Human languages exhibit a variety of strategies for communicating spatial information, including toponyms, spatial nominals, locations that are described in relation to other locations, and movements along paths. SpaceEval is a combined information extraction and classification task with the goal of identifying and categorizing such spatial information. In this paper, we describe the SpaceEval task, annotation schema, and corpora, and evaluate the performance of several supervised and semi-supervised machine learning systems developed with the goal of automating this task.