Beyond Static Graphs: a Scale-Variant Dynamic Graph-Based Framework Integrating Spatial and Hierarchical Relationships for Urban Land-Use Mapping

Urban land-use mapping is essential for understanding urban functional structures. Point of Interest (POI) data offers advantages for efficient land-use mapping, owing to its lightweight format, accessibility, and socioeconomic semantics. Although recent graph-based models integrate the POI semantics and spatial relationships, they deviate from human cognition due to three main limitations: 1) Node-level semantic objects rarely incorporate fine-grained semantic details, leading to representational ambiguity. 2) Features propagated between semantic objects contain redundancy caused by information overlap among neighboring objects. 3) The inability to model hierarchical “object-functional cluster-land use” relationships. To address these issues, a scale-variant dynamic graph-based framework integrating spatial and hierarchical relationships (DGLU) is proposed for urban land-use mapping. To enhance the discriminative power during node embedding, multi-granular semantic object encoding is proposed by incorporating detailed toponyms with the large language model (LLM). To mitigate feature redundancy in the edge-level spatial relationship modeling, the disparity-aware dynamic graph convolution (DAGC) is proposed to concentrate on distinctive neighborhood features when updating semantic object features. To enable scale-variant hierarchical understanding, DGLU adopts learnable land-use graph pooling (LGP) to integrate homogeneous objects into a “hyper-object”, thereby adaptively generating fine-to-coarse graph structures rather than fixed graph structures. Using open-source areas of interest (AOI) and POI data, we conducted experiments on the POI-CUN dataset covering 34 Chinese cities. The trained model was subsequently applied for urban land-use mapping in the central urban areas of Chengdu, Shanghai, and Wuhan. Both quantitative and visual results demonstrate the effectiveness and superiority of the proposed DGLU framework.