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

本数据集汇聚了印尼全国范围内，通过高空间分辨率影像的视觉解释所收集的地表覆盖（LC）参考数据，这些数据在地理分布上具有多样性、时间上保持一致，且与国家相关。这些数据是通过一次针对七个通用LC类别的全国性众包活动（以及一系列针对十七个详细LC类别的专家研讨会）收集的。解释者包括当地公民科学家（众包/非专家）和来自该国不同地区的当地LC视觉解释专家。这有助于确保LC地图产品具有相关性，并能有效满足国家及次国家级利益相关者和LC产品最终用户的可操作信息需求。我们提供了原始LC参考数据集，以及经过质量筛选的数据集，并附带质量评估指标。该数据集对LC制图社区，即研究人员和从业者而言具有重要意义，可作为训练机器学习算法和地图精度评估（应用适当的质量筛选器）的参考数据。此外，该数据集对公民科学社区也有价值，即作为一个大规模的经验数据集，用于研究众包/非专家的潜力和局限性，这是在我们所知的情况下，首次在印尼LC制图领域进行，旨在补充传统由专家解释的数据收集方式。数据的详细描述和数据收集方法可在以下论文中找到：Hadi, Yowargana, P., Zulkarnain, M.T. 等人. 印尼本土众包倡议的全国尺度的地表覆盖参考数据集. Sci Data 9, 574 (2022). https://doi.org/10.1038/s41597-022-01689-5