The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

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.

We discuss the potential application to virtual citizen science of a recent standard (BS ISO 27500:2016 “The human-centred organisation”) which encourages the adoption of a sociotechnical systems perspective across a wide range of businesses, organizations and ventures. Key tenets of the standard concern taking a total systems approach, capitalizing on individual differences as a strength, making usability and accessibility strategic objectives, valuing personnel and paying attention to ethical and values-led elements of the project in terms of being open and trustworthy, social responsibility and health and wellbeing. Drawing upon our experience of projects in our laboratory and the wider literature, we outline the principles identified in the standard and offer citizen science themed interpretations and examples of possible responses.

In the last decade, vast amounts of planetary science data have been made available publicly often focused on Mars. Such data is typically disseminated via the web and made available through screen-based visualizations. However, this approach can make it difficult to convey the broader context of a feature of interest or the spatial arrangement of surface phenomena. To better support learning and engagement, we present and evaluate MarsCAPE: Mars Communicated through an Augmented, Physical Environment. MarsCAPE consists of physical models of the surface of Mars, augmented by projected information and visualizations. To assess its learning and engagement value, a structured workshop and formal evaluation were conducted. Participants reported a significant increase in knowledge, found the models engaging, and exhibited natural learning without prompting. Systems such as MarsCAPE have the potential to provide an interesting, educational way for the public to access planetary data that goes beyond the capabilities of on-screen visualizations.