Spatial context-aware method for urban land use classification using street view images

Street view images (SVIs) have great potential for automatic land use classification. Previous studies have paid little attention to the spatial context of SVIs and land parcels, leaving room for improvement in classification accuracy and identification of parcels without SVIs. This study proposes a novel spatial context-aware method for land use classification that synthesizes SVI content and spatial context among SVIs and land parcels through a derived spatial context graph convolution network (SC-GCN). Specifically, the method characterizes the spatial context among SVIs and land parcels into a graph, which formalizes SVIs and land parcels as nodes. The spatial relationships among SVIs and land parcels are represented as graph edges. SC-GCN is designed to model the spatial context of relevant SVIs and land parcels by incorporating heterogeneous structural information into land use classification. Experimental results show that the proposed method outperforms the baseline methods of land use classification at the parcel level and can successfully identify land use types of land parcels without SVIs. Specifically, precision, recall and F1-score values of the proposed method are 72.22%, 64.22% and 68.13%, respectively, which are 2.38%, 12.40% and 13.56% higher than those of the Random Forest method. This work contributes to land use mapping with limited available data by exploring the modeling of complex geospatial relationships, and it serves as a methodological reference for the prediction and supplementation of missing geographic data.

Automated summary

This study proposes a novel spatial context-aware method for land use classification that improves classification accuracy and identification of parcels without street view images by modeling the spatial relationships between street view images and land parcels.