goals

AbstractThis article provides an example of the ways in which remote sensing, Earth observation, and machine learning can be deployed to provide the most up to date quantitative portrait of the South Asian ‘Brick Belt’, with a view to understanding the extent of the prevalence of modern slavery and exploitative labour. This analysis represents the first of its kind in estimating the spatiotemporal patterns in the Bull’s Trench Kilns across the Brick Belt, as well as its connections with various UN Sustainable Development Goals (SDGs). With a principal focus on Sustainable Development Goal Target 8.7 regarding the effective measures to end modern slavery by 2030, the article provides additional evidence on the intersections that exist between SDG 8.7 and those relating to urbanisation (SDG 11, 12), environmental degradation and pollution (SDG 3, 14, 15), and climate change (SDG 13). Our findings are then used to make a series of pragmatic suggestions for mitigating the most extreme SDG risks associated with brick production in ways that can improve human lives and human freedom.

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

2019 ◽  
Vol 11 (3) ◽  
pp. 266 ◽  
Author(s):  
Giles Foody ◽  
Feng Ling ◽  
Doreen Boyd ◽  
Xiaodong Li ◽  
Jessica Wardlaw
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Spatial Resolution ◽  
Earth Observation ◽  
Sustainable Development Goal ◽  
Learning Methods ◽  
Development Goal ◽  
Machine Learning Methods ◽  
Brick Kilns ◽  
Fine Spatial Resolution

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.

scholarly journals The Determinants of Access to Sanitation: The Role of Human Rights and the Challenges of Measurement

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1676
Author(s):  
Rebecca Schiel ◽  
Bruce M. Wilson ◽  
Malcolm Langford
Keyword(s):  
Sustainable Development ◽  
Human Rights ◽  
Field Data ◽  
Data Availability ◽  
Regression Analyses ◽  
Human Right ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
The Sustainable Development

Ten years after the United Nation’s recognition of the human right to water and sanitation (HRtWS), little is understood about how these right impacts access to sanitation. There is limited identification of the mechanisms responsible for improvements in sanitation, including the international and constitutional recognition of rights to sanitation and water. We examine a core reason for the lack of progress in this field: data quality. Examining data availability and quality on measures of access to sanitation, we arrive at three findings: (1) where data are widely available, measures are not in line with the Sustainable Development Goal (SDG) targets, revealing little about changes in sanitation access; (2) data concerning safe sanitation are missing in more country-year observations than not; and (3) data are missing in the largest proportions from the poorest states and those most in need of progress on sanitation. Nonetheless, we present two regression analyses to determine what effect rights recognition has on improvements in sanitation access. First, the available data are too limited to analyze progress toward meeting SDGs related to sanitation globally, and especially in regions most urgently needing improvements. Second, utilizing more widely available data, we find that rights seem to have little impact on access.

scholarly journals Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

2020 ◽  
Vol 12 (10) ◽  
pp. 1634 ◽  
Author(s):  
Raha Hakimdavar ◽  
Alfred Hubbard ◽  
Frederick Policelli ◽  
Amy Pickens ◽  
Matthew Hansen ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Surface Water ◽  
Spatial Resolution ◽  
The Philippines ◽  
Earth Observation ◽  
Landsat 8 ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Data Products ◽  
Over Time

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

scholarly journals Debating medicalization of Female Genital Mutilation/Cutting (FGM/C): learning from (policy) experiences across countries

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Els Leye ◽  
◽  
Nina Van Eekert ◽  
Simukai Shamu ◽  
Tammary Esho ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Human Rights ◽  
Harm Reduction ◽  
Female Genital Mutilation ◽  
Public Discourse ◽  
The United States ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Genital Mutilation ◽  
Female Genital

Abstract Background Although Female Genital Mutilation/Cutting (FGM/C) is internationally considered a harmful practice, it is increasingly being medicalized allegedly to reduce its negative health effects, and is thus suggested as a harm reduction strategy in response to these perceived health risks. In many countries where FGM/C is traditionally practiced, the prevalence rates of medicalization are increasing, and in countries of migration, such as the United Kingdom, the United States of America or Sweden, court cases or the repeated issuing of statements in favor of presumed minimal forms of FGM/C to replace more invasive forms, has raised the debate between the medical harm reduction arguments and the human rights approach. Main body The purpose of this paper is to discuss the arguments associated with the medicalization of FGM/C, a trend that could undermine the achievement of Sustainable Development Goal 5.3. The paper uses four country case studies, Egypt, Indonesia, Kenya and UK, to discuss the reasons for engaging in medicalized forms of FGM/C, or not, and explores the ongoing public discourse in those countries concerning harm reduction versus human rights, and the contradiction between medical ethics, national criminal justice systems and international conventions. The discussion is structured around four key hotly contested ethical dilemmas. Firstly, that the WHO definition of medicalized FGM/C is too narrow allowing medicalized FGM to be justified by many healthcare professionals as a form of harm reduction which contradicts the medical oath of do no harm. Secondly, that medicalized FGM/C is a human rights abuse with lifelong consequences, no matter who performs it. Thirdly, that health care professionals who perform medicalized FGM/C are sustaining cultural norms that they themselves support and are also gaining financially. Fourthly, the contradiction between protecting traditional cultural rights in legal constitutions versus human rights legislation, which criminalizes FGM/C. Conclusion More research needs to be done in order to understand the complexities that are facilitating the medicalization of FGM/C as well as how policy strategies can be strengthened to have a greater de-medicalization impact. Tackling medicalization of FGM/C will accelerate the achievement of the Sustainable Development Goal of ending FGM by 2030.

scholarly journals Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

2021 ◽  
Vol 13 (9) ◽  
pp. 1754
Author(s):  
Jonathan Reith ◽  
Gohar Ghazaryan ◽  
Francis Muthoni ◽  
Olena Dubovyk
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
High Resolution ◽  
Land Degradation ◽  
Management Practices ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
High Resolution Data ◽  
Medium Resolution ◽  
Resolution Data

Monitoring land degradation (LD) to improve the measurement of the sustainable development goal (SDG) 15.3.1 indicator (“proportion of land that is degraded over a total land area”) is key to ensure a more sustainable future. Current frameworks rely on default medium-resolution remote sensing datasets available to assess LD and cannot identify subtle changes at the sub-national scale. This study is the first to adapt local datasets in interplay with high-resolution imagery to monitor the extent of LD in the semiarid Kiteto and Kongwa (KK) districts of Tanzania from 2000–2019. It incorporates freely available datasets such as Landsat time series and customized land cover and uses open-source software and cloud-computing. Further, we compared our results of the LD assessment based on the adopted high-resolution data and methodology (AM) with the default medium-resolution data and methodology (DM) suggested by the United Nations Convention to Combat Desertification. According to AM, 16% of the area in KK districts was degraded during 2000–2015, whereas DM revealed total LD on 70% of the area. Furthermore, based on the AM, overall, 27% of the land was degraded from 2000–2019. To achieve LD neutrality until 2030, spatial planning should focus on hotspot areas and implement sustainable land management practices based on these fine resolution results.

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