Open spaces in Swiss mountain regions

In this study, we presented a novel approach that combines expert knowledge with machine learning to map open spaces in mountain regions. Rather than relying exclusively on physical attributes, we used a Delphi survey to reach a consensus among experts on what constitutes open spaces, considering both physical aspects and their subjective perceptions of the landscape. Using machine learning methods, we extrapolated this information and created a map of open spaces in the evaluated mountain regions. In addition to providing a validated and detailed map, this approach fostered collaborative decision making and facilitated processes of knowledge redefinition, ultimately leading to improved and shared outcomes. This dataset contains all the scripts as well as the final map, but not the starting raw data, as these are either already provided elsewhere or not openly accessible.本研究提出了一种融合专家知识与机器学习技术的全新方法，用于绘制山区开放空间分布图。本研究未仅依赖物理属性，而是采用德尔菲调查（Delphi survey）法，组织专家就开放空间的界定标准达成共识，同时兼顾景观的物理特征与专家的主观感知。依托机器学习方法，我们将该共识信息进行外推拓展，最终生成了本次评估区域内的山区开放空间分布图。该方法不仅产出了经过严谨验证且细节饱满的开放空间分布图，还推动了协同决策进程，助力知识再定义工作开展，最终实现了更优化且可共享的研究成果。本数据集包含全部代码脚本与最终生成的分布图，但未包含初始原始数据，此类原始数据或已在其他渠道发布，或无法公开获取。