Implementation of AI-based detection into the climate policy within the European Green Deal

The European Green Deal outlines the European Union's roadmap for the green transition required by the Paris Climate Agreement. As part of its sustainable environmental policies, the European Green Deal aims to integrate digital transformation with the preservation of ecosystem services, the enhancement of green infrastructure, and the long-term sustainability of green networks. Green infrastructure contributes directly to the environmental objectives of the Green Deal by reducing carbon emissions, improving air quality, and conserving biodiversity. Therefore, accurately identifying, monitoring, and mapping green infrastructure is essential to achieving these goals. In this context, artificial intelligence (AI)-based automated tree detection systems, a rapidly advancing technology, play a critical role in fields such as forest management, biodiversity monitoring, and carbon footprint assessment. This study aims to support green policy objectives by automatically detecting tree communities in a specified region using AI algorithms applied to open-access satellite imagery. The research was conducted across three sample areas with varying environmental characteristics. The methodology integrates image-processing techniques with object detection algorithms, enabling high-accuracy classification of trees. The results contribute significantly to climate change mitigation efforts, carbon stock monitoring, smart urban planning, and the formulation of agricultural policies. Moreover, the proposed system can function as a decision support mechanism for public institutions, local governments, environmental scientists, and policymakers. In alignment with the European Green Deal's vision of digital green transformation, such AI-based applications hold substantial potential for enhancing environmental sustainability.