Graph reasoning-based spatial representation learning from geo-entities for multi-modal urban functional zone sensing

An urban functional zone (UFZ) serves as the planning and implementation unit in urban development and management strategies. Previous works on multi-modal UFZ representation learning have integrated socio-economic attributes from points-of-interest (POIs) with visual features from remote sensing images. However, the inherent sampling bias and spatial inequality in POIs can impede the model's discriminative capacity. To address the problems of insufficient data coverage and incomplete representation of physical-semantic sensing, we propose an interconnected, consistent and scalable framework within the physical-spatial-semantic representation space that we term TUF-Sensing. TUF-Sensing models building footprints and POIs as graph nodes, respectively, and applies a symmetrical graph convolutional architecture to capture the topology of the constructed graph, and the neighborhood influence between entities. To enhance the expressivity of nodes and stimulate neighborhood aggregation, the input features of buildings and POIs are constructed differently, using the polygonal attributes of buildings and one-hot encoding that reflects the categorical identity of POIs. The conducted experiments compared the performance of TUF-Sensing and six other methods on different scales of grids and blocks in Wuhan, China. The results demonstrate that TUF-Sensing yields significant improvements in both probability distribution- and categorical performance-based metrics, indicating its adaptability in large-scale and fine-grained UFZ recognition.