Abstract
Abstract
Detailed data on individual trees growing outside forests (TOF) are essential for arboriculture, biodiversity assessment, ecosystem services assessment, climate adaptation, and environmental health research. However, existing datasets are often incomplete and seldom provide taxonomic information beyond publicly managed trees in cities of Germany and other countries where maintaining a tree cadaster is a legal requirement. Here, we present for the first time a statewide dataset of individual TOF in the state of Baden-Württemberg, Germany, including tree locations, crown geometry, height, and genus-level labels. We mapped trees from high-resolution multi-spectral aerial imagery and height data using deep learning, with terrestrial LiDAR surveys used to create training labels within the URBORETUM project. The released dataset includes ten genus-level classes, including the aggregate categories “Other Deciduous” and “Coniferous” where assignment from aerial imagery is uncertain. The final ten-class genus model achieved a mean average precision (mAP) of 0.382 at an intersection-over-union (IoU) threshold of 0.5 (mAP@0.5) and a macro-averaged F1 score of 0.627 for taxonomic class assignment, while a separately trained one-class tree detector achieved an mAP@0.5 of 0.647, indicating stronger reliability for crown detection than for taxonomic classification. The final inventory contains 16.3 million predicted trees across the mapped non-forest domain of Baden-Württemberg. Beyond providing a statewide dataset, this work offers a scalable vision and operational blueprint for an evolving tree inventory that can be systematically improved over time through better imagery, richer local reference data, and targeted retraining.