Reference to the other related API named “Vegetation management Lot 2: Vegetation Management Solution": 7f1bb09e‐eaa8‐4c70‐af86‐baf9ed2bd45a ENEDIS SA (THE COMPANY), as part of its public service mission and monitoring of the electricity grid, wishes to identify a modular solution for processing data from field capture (LIDAR, imaging, satellite and other innovative technologies). The aim is to have a tool to: Identify risk areas (forested areas, biodiversity, etc.); Generate a list of pruning interventions enriched and prioritized, taking into account several parameters: (the level of urgency, pruning policy, the regulations in force, local specificities and growth dynamics of the vegetation, the health of the vegetation, etc.); Be able to manage post-pruning control. The value chain consists of the following steps: --- 1. A capture phase (outside the scope of this API request): This capture phase is not included in this API, which focuses exclusively on a modular solution for analysing and processing the data from this capture, having to take into account the diversity of possible input data sources. This capture is carried out at national level (25 Regional Directorates). It allows an inventory of the state of the vegetation around the electrical works. This capture using several different vectors (on foot, by drone, by satellite, by helicopter, etc.) will be based on several on-board technologies: A LIDAR and/or photogrammetry sensor for the creation of a digital twin of lines and vegetation (models 2D/3D); Photographic devices for visual documentation of vegetation around structures and lines; Multispectral sensors to measure the health of vegetation; And possible innovative remote sensing solutions, etc. ---2. A data processing and analysis module (included in the Lot 1 API): This processing and analysis module includes, for example (non-exhaustive list): algorithms and operations for the classification of point clouds (network, vegetation, soil, buildings, etc.); Estimating the linear distances concerned and the volumes of vegetation to be treated; Detection of areas to be pruned according to the regulatory templates defined by Enedis and processing of various data (multispectral, lidar, etc.); Detection of vegetation beyond the regulatory template to identify vegetation and its health, near our structures; A predictive model of the growth of vegetation by area on the territory, enabling risks to be anticipated and the emergency level of interventions to be graduated over time; Post-pruning control and reporting. --- 3. A return module (included in this Lot 1 API): The solution must make it possible to return lists of interventions enriched and prioritized in an HMI and in 2D and 3D cartographic format of the various analyses. In addition, the solution must propose dashboards to measure the progress, the quality of the input data, the processing times and provide KPIs on the processed data. --- 4. Other use cases (included in this Lot 1 API). The solution must allow the use and processing of other data (distance to obstacles, topography, heritage programmes, identification of network works and anomalies in photos, crisis management and troubleshooting, etc.) and other use cases. This solution must be able to integrate data in other formats or internal and external layers without requiring additional processing (pedestrian inventory, IGN, etc.). The data and restitution modules (mapping, 2D, 3D, photos, etc.) produced must be able to integrate easily and quickly with Enedis’ native IS (GIS, CMMS, mobility tools, etc.) and with any vegetation management tool covered by another API (see Lot 2 – Vegetation Management Solution). This solution aims to strengthen the operational performance of the activity, piloting, field teams, standardise monitoring practices, and optimise vegetation management plans with a view to prevention, safety and cost control. It is fully in line with Enedis’ strategy for innovation and digitalisation of business processes and the new vegetation management policy. The volume of data processing will be significant as it could involve nearly 330,000 km of airborne HTA network as well as 390,000 km of airborne BT network per year. The performance of the proposed solution will thus be essential.
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