Fine-Grained Data Inference via Incomplete Multi-Granularity Data

Urban fine-grained data map inference, leveraging information from coarse-grained maps, has emerged as a significant area of research due to the growing complexity and data heterogeneity in urban environments.Existing methods have a priori assumption that a coarse-grained data map, one fixed-size granularity, transforms into a fine-grained data map, also one fixed-size granularity.However, in actual scenarios, the collected coarse-grained data maps are often incomplete and have significantly distinct granularities in various urban areas, which results in incomplete heterogeneous data, i.e., multi-granularity data maps in terms of spatial information.Meanwhile, different granularity data maps are needed for various urban downstream tasks, which is a multi-task problem.To that end, this paper proposes a novel framework, a multi-granularity super-resolution data map inference framework (MGSR), designed to harness spatio-temporal information to transform incomplete coarse-grained multi-granularity data maps into fine-grained multigranularity data maps.Specifically, we design a granularity alignment network to align multi-granularity information and address missing data on each granularity data map by leveraging the other granularity data maps with a well-designed self-supervised task.Then, we introduce a feature extraction network to capture spatiotemporal dependencies and extract features.Finally, we devise a recurrent super-resolution network with shared parameters to infer multi-granularity data maps.We conduct extensive experiments on three real-world benchmark datasets and demonstrate that MGSR significantly outperforms the state-of-the-art methods for multigranularity urban data map inference and reduces RMSE and MAE by up to 40.1% and 50.3%, respectively.The source code has been released at https://github.com/wn13/MGSR_code.