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

Onto new horizons:Insights from the WeObserve project to strengthen the awareness, acceptability and sustainability of Citizen Observatories in Europe

WeObserve delivered the first European-wide Citizen Observatory (CO) knowledge platform to share best practices, to address challenges and to inform practitioners, policy makers and funders of COs. We present key insights from WeObserve activities into leveraging challenges to create interlinked solutions, connecting with international frameworks and groups, advancing the field through communities of practice and practitioner networks, and fostering an enabling environment for COs. We also discuss how the new Horizon Europe funding programme can help to further advance the CO concept, and vice versa, how COs can provide a suitable mechanism to support the ambitions of Horizon Europe

Citizen Science Projects (MOOC) 2.13:Technology and data collection: the case of Makerspaces

This record is part of a wider collection that captures the online course, Citizen Science Projects: How to make a difference (MOOC). This record represents a single learning activity in the MOOC in chronological order. Associated content is linked to the master record below

WeObserve:An Ecosystem of Citizen Observatories for Environmental Monitoring

The last decade has witnessed a rise in the field of citizen science which can be described as a collaborative undertaking between citizens and scientists to help gather data and create new scientific knowledge. In the EU, efforts have been channeled into developing the concept of Citizen Observatories (COs), which have been supported via the Seventh Framework Program (FP7) and continue to be funded in Horizon 2020. COs, often supported by innovative technologies including Earth Observation (EO) and mobile devices, are the means by which communities can monitor and report on their environment and access information that is easily understandable for decision making. To improve the coordination between existing COs and related citizen science activities, the WeObserve project tackles three key challenges that face COs: awareness, acceptability and sustainability. The WeObserve mission is to create a sustainable ecosystem of COs that can systematically address these identified challenges and help move citizen science into the mainstream. The WeObserve approach will apply several key instruments to target, connect and coordinate relevant stakeholders. The first is to develop and foster five communities of practice to strengthen the current knowledge base surrounding COs. Topics will include citizen engagement, the value of COs for governance and CO data interoperability. In co-creating this knowledge base, CO practitioners will have a platform to effectively share best practices and avoid duplication. Secondly, the project will expand the geographical reach of the knowledge base to different target groups via toolkits, a Massive Open Online Course (MOOC), capacity development roadshows and an Open Data Exploitation Challenge, to strengthen the uptake of CO-powered science by public authorities and SMEs. A third mechanism will forge links with GEOSS and Copernicus to demonstrate how COs can complement the EU’s Earth Observation monitoring framework. This paper will describe these various mechanisms and issue a call to bring together diverse stakeholders who share a joint (practice-oriented) interest in citizen science. The WeObserve consortium brings together the current H2020 COs (Ground Truth 2.0, GROW, LandSense, Scent) who will actively open up the citizen science landscape through wide ranging networks, users and stakeholders, including ECSA, GEOSS and Copernicus to foster social innovation opportunities. The WeObserve approach and outcomes have the potential to create a step-change in the Earth Observation sector and make COs a valuable component of Earth system science research to manage environmental challenges and empower resilient communities

Citizen Science Projects (MOOC) 2.13:Technology and data collection: the case of Makerspaces

This record is part of a wider collection that captures the online course, Citizen Science Projects: How to make a difference (MOOC). This record represents a single learning activity in the MOOC in chronological order. Associated content is linked to the master record below