In recent years, with the rapid development of urbanization, the urban public emergency management faces increasing challenges, and the number of fire incidents has increased largely. For mitigating injury risk and reducing property loss, fire station locations need to be optimized to provide efficient fire emergency services. However, the locations of fire facilities in China are mainly determined according to administrative divisions, lacking effective data-driven applications. In addition, existing location models are designed for general purpose, and few have taken into account the unique characteristics of fire services with multiple objectives, i.e., maximum coverage, minimum overlapping coverage and balanced workloads. With the recent development of geospatial big data, lots of fire risk related data (e.g. fire incidents data and travel time data) can be obtained, which provides an unprecedented opportunity for urban fire emergency facilities planning. In this paper, we propose to establish the multi-objective maximal covering location model by accurately estimating fire rescuing demands and travel cost from geospatial big data. A case study was carried out in Nanjing, China, and the result shows that our method is effective in optimizing the locations of fire stations.