Like many places around the world, the wildland–urban interface areas surrounding urban regions are subject to variable levels of fire risk, threatening the natural habitats they contact. This risk has been assessed by various authors using many different methods and numerical models. Among these approaches, machine learning models have been successfully applied to determine the weights of criteria in risk assessment and risk prediction studies. In Istanbul, data have been collected for areas that are yet to be urbanized but are foreseen to be at risk using geographic information systems (GIS) and remote sensing technologies based on fires that occurred between 2000 and 2021. Here, the land use/land cover (LULC) characteristics of the region were examined, and machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB) models, were applied to classify the factors that affect fires. The RF model yielded the best results, with an accuracy of 0.70, an F1 score of 0.71, and an area under the curve (AUC) value of 0.76. In the RF model, the grouping between factors that initiate fires and factors that influence the spread of fires was distinct, and this distinction was also somewhat observable in the other two models. Risk scores were generated through the multiplication of the variable importance values of the factors and their respective layer values, culminating in a risk map for the region. The distribution of risk is in alignment with the number of fires that have previously occurred, and the risk in wildland–urban interface areas was found to be significantly higher than the risk in wildland areas alone.

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.