# RailGoerl24: G"orlitz Rail Test Center CV Dataset 2024

arXiv: https://arxiv.org/abs/2504.00204 · HTML: https://mlsanity.com/paper/2504.00204/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [OSDaR23: Open Sensor Data for Rail 2023](https://mlsanity.com/paper/2305.03001/) (2023)
- [FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains](https://mlsanity.com/paper/2002.05665/) (2020)
- [Rail Detection: An Efficient Row-based Network and A New Benchmark](https://mlsanity.com/paper/2304.05667/) (2023)
- [TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation](https://mlsanity.com/paper/2302.14486/) (2023)
- [Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type](https://mlsanity.com/paper/2307.13345/) (2023)
- [RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety](https://mlsanity.com/paper/2102.11730/) (2021)

## Built on by (most cited first, 5 of 5)

- [Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox](https://mlsanity.com/paper/2507.16413/) (2025)
- [OSDaR-AR: Enhancing Railway Perception Datasets via Multi-modal Augmented Reality](https://mlsanity.com/paper/2602.22920/) (2026)
- [RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation](https://mlsanity.com/paper/2603.27112/) (2026)
- [When concept-based XAI is imprecise: Do people distinguish between generalisations and misrepresentations?](https://mlsanity.com/paper/2506.17936/) (2025)
- [Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain](https://mlsanity.com/paper/2604.22507/) (2026)

## Abstract (arXiv)

Driverless train operation for open tracks on urban guided transport and mainline railways requires, among other things automatic detection of actual and potential obstacles, especially humans, in the danger zone of the train's path. Machine learning algorithms have proven to be powerful state-of-the-art tools for this task. However, these algorithms require large amounts of high-quality annotated data containing human beings in railway-specific environments as training data. Unfortunately, the amount of publicly available datasets is not yet sufficient and is significantly inferior to the datasets in the road domain. Therefore, this paper presents RailGoerl24, an on-board visual light Full HD camera dataset of 12205 frames recorded in a railway test center of T"UV S"UD Rail, in G"orlitz, Germany. Its main purpose is to support the development of driverless train operation for guided transport. RailGoerl24 also includes a terrestrial LiDAR scan covering parts of the area used to acquire the RGB data. In addition to the raw data, the dataset contains 33556 boxwise annotations in total for the object class 'person'. The faces of recorded actors are not blurred or altered in any other way. RailGoerl24, available at data.fid-move.de/dataset/railgoerl24, can also be used for tasks beyond collision prediction.