Urban tree cadastres, crucial for climate adaptation and urban planning, face challenges in maintaining accuracy and completeness. A transdisciplinary approach in Kaiserslautern, Germany, complements existing incomplete tree data with additional precise GPS locations of urban trees. Deep learning models using aerial imagery identify trees, while other applications employ street view imagery and LIDAR data to collect additional attributes, such as height and crown width. A web application encourages citizen participation in adding features like species and improving datasets for further model training. The initiative aims to minimize resource-intensive maintenance conducted by local administrations, integrate additional features, and improve data quality. Its primary goal is to create transferable AI models utilizing aerial imagery and LIDAR data that can be applied in regions with similar tree populations. The approach includes tree clusters and private trees, which are essential for assessing allergy and ozone potential but are usually not recorded in municipal tree cadastres. The paper highlights the potential of improving tree cadastres for effective urban planning in a transdisciplinary approach, taking into account climate change, health, and public engagement.