A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia

Here we present a geographically diverse, temporally consistent, and nationally relevant land cover
(LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in
a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert
workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen
scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the
country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the
quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping
community (researchers and practitioners), i.e., as reference data for training machine learning
algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen
science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of
contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first
time to our knowledge, within the context of complementing traditional data collection by expert
interpreters.