Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations

Abstract

Environmental policy involving citizen science (CS) is of growing interest. In support ofthis open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or "pillars". Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as "two sides of the same coin". Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data product for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g., error propagation, paving the route to improvements in the approach.

Using iNaturalist in a Coverboard Protocol to Measure Data Quality: Suggestions for Project Design

“…All 530 observations elevated to research grade through the iNaturalist crowdsourcing approach were accurate, which supports other studies that concluded that crowdsourcing is the best method to identify errors in biodiversity data (Goodchild and Li 2012). Other studies have assessed the accuracy of CS-verified photographic data but resulted in similar but slightly lower accuracy for species identification (Swanson et al 2015;Leibovici et al 2017). For example, the Swanson et al study (2015) evaluated the Snapshot Serengeti volunteers' identifications of 48 different species and species groups (n = 4,149) of African wildlife, which resulted in 97% accuracy for species identifications with an accuracy rate that varied by species (Swanson et al 2015(Swanson et al , 2016.…”

Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities

“…The validation process is an important step that should be properly handled. Citizen science approaches complemented with the use of aerial and/or on-the-ground imagery can be used to validate the identification of green areas as well as identifying private and public space from LUCAS database pictures [77,78]. It could help to build a tool able to efficiently and accurately classify and differentiate private green space from public green space.…”

Modelling Physical Accessibility to Public Green Spaces in Switzerland to Support the SDG11

“…As OSM data still lack completeness for many of its object attributes in many places, there is an urgent need to find alternatives for filling this information gap. Among potential alternatives, citizen science campaigns can be a promising approach to identify public/private areas using aerial and/or on-theground images available in public databases (e.g., Flickr) or the Land Use and Coverage Area frame Survey (LUCAS) [34,35]. In addition, using patterns of mobile phone data (e.g., number and pattern over time of mobile phone users in a dedicated space) can also help detecting if a given green area is publicly accessible or not.…”