This study categorizes traffic detectors into Eulerian and Lagrangian and addresses the challenges each faces. First, Eulerian sensing collects traffic data from all passing vehicles at detector locations over time. However, due to several factors, including detector malfunctions and communication errors, missing data frequently occurs. To address this problem, a Spatial Temporal Transformer Networks (STTNs) model was developed that applies an attention mechanism to impute missing traffic data. This model was evaluated across three types of missing patterns (MCAR, TCM and SCM) and missing rates ranging from 10% to 90%. The evaluation results showed that the accuracy decreases as the missing rate increases, with the highest accuracies observed for MCAR, followed by SCM and TCM. Next, Lagrangian sensing involves collecting trajectory data from probe vehicles, but this data is typically sparse, and detailed trajectory information is obscured during the spatiotemporal aggregation. To address this issue, a Pix2Pix Generative Adversarial Networks (GANs) model was developed, capable of estimating traffic speeds at the trajectory level with only 5% of the probe vehicle data. Despite being trained on simulation data, this model demonstrated superior performance on real-world datasets from NGSIM US-101 and I-80. These results suggest that even with varying road geometries and traffic characteristics, traffic states can be accurately estimated at the trajectory level based on images derived from trajectory data.