Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience

Abstract

The development of remotely sensed products such as land cover requires large amounts of high-quality reference data, needed to train remote sensing classification algorithms and for validation. However, due to the lack of sharing and the high costs associated with data collection, particularly ground-based information, the amount of reference data available has not kept up with the vast increase in the availability of satellite imagery, e.g. from Landsat, Sentinel and Planet satellites. To fill this gap, the Geo-Wiki platform for the crowdsourcing of reference data was developed, involving visual interpretation of satellite and aerial imagery. Here we provide an overview of the crowdsourcing campaigns that have been run using Geo-Wiki over the last decade, including the amount of data collected, the research questions driving the campaigns and the outputs produced such as new data layers (e.g. a global map of forest management), new global estimates of areas or percentages of land cover/land use (e.g. the amount of extra land available for biofuels) and reference data sets, all openly shared. We demonstrate that the amount of data collected and the scientific advances in the field of land cover and land use would not have been possible without the participation of citizens. A relatively conservative estimate reveals that citizens have contributed more than 5.3 years of the data collection efforts of one person over short, intensive campaigns run over the last decade. We also provide key observations and lessons learned from these campaigns including the need for quality assurance mechanisms linked to incentives to participate, good communication, training and feedback, and appreciating the ingenuity of the participants.

Keywords

• remote sensing
• reference data
• citizen science
• crowdsourcing
• land cover
• environmental monitoring