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Hadi; Yowargana, Ping; Zulkarnain, Muhammad Thoha; Mohamad, Fathir; Goib, Bunga K.; Hultera, Paul; Sturn, Tobias; Karner, Mathias; Dürauer, Martina; See, Linda; Fritz, Steffen; Hendriatna, Adis; Nursafingi, Afi; Melati, Dian Nuraini; Prasetya, F. V. Astrolabe Sian; Carolita, Ita; Kiswanto; Firdaus, Muhammad Iqbal; Rosidi, Muhammad; Kraxner, Florian

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

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  • Media type: E-Article
  • Title: A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia
  • Contributor: Hadi; Yowargana, Ping; Zulkarnain, Muhammad Thoha; Mohamad, Fathir; Goib, Bunga K.; Hultera, Paul; Sturn, Tobias; Karner, Mathias; Dürauer, Martina; See, Linda; Fritz, Steffen; Hendriatna, Adis; Nursafingi, Afi; Melati, Dian Nuraini; Prasetya, F. V. Astrolabe Sian; Carolita, Ita; Kiswanto; Firdaus, Muhammad Iqbal; Rosidi, Muhammad; Kraxner, Florian
  • imprint: Springer Science and Business Media LLC, 2022
  • Published in: Scientific Data
  • Language: English
  • DOI: 10.1038/s41597-022-01689-5
  • ISSN: 2052-4463
  • Keywords: Library and Information Sciences ; Statistics, Probability and Uncertainty ; Computer Science Applications ; Education ; Information Systems ; Statistics and Probability
  • Origination:
  • Footnote:
  • Description: <jats:title>Abstract</jats:title><jats:p>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.</jats:p>
  • Access State: Open Access