## 4 Data Cleaning and Synchronization  
Data cleaning is a crucial stage in preparing our dataset for robust analyses and application in urban mobility and planning studies. Given the complexity of urban environments and the variety of sensors involved, systematic cleaning procedures were essential to ensure the consistency, accuracy, and reliability of our multimodal dataset. After completing our data collection routes, we sampled GPS data at 4 m intervals. This strategy was selected to strike a balance between comprehensiveness and manageability, ensuring an accurate representation of the traveled paths without redundancy. The chosen interval resulted in the collection of 950 distinct GPS points across the surveyed area, a figure that effectively represents the total length of the routes divided by the sampling interval. For each of these 950 GPS points, timestamps were recorded, capturing the exact moment each point was logged. These timestamps are vital for synchronizing the GPS data with sensor data from a variety of sources including GPS, IMU, stereocamera, and LiDAR, which capture complementary information at varying frequencies.  
To synchronize the GPS data with the data collected from different sensors, we employed a two-pronged strategy to achieve effective synchronization:  
1. **Automated Synchronization with Jetson Clock and KD-Tree:** We employ the Jetson's internal system clock as the primary reference for all sensor timestamps to minimize potential discrepancies due to clock drift from individual sensors. Each sensor reading is timestamped at the exact moment of capture, referenced to the Jetson clock, ensuring consistency across the dataset. Following data acquisition, we utilize the KD-tree algorithm [28] for post-processing alignment. This algorithm efficiently organizes timestamps into a k-dimensional space, allowing for rapid nearest-neighbor searches. By employing this method, we are able to accurately match each GPS point with the closest corresponding frames from the stereocamera and each LiDAR scan based on temporal proximity. This synchronization process ensures that the GPS data aligns perfectly with the data collected from different sensors, thus maintaining the integrity and accuracy of our multimodal dataset.  
Manual Synchronization for Image Data:  
For the manual synchronization phase, we developed an intuitive graphical user interface (GUI) that facilitates seamless navigation through two key datasets: RGB images from the stereocamera, which are synchronized with the Jetson system clock and serve as a temporal reference, and the extracted 4K images from the Osmo 3 video footage, which lack direct timestamps tied to the Jetson system clock. By manually browsing and comparing images from both datasets, we identified pairs that best matched and depicted the same scene at the exact moment. We then assigned the same timestamp to the manually selected high-resolution images as their counterpart in the stereo camera RGB dataset. Although this manual process is more time-consuming than automated synchronization, it is essential for ensuring the accurate temporal alignment of the high-resolution Osmo 3 images with data from other sensors. Figure 6 illustrates the GUI in action.