This is a Winter Driving Dataset with high-quality GNSS/INS readings, forward-facing images, and 32-channel lidar scans (top mounted) for 1000 samples. Road segmentation annotations are provided for the images. The data is used in this paper. The source code for the paper is available in github. Image data has been anonymized so that it doesn't contain personal data. This work was funded by Henry Ford Foundation Finland and Aalto Doctoral School. The exact sensor setup is: GNSS/INS: Novatel PWRPAK 7DE2 Camera: FLIR blackfly S 2448x2048 Lidar: Velodyne VLP-32C The dataset is divided into countryside and suburb subsets, both containing a test sequence with 400 samples and a validation sequence with 100 samples. The file structure is as follows: finnish_winter_driving_dataset/├─ processed/│ ├─ countryside_test/│ │ ├─ data/│ │ │ ├─ images/│ │ │ │ ├─ 0.png│ │ │ │ ├─ ...│ │ │ ├─ scans/│ │ │ │ ├─ 0.npy│ │ │ │ ├─ ...│ │ │ ├─ pose_ids.txt│ │ │ ├─ poses.csv│ │ ├─ labels│ │ │ ├─ 0.png│ │ │ ├─ ...│ ├─ countryside_validation/│ │ ├─ ...│ ├─ suburb_test/│ │ ├─ ...│ ├─ suburb_validation/│ │ ├─ ... ├─ calib.yaml images folder contains undistorted images scans folder contains lidar scans separated into scan rings in numpy npy format. poses.csv contains latitude, longitude, and azimuth measurements at 10 Hz pose_ids.txt contains the id of each sample in poses.csv. The pose for the n:th sample can be retrieved by taking the n:th pose_id from pose_ids.txt and then taking the pose_id:th value from poses.csv. labels folder contains the manual road segmentation ground truth labels for the images calib.yaml contains the extrinsic and intrinsic calibration parameters. If you use this dataset, please cite @article{alamikkotervo2024trajectory, title={Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions}, author={Alamikkotervo, Eerik and Toikka, Henrik and Tammi, Kari and Ojala, Risto}, journal={arXiv preprint arXiv:2412.02370}, year={2024} }