With the aim of helping local authorities to green their territories and, more generally, to facilitate and rationalise the adaptation of cities to climate change and heat waves, the Métropole de Lyon, its open innovation laboratory, its Metropolitan Data service and its business services (greening services, water management services, etc.), researchers and several emerging players, gathered around a SCOP (TelesCoop), intend to create a chain of tools to analyse, visualise and cross-reference pre-existing territorial data in order to locate plantable areas, areas in which vegetation can be densified or, more broadly, areas in which urban developments combining planting, soil de-sealing and urban cooling are possible. To do this, the IA.rbre project builds on the results of a previous project dedicated to the plantability of tall vegetation. It will improve the quality of the pre-existing plantability index, in particular through supervised learning, and will make it possible to extend the analyses to issues related to vegetation such as de-waterproofing, cooling cities and, more broadly, improving livability; it will incorporate additional inputs from AI and data science, all with a frugal approach, leveraging open data and pre-existing models. In this way, while developing an AI chain immediately applicable to the challenges of greening, de-waterproofing and urban cooling, IA.rbre must consolidate and improve a methodology for responding to objectives of general interest that can also be reprodicible and replicable to other territories because it is based on the following principles: to help public decision-making by cross-referencing data of various kinds likely to improve knowledge of the territories; Prioritise the sharing of data (open data, datalab), algorithms (licensed as digital commons) and documentation (methodological guides, booklets, etc.), in line with the logic of open science; in that of open innovation, to ensure both adaptation to needs, the consideration of multiple areas of expertise and the adoption of the tools developed, co-design and prototype them based on the needs and uses of users, i.e. the services of the Métropole de Lyon, as well as the staff of other local authorities likely to reuse both the AI chain and the methodology; involve in the design, developments, experiments and evaluations by many stakeholders, including researchers (CNRS, Université Lumière Lyon 2) and economic entities favouring the collective interest, such as the SCOPs (TelesCoop, leader of the consortium); In addition, given the cross-cutting nature of the challenges and means of the ecological transition, promote the decompartmentalization and collaboration between organizations and between departments of the same organization.