ARNOLD – Annotated Repository of Navigational Obstacles from LiDAR Data

DESCRIPTION The ARNOLD dataset provides real-world LiDAR data collected across diverse marine environments—including ports, marinas, and open waters—and features both static and dynamic objects. It includes raw point clouds and annotated targets in four categories: quay, motor boat, sailing boat, and ship. The dataset is designed for the training and testing of perception algorithms focused on detection, classification, and tracking. Parsing tools are provided to facilitate easy integration into existing workflows. Detailed information about the dataset composition and usage can be found in the original paper and in the README file included with the dataset. HOW TO CITEPlease do not cite this dataset via Zenodo. Instead, refer to the original journal article in which the dataset is presented and described: Martelli, M., Faggioni, N., & Ponzini, F. (2025). ARNOLD – Annotated Repository of Navigational Obstacles from LiDAR Data. Autonomous Transportation Research. ADDITIONAL MATERIAL For an analysis of the obstacle features of each class and a possible application of class feature extraction for classification purposes using a Random Forest Classifier, please refer to: Ponzini, F., Zaccone, R., & Martelli, M. (2025). LiDAR target detection and classification for ship situational awareness: A hybrid learning approach. Applied Ocean Research, 158, 104552. DOI: https://doi.org/10.1016/j.apor.2025.104552