We present a \textbf{L}andmark-\textbf{A}ware \textbf{V}isual \textbf{N}avigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies. We collect RGB observation and human point-click pairs as a human annotator explores virtual and real world environments with the goal of full coverage exploration of the space. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization.