Focus on Hard Areas of Reconstruction: A Fine‐Grained Urban Flow Inference Framework

ABSTRACT Fine‐grained Urban Flow Inference (FUFI), as a critical spatial data reconstruction task, always faces the challenges of complex spatial correlations between coarse‐ and fine‐grained urban flows and spatial heterogeneity among different areas. However, general reconstruction methods primarily focus on extracting spatial correlations of flow data, with inadequate consideration of the spatial heterogeneity inherent in urban geographical contexts. This oversight leads to relatively larger errors concentrated in areas characterized by complex flow pattern features. In this paper, we argue that this problem lies in the lack of explicit spatial heterogeneity modeling. To address this problem, we propose a spatial heterogeneity aware FUFI framework, named FocusNet, to improve fine‐grained flow inference results by focusing on areas with relatively larger reconstruction errors, referred to as ‘hard areas’ in this paper. Specifically, our framework consists of two stages: first, a masking strategy is proposed to adaptively mask the easily reconstructed areas to force the model to focus on the hard areas; then, since the first stage achieves a binary recognition task (easy or hard areas), finer‐grained hard areas are further located and compensated by an encoder‐dual decoder model. Extensive benchmark evaluation shows that FocusNet achieves state‐of‐the‐art performance, and our framework can also incorporate previously proposed methods.