The rapid expansion of tourism, transportation, energy, and agricultural infrastructure in mountain areas raises concerns about landscape fragmentation and impacts on aesthetic values. Effective delineation of these areas relies on negotiating various qualities that define them. In our study, we developed a collaborative consensus-building process with experts to map open spaces. Rather than collecting information on the factors characterizing open spaces, we first obtained a consensus on their delineation using a Delphi survey, followed by machine learning to extract variables explaining the spatial extent of the open spaces. Results show that the Delphi survey allowed experts to get a collective understanding on the delineation of open spaces through a process of knowledge (de)construction. By applying machine learning on the consolidated outcomes, we were then able to predict open spaces not only defined by physical aspects, but also characterized by subjective elements related to experts' perceptions of the landscape. Such an approach cannot only serve as a decision-support tool for more sustainable management of mountain areas, but as a tool to produce legitimized maps integrating knowledge and perception of various stakeholders. By incorporating these diverse perspectives, this participative process also fosters understanding and acceptance for future spatial planning decisions.

中文翻译：

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通过建立共识和机器学习绘制瑞士山区的开放空间

山区旅游、交通、能源和农业基础设施的快速扩张引发了人们对景观破碎化和对审美价值影响的担忧。这些区域的有效划分依赖于对定义它们的各种品质进行协商。在我们的研究中，我们与专家制定了协作共识建立流程来绘制开放空间地图。我们没有收集有关开放空间特征的因素的信息，而是首先使用德尔菲调查对其划分达成共识，然后通过机器学习提取解释开放空间空间范围的变量。结果表明，德尔菲调查让专家们通过知识（解构）过程对开放空间的划分有了集体理解。通过对综合结果应用机器学习，我们能够预测开放空间，不仅由物理方面定义，而且还由与专家对景观的看法相关的主观元素来表征。这种方法不仅可以作为山区可持续管理的决策支持工具，而且可以作为制作整合各利益相关者的知识和看法的合法地图的工具。通过整合这些不同的观点，这一参与过程还促进了对未来空间规划决策的理解和接受。