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

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/98900 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/98900 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/98900 archive 353 disk0/00/01/82/31 2022-09-20 09:23:10 2024-09-09 12:49:43 2022-09-20 09:23:10 article show 1 Hadi H. 9030 Yowargana P. 2069 Zulkarnain M.T. Mohamad F. Goib B.K. Hultera P. Sturn T. 2051 Karner M. 1983 Dürauer M. 2013 See L. 8571 0000-0002-2665-7065 Fritz S. 8130 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. 7904 A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia pub ASA NODES BNR AFE SI 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. 2022-09-17 published Nature Publishing Group doi:10.1038/s41597-022-01689-5 3185 341 301 144 73 276 98 165 public Scientific Data 9 1 TRUE 2052-4463 no info:eu-repo/semantics/article