  Vision-and-language navigation (VLN) aims to guide autonomous agents through
real-world environments by integrating visual and linguistic cues. While
substantial progress has been made in understanding these interactive
modalities in ground-level navigation, aerial navigation remains largely
underexplored. This is primarily due to the scarcity of resources suitable for
real-world, city-scale aerial navigation studies. To bridge this gap, we
introduce CityNav, a new dataset for language-goal aerial navigation using a 3D
point cloud representation from real-world cities. CityNav includes 32,637
natural language descriptions paired with human demonstration trajectories,
collected from participants via a new web-based 3D simulator developed for this
research. Each description specifies a navigation goal, leveraging the names
and locations of landmarks within real-world cities. We also provide baseline
models of navigation agents that incorporate an internal 2D spatial map
representing landmarks referenced in the descriptions. We benchmark the latest
aerial navigation baselines and our proposed model on the CityNav dataset. The
results using this dataset reveal the following key findings: (i) Our aerial
agent models trained on human demonstration trajectories outperform those
trained on shortest path trajectories, highlighting the importance of
human-driven navigation strategies; (ii) The integration of a 2D spatial map
significantly enhances navigation efficiency at city scale. Our dataset and
code are available at https://water-cookie.github.io/city-nav-proj/
