Transportation

The AIS dataset is provided by the National Oceanic and Atmospheric Administration (NOAA), spanning from January 2020 to December 2020. The trajectories of 140 individual vessels (including tankers and cargo) were collected. Weather and ocean conditions for the same period are obtained from the National Data Buoy Center (NDBC), collected from 15 buoys. 

The datasets are sourced from the Caltrans Performance Measurement System (PeMS) in California, which monitors and collects real-time traffic data from over 39,000 sensors deployed on major highways throughout the state. The PeMS system collects data every 30 seconds and aggregates it into 5-minute interval, with each sensor generating data for 288 time steps daily. Additionally, road network structure data is derived from the connectivity status and actual distances between sensors.

We present a comprehensive multi-sensor dataset comprising 4D mmWave radar point clouds, lidar point clouds, and camera images in the open-pit mine. The dataset encompasses various operational scenarios, including the dumping site, loading site, connecting roads, and haulage maintenance area, under various lighting conditions such as cloudy, dark, daylight, and overcast skies.

Dataset capturedbyrealtimevehicle-mountedcamerasystem,  600 high-quality images was extracted, 480 as training set, 120 as valid set. The images have a resolution of 1600x1200 and encompass three types of pavement defects, that is, cracks, patches and potholes. Our dataset is in YOLO format, YOLO (You Only Look Once) is a popular object detection framework that uses a single neural network to predict bounding boxes and class probabilities for various objects in an image. The YOLO dataset format typically consists of two main components: the image files and the annotation files.