  We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame
\textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task
of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle
delivery systems. The dataset encompasses a diverse range of five categories,
enabling comprehensive training and evaluation of NER models. To construct this
dataset, we sourced the data from a real-world UAV delivery system and
conducted a rigorous data cleaning and desensitization process to ensure
privacy and data integrity. The resulting dataset, consisting of around 12,000
annotated samples, underwent human experts and \textbf{L}arge \textbf{L}anguage
\textbf{M}odel annotation. We evaluated classical NER models on our dataset and
provided in-depth analysis. The dataset and models are publicly available at
\url{https://github.com/zhhvvv/CNER-UAV}.
