Addressing the global environmental crisis necessitates coordinated efforts, supported by open and reproducible research practices. Such practices aim to enhance the reliability, efficiency, and credibility of scientific outputs. Innovative tools are necessary for systematic conservation planning. This technical note presents a reproducible and automated approach for supporting land management and planning by identifying and updating ecological infrastructure (EI). Grounded in open science Findable, Accessible, Interoperable, and Reusable principles data management, and digital twin (DT) concepts, the method focuses on the Canton of Geneva, Switzerland. It integrates species distribution modelling, ecosystem service assessments, and spatial prioritization within a shared JupyterLab environment. The infrastructure centralizes data, automates indicator calculations, and ensures transparency, traceability, and reproducibility through version control and metadata generation. Ecological tools like Zonation enable the identification of high-priority conservation areas aligned with international targets. The system facilitates collaborative workflows and indicator updates. Its architecture allows scalability to broader regions and scenario modelling, laying the foundation for a DT of Geneva’s environment. Despite challenges in harmonizing workflows across institutional partners, this solution enhances efficiency and replicability in EI planning. The methodology is transferable to other regions and adaptable to various environmental modelling domains, offering a robust base for sustainable territorial management.