Accurate Trajectory Recovery in Underserved Areas via Location Inference from Web Crowdsourced Data

In remote or underserved regions, where road networks are often either unavailable or poorly mapped, and GPS signals are sparse and unreliable, the quality of individual trajectories is severely compromised. In such contexts, web crowdsourced data becomes essential for accurately recovering trajectories and compensating for missing spatial information in the absence of explicit road networks. However, this situation introduces two key challenges:(i) scarcity of data in unseen regions, restricting transfer learning; and (ii) the necessity to infer latent movement structures under roadless conditions. To address these, we propose Region-aware Hierarchical Trajectory Recovery (RHTR) model, designed for location inference from web crowdsourced data in sparse, roadless scenarios. RHTR constructs multi-scale implicit grid-based offset maps from historical location data, with coarse grids capturing global patterns and fine grids refining local details. These sequential representations form region-aware encodings by sampling information around observed points, facilitating trajectory recovery. A coarse-to-fine mechanism leverages contextual information to progressively reconstruct missing segments. Experiments on two public datasets—simulating underserved and disaster-like settings with cross-region transfer—demonstrate that RHTR achieves state-of-the-art performance in trajectory recovery.