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

Hadi, H., Yowargana, P., Zulkarnain, M.T., Mohamad, F., Goib, B.K., Hultera, P., Sturn, T., Karner, M., Dürauer, M., See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549, Hendriatna, A., Nursafingi, Afi, Melati, D.N., Prasetya, F.V.A.S., Carolita, I., Kiswanto, Firdaus, M.I., Rosidi, M., & Kraxner, F. (2022). A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia. Scientific Data 9 (1) 10.1038/s41597-022-01689-5.

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Abstract

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.

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