Due to the widespread use of drones in an urban environment, drones present an increased risk to the safety of urban life. Reliable detection of drones becomes crucial for countering the hazard introduced by drones. However, drones are difficult to detect because of their size and customization. This paper introduces DDL, a dataset aimed at drone sound detection, classification, and localization via a specially constructed set of microphones. As a baseline, we propose a deep uncertainty-aware framework implementing Conformer for joint drone classification and localization. We employ heteroscedastic loss functions that jointly estimate means and variances for spatial localization to model prediction uncertainty. Experiments on the DDL dataset demonstrate a classification accuracy of 99.9% and a Euclidean distance mean absolute error (MAE) of approximately 16 meters. The uncertainty estimates are well-calibrated, with coverage closely matching the expected confidence intervals (68%, 95%, and 99.7%) as defined by the empirical rule, suggesting DDL as a benchmark dataset for audio-based drone localization.