Nat Commun. 2022 Sep 7;13(1):5178. doi: 10.1038/s41467-022-32693-3.
Advances in geospatial and Machine Learning techniques for large datasets of georeferenced observations have made it possible to produce model-based global maps of ecological and environmental variables. However, the implementation of existing scientific methods (especially Machine Learning models) to produce accurate global maps is often complex. (co-founder of OpenGeoHub foundation), (researcher at ETH Zürich), and (Science IT Consultant at the University of Zürich) shared with their perspectives for creators and users of these maps, focusing on the key challenges in producing global environmental geospatial datasets to achieve significant impacts.
地理空间和机器学习技术在地理参考观测大数据集中的进步使得基于模型的生态和环境变量全球地图的制作成为可能。然而，实施现有的科学方法（尤其是机器学习模型）来制作准确的全球地图往往很复杂。（OpenGeoHub 基金会的联合创始人）、（ETH 苏黎世的研究员）和（苏黎世大学的科学 IT 顾问）分享了他们对这些地图的创建者和使用者的看法，重点讨论了在制作具有重大影响的全球环境地理空间数据集方面所面临的关键挑战。
