Spatio-Temporal Pyramid-Based Multi-Scale Data Completion in Sparse Crowdsensing

Sparse Crowdsensing has emerged as a crucial and flexible method for collecting spatio-temporal data in various applications, such as traffic management, environmental monitoring, and disaster response. By recruiting users and utilizing their diverse mobile devices, this approach often results in data that is both sparse and multi-scale, complicating the data completion process. Although numerous data completion algorithms have been developed to address data sparsity, most assume that the collected data is of the same or similar scale, rendering them ineffective for multi-scale data. To overcome this limitation, in this paper, we propose a spatio-temporal pyramid-based multi-scale data completion framework in Sparse Crowdsensing. The basic idea is to leverage a pyramid structure to efficiently capture the complex interrelations between different scales. We first develop a Spatial-Temporal Pyramid Construction Module (ST-PC) to handle multi-scale inputs, and then propose a Spatial-Temporal Pyramid Attention Mechanism (ST-PAM) to capture multi-scale correlations while reducing computational complexity. Furthermore, our method incorporates cross-scale constraints to optimize completion performance. Extensive experiments on four real-world spatio-temporal datasets demonstrate the effectiveness of our framework in multi-scale data completion.