The aforementioned trends provide an explanation of the current integration of citizen science within EO activities. In this section, we will look at a general typology of major types of activities in citizen science, which are identified by their domain, technical needs and the level of engagement of participants in the projects.
Figure 1 presents the topics that the following sections will cover. Under the banner of citizen science we can see three types of activities, each highlighting a different facet of the field. First, long-running citizen science is defined by activities that involve the public in areas where the practice of working in collaboration with non-professional scientists is well established. There are many areas of science in which volunteers continue to play a role in research and, from the perspective of EO, ecological and biological observations, weather observations and participation in archaeology provide good examples of the potential of citizen science. Other areas, such as astronomy, have also demonstrated sustained engagement with citizen science. The next type of citizen science projects highlights the way technology influences citizen science, and includes projects that rely on the Internet and the Web. These citizen cyberscience projects use the ability of computers as both computing and communication devices to engage citizen scientists. In fact, projects that fall under this category would not have existed without the proliferation of computers and the Internet. Here we find volunteer computing, which utilises the unused computing resources of participants’ computers; volunteer thinking, which asks the participants to contribute through their cognitive abilities; and passive sensing, which relies on the sensors that are integrated into mobile computing devices to carry out automatic sensing tasks. The final group of citizen science projects that will be discussed here emphasises the depth of engagement of participants, and we will term these as community science—projects that are carried out as part of local, everyday settings, to address local concerns and needs. Here we look at three types of activities: participatory sensing, a joint activity between researchers and members of the public with varied levels of participation in setting what will be detected, where and how it will be analysed; DIY science, in which participants create the scientific instrument themselves, and repurpose a range of materials and tools to build laboratories and carry out their enquiries; and, finally, civic science, which covers scientific activities that aim to build relationships between the public, experts and policy makers and enable them all to participate in scientific knowledge production (Bäckstrand 2003). This type of scientific practice can also be recognised as bottom-up science (McQuillan 2014).
To understand how each of these families of citizen science operates and their relevance to EO, the following sections will look at each, in turn.
Citizen Science Across Domains: Long-Running Citizen Science
At first sight, there are many areas of scientific activities that continued to engage with non-professional scientists throughout the era of Big Science: birdwatching in biological and ecological observations (Kobori et al. 2016, Bonney et al. 2009), recording of meteorological conditions (WMO 2001), and volunteers in archaeological digs (Clarke 1978) are all examples of sustained engagement of citizen scientists. However, the aforementioned trends have changed the interaction with volunteers and the way in which they carry out their work and, especially, share information.
For example, new technologies are making a step change in the relevance of volunteered ecological and biodiversity observations for wider EO systems. Historically, amateur naturalists (as they were known) recorded information in their notebooks, frequently using idiosyncratic records management systems, and the sharing of the information with others was partial. As August et al. (2015) discuss, the use of digital technologies not only supports the immediate sharing of information, but also contributes in a structured way: for example through predefined forms on websites and increased use of apps on smartphones, which provide further information such as GPS coordinates, geolocated images or audio recordings (Jepson and Ladle 2015, Powney and Isaac 2015). In some of these systems and apps, information can appear in global databases (e.g. the Global Biodiversity Information Facility—GBIF) instantaneously. Therefore, through the link between more educated volunteers and ICT-enabled streamlined sharing, current citizen science contributes to the creation of EO systems in the area of biodiversity.
As noted, the participation of volunteers in weather and meteorological observations is also well documented. The network of meteorological observations is one of the longest-standing examples of citizen science, with many thousands of volunteers reporting local meteorological conditions to national organisations, which improves the quality of modelling and understanding weather and climate (WMO 2001). As such, this area demonstrates a union between citizen science and established professional science that is both persistent and evolving over time through the development of instruments and the abilities of participants. For example, the commercial provider of meteorological forecasts, The Weather Company, is managing a large-scale crowdsourcing aggregation of weather observations through the Weather Underground network. A network of over 180,000 participants link observations from their personal weather stations to improve The Weather Company’s predictions, and benefit by receiving personalised forecasts. Another example of the scale and scope of citizen science in this area is provided by the UK Met Office Weather Observation Website (wow.metoffice.gov.uk), which received 38 million observations in its first year of operation in 2011 (POST 2014), and provides a source of additional information for the Met Office that is especially useful during extreme weather events. Here, too, technological advances streamlined and standardised information sharing, while the increased awareness and skills in the general public contributed to greater participation in reporting.
Archaeology is another field with a tradition of voluntary participation with historical links to EO. Looking back at the 1966 report (Willow Run Laboratories 1966), satellites were seen as an extension of aerial photography, which was already in use at the time in archaeology. However, while satellite instruments were expected to assist in identifying large features, such as buried cities, “… the requirements for the use of such sensors in the detection of small features remain very near and possibly beyond the capabilities of orbital sensor equipment as presently envisioned” (p. 153). Today, there is a flourishing sub-discipline of Space Archaeology, which uses the abilities of EO to advance the field. Citizen science, in the form of crowdsourcing, now addresses the exact problem that, 50 years ago, was considered beyond the possible. In 2010, Albert Yu-Min Lin and colleagues devised a system based on high resolution satellite imagery to engage over 10,000 volunteers in the task of assessing potential locations for the unknown burial site of Genghis Khan (Lin et al. 2014). The system asked volunteers to evaluate an area visually and mark locations that they considered as potentially interesting. The ability to engage a huge number of volunteers enabled the examination of a very large area (6000 km2), yielding 55 candidate sites for further archaeological studies on the ground. The application that was developed for this task eventually evolved into the Tomnod system, now used by Digital Globe for humanitarian and other crowdsourcing efforts. Here, the ability of people to collaborate online is significant, and vividly demonstrates the importance of broadband and the bidirectional web in opening up new avenues for collaboration between professional and non-professional researchers.
The Impact of Technology: Citizen Cyberscience
As the overview noted, the emergence of the Internet and the Web as a global infrastructure has enabled a new incarnation of citizen science, which has been termed citizen cyberscience by Francois Grey (2009) and could not possibly have existed before. Characteristically it relies on the proliferation of billions of connected personal computing devices—desktop computers, smartphones and games consoles—and utilises the computational and sensing power of these devices to double as scientific instruments. If the previous section considered how citizen science is integrated into different scientific disciplines, here we look at how advances in personal computing transformed the potential of citizen science in contributing to EO. In particular, we will focus on three subcategories: volunteered computing, volunteered thinking and passive sensing.
Volunteered computing was first launched in 1999, with the SETI@home project (Anderson et al. 2002), which exploits the unused processing capacity in personal computers and uses the Internet to send and receive work packages that are analysed automatically and sent back to the main server. The system on which SETI@home is based, the Berkeley Open Infrastructure for Network Computing (BOINC), is now used for over 100 projects. While volunteer computing is popular in the area of biological and medical research, it is not well utilised in the area of EO. An example of the potential of volunteer computing is provided by the ClimatePrediction.net project, which was established by climate researchers at the University of Oxford in 2002 and, with exposure from mass media, reached 60,000 volunteers. In the early months of 2014, when the project team wanted to suggest the degree to which recent floods could be attributed to climate change, they were able to run over 33,000 different models and demonstrate that it is highly likely that the floods were more severe due to climate change (Climateprediction.net 2014).
While volunteered computing asks very little from the participants, apart from installing software on their computers, volunteered thinking engages volunteers at a more active and cognitive level (Grey 2009). In these projects, participants use a website in which information or an image is presented to them. They are provided with a little training in the task of classifying the information, after which they are exposed to information that has not been analysed and are asked to carry out classification work. Galaxy Zoo (Lintott et al. 2008) is one of the most well-known and developed examples of volunteer thinking. Over 100,000 volunteers classified images of galaxies for this project, and it spawned a range of applications that are included in the wider Zooniverse set of projects (see http://www.zooniverse.org/). We have already encountered one example of volunteer thinking work in the previous section, with the effort to locate Genghis Khan’s tomb.
Another highly relevant example of the involvement of volunteer thinking in EO is provided through the OpenStreetMap project (Haklay and Weber 2008). This distributed project has now engaged millions of people in mapping their area through a combination of tracing satellite imagery and on the ground survey, as demonstrated by the “Missing Maps” project (Feinmann 2014) in which areas that were not mapped before are being added to OpenStreetMap to support humanitarian efforts.
The final example of citizen cyberscience is provided by passive sensing, in which participants either connect sensors to their computers or smartphones, or use the built-in sensors that are available in devices, to support EO efforts. Unlike participatory sensing, which we will encounter in the next section, passive sensing is mostly based on automatic data capture and sharing, without the conscious intervention of the volunteer. We have seen one example of such passive sensing in the Weather Underground network above. The personal weather stations that are linked to the network operate automatically, mostly without intervention from their owner, and, once they are set to deliver the information to The Weather Company’s server, they will continue to do so. However, further potential for EO integration is provided via mobile devices. For example, the Quake Catcher Network (QCN) is utilising the movement sensors that are integrated into some laptop computers, to enhance observations from existing seismic observation stations (Cochran et al. 2009). QCN is improving the quality of seismic information that is emerging from events. Interestingly, QCN is utilising the BOINC framework but extends it by linking to sensors.
Depth of Participation: Community Science
Community science is a term used here to describe citizen science projects with a significant element of bottom-up control over the project; at its extreme, activities are initiated and driven by a group of participants who identify a problem that is a concern for them and address it using scientific methods and tools. The problem definition, data collection and analysis might be carried out by community members or in collaboration with scientists in established laboratories whose role is to support and carry out work on behalf of the community members. This is in contrast to the types of citizen science discussed above, where the scientific research question, data collection methodology and the analysis are all done by professional scientists and the role of participants is somewhat restricted.
In the area of community science, three examples demonstrate the role of participants and professional scientists, and their potential of integration with EO.
First, participatory sensing is defined as sensing activity in which a group of participants contribute together to a body of information. Importantly, while the term is now used liberally to describe a wide range of crowdsourced sensing activities with varying levels of active engagement with the citizen scientists who will carry out the sensing, in the original definition (Burke et al. 2006; Goldman et al. 2009), “Participatory Sensing emphasizes the involvement of citizens and community groups in the process of sensing and documenting where they live, work, and play …” (p. 4). Unlike passive sensing, the participants are expected to take a more significant role in shaping the sensing project. In its simple form, participatory sensing requires lower cognitive effort from participants and relies on users to provide sensory information in a structured manner via their mobile devices and cloud services (Estrin 2010). The participants select when and where to carry out data collection, but the application and the data infrastructure are set. The examples that were mentioned above of apps for ecological and biodiversity recording operate under this scheme—many of the apps that are provided to volunteers (see Jepson and Ladle 2015, Powney and Isaac 2015) expect participants to take an image of the species that they have identified using their smartphones and share them by adding them to national or global databases. Another interesting example is Ikarus (http://thermal.kk7.ch/), where paraglider flight log data is collected and processed to generate thermal maps. With a large number of paragliders and flight paths, Ikarus is one of the largest participatory sensing initiatives (Von Kaenel et al. 2011).
In contrast, the practices of DIY science mean that the participants develop instruments, methodologies for data collection and analysis (Nascimento et al. 2014). This requires very deep engagement from the participants, as well as technical and scientific knowledge to carry out the scientific study in question. In the area of EO, we can see an emerging interest in the development of devices and software that can facilitate balloon and kite mapping, for example by the Public Laboratory for Open Technology and Science (Public Lab for short). By using simple adapted technology, digital cameras are strapped to balloons or kites and used to observe and analyse local conditions. Simple adaptation can convert a camera to near-infrared, and thus provide information at other wavelengths than visible light (Breen et al. 2015). Moreover, if the group who collected the data wishes, this very detailed local mosaic can be shared through Google Earth.
The final type of community science is civic science, which is explicitly linked to community goals and questions the state of things. While some DIY science is done from such a perspective (e.g. Breen et al. 2015), civic science can also include work with indigenous communities in the use of smartphones to record community resources and other local features, even when the participants are non-literate (Stevens et al. 2014). While the approach is highly sensitive to local cultural practices and involves a lengthy discussion about information sharing to ensure consent, it can be integrated into larger EO systems, providing the unique perspective of local and traditional ecological knowledge.