Deep-learning–based individual tree-crown (ITC) mapping has become increasingly prominent in remote sensing, yet rigorous validation of these predictions at large spatial scales remains challenging. Using data from an extensive case study involving the mapping of approximately 218.7 million trees across the German federal states of Sachsen and Sachsen-Anhalt from multispectral aerial imagery, we demonstrate that scaling such models beyond controlled environments significantly exacerbates validation difficulties. Minor inaccuracies in tree crown segmentation can critically affect practical applications, including forestry management and urban planning. Our findings highlight validation complexities arising specifically from tree allometry, seasonal variability, shadow effects, and annotation characteristics within training datasets. Consequently, achieving reliable model performance requires deliberate design of training data and potentially leveraging task-specific pre-training through Foundation Models. We emphasize the importance of rigorous validation procedures to ensure the reliability and practical utility of large-scale deep-learning models in ecological and urban management contexts.