Mapping open spaces in Swiss mountain regions through consensus-building and machine learning
As mountain areas experience increasing tourism, transportation, energy and agricultural development, the need to manage landscape fragmentation and preserve aesthetic and cultural qualities becomes increasingly relevant.
In collaboration with Mountain Wilderness, Stiftung Landschaftsschutz Schweiz, and Schweizer Alpen-Club (SAC), we took an initial step in addressing this situation by mapping open spaces in Swiss mountain regions. To effectively delineate and manage open spaces, our research presents a new methodology that combines a collaborative consensus-building process with experts and the application of machine learning.
In this study, we employed a Delphi survey to facilitate a collective understanding and redefinition of open spaces based on expert insights and interpretations. Machine learning techniques were subsequently applied to the agreed delineations to identify determining factors and finally to illustrate the spatial distribution of open spaces in Swiss mountain regions. The assessment and inclusion of both physical attributes and subjective expert perceptions provides a shared and legitimized delineation of open spaces. Furthermore, this participatory process promotes comprehension and acceptance of future spatial planning decisions, making the resulting map a valuable decision-support tool for the sustainable management of mountain areas.
Read the full article here: Mapping open spaces in Swiss mountain regions through consensus-building and machine learning.
Applied Geography, 165, 103237. external pagehttps://doi.org/10.1016/j.apgeog.2024.103237