A data annotation service

Introduction: ROADVIEW is an EU-funded Horizon Europe Innovation Action aiming to develop robust and cost-efficient in-vehicle perception and decision-making systems for connected and automated vehicles with enhanced performance under harsh weather conditions and different traffic scenarios. https://roadview-project.eu/ . Service Description: Lapland UAS is acquiring a data annotation service for vehicle sensor data gathered in the ROADVIEW project by AVL Software and Functions from Germany. The data has been stored by RISE Research Institute of Sweden. The gathered datasets include 3D point cloud data from three LiDAR sensors, as well as IMU and GNSS data. Lidar data has been recorded using two 16 channel lidars and one 32 channel lidar resulting in around 80k point per frame. Tens of thousands of frames are available for annotations from the project. The requirements for LiDAR 3D point cloud data annotation are as follows: - Point-wise annotation (each point in the point cloud needs to be labelled with high precision) - 3D Bounding boxes for Pedestrians, Cyclists, and Vehicles - Annotation using Semantic KITTI classes (https://www.semantic-kitti.org/dataset.html#classes) - Service needs to be provided by the end of July 2025 (31.7.2025) and labels need to be in a similar format to the KITTI dataset. The tender is implemented as a reverse tender, in which a maximum budget (95.000 €, VAT 0%) is given for the implementation of the annotation service. The budget announced in advance is intended to be used entirely for service provision.

Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them. Project grant no. 101069576. UK participants in this project are co-funded by Innovate UK under contract no.10045139. Swiss participants in this project are co-funded by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract no. 22.00123.