Spatial Proximity Relations-Driven Semantic Representation for Geospatial Entity Categories

Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge and help achieve the deep fusion of geospatial data. In this paper, a method for the semantic representation of the geospatial entity categories (denoted as feature embedding) is presented, taking advantage of the characteristic that regions with similar distributions of geospatial entity categories also have a certain level of similarity. To construct the entity category embedding, a spatial proximity graph of entities and an adjacency matrix of entity categories are created using a large number of geospatial entities obtained from OSM (OpenStreetMap). The cosine similarity algorithm is then employed to measure the similarity between these embeddings. Comparison experiments are then conducted by comparing the similarity results from the standard model. The results show that the results of this model are basically consistent with the standard model (Pearson correlation coefficient = 0.7487), which verifies the effectiveness of the feature embedding extracted by this model. Based on this, this paper applies the feature embedding to the regional similarity task, which verifies the feasibility of using the model in the downstream task. It provides a new idea for realizing the formal expression of the unsupervised entity category semantics.