Bird's-Eye View Semantic Segmentation

The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers. Source: PointPainting: Sequential Fusion for 3D Object Detection

The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images. ### Dataset features * land cover from Poland, Central Europe * three spectral bands - RGB * 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px) * 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px) * total area of 216.27 sq. km ### Dataset format * rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system * masks are single-channel GeoTiffs with EPSG:2180 spatial reference system

The SimBEV dataset is a collection of 320 scenes spread across all 11 CARLA maps and contains data from a variety of sensors, including five camera types (RGB, semantic segmentation, instance segmentation, depth, and optical flow), lidar, semantic lidar, radar, GNSS, and IMU, along with 3D object bounding boxes and accurate bird's-eye view (BEV) ground truth. With each scene lasting 16 seconds at a frame rate of 20 Hz, the SimBEV dataset contains 102,400 annotated frames, over 8 million 3D object bounding boxes, and more than 2.5 billion BEV ground truth labels.