The representation and similarity measurement of geospatial scenes are fundamental tasks in spatial cognition, with broad applications in spatial information retrieval, scene matching, and multi-source data fusion. With the advancement of GeoAI, the adoption of deep learning methods has significantly improved the precision and efficiency of similarity measurement. This paper provides a systematic review of recent advances in geospatial scene similarity measurement. First, it summarizes traditional geospatial scene representation methods and categorizes conventional similarity models into three types: the gradual transformation model based on conceptual neighborhood, the multi-factor weight assignment model, and the association graph structure model. Subsequently, the paper focuses on deep learning-based methods, analyzing raster, vector, and multimodal representations based on CNN, GNN, and Transformer architectures. Furthermore, similarity measurement techniques are classified into three learning paradigms: supervised metric learning, unsupervised representation learning, and self-supervised contrastive learning, along with a review of representative studies and application scenarios. Finally, the paper analyzes key challenges in the current research and looks forward to future development directions. It provides theoretical support and methodological references to advance the theory of geospatial similarity measurement and the construction of intelligent spatial cognitive systems.