A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia
This collection represents geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference data 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 local citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. This helps to ensure that the LC map products are relevant and can contribute effectively to the actionable information needs of the national and sub-national stakeholders and end users of the LC products within the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. The dataset is relevant for the LC mapping community, i.e., researchers and practitioners, as reference data for training ML algorithms and for map accuracy assessment (with appropriate quality-filters applied). The dataset is also useful for the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of 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. The detail description of the data and the data collection methodology can be found in our paper below.
Hadi, Yowargana, P., Zulkarnain, M.T. et al. A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia. Sci Data 9, 574 (2022). https://doi.org/10.1038/s41597-022-01689-5
This work was supported by the RESTORE+ project (www.restoreplus.org), which is part of the International Climate Initiative (IKI), supported by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) based on a decision adopted by the German Bundestag.
- Other earth sciences not elsewhere classified
- Environmental assessment and monitoring
- Environmental management not elsewhere classified
- Other environmental sciences not elsewhere classified
- Natural resource management
- Physical geography and environmental geoscience not elsewhere classified
- Human geography not elsewhere classified
- Human-computer interaction
- Environmental education and extension
- Ecology not elsewhere classified
- Land use and environmental planning