MRSI: A multimodal proximity remote sensing data set for environment perception in rail transit

Rail transit is becoming a major mode of rapid urban and intercity passenger and freight transportation, and its safe operation is of great significance in safeguarding people's lives and properties and maintaining social stability. The current scheme of manual hazard monitoring in rail transit still remains potential safety risks. Accurate rail scene understanding is an essential step towards a smart train. Limited by the closeness of railway scenes, not much research has been conducted on the perception and understanding of rail transit. In view of the above, we propose multimodal remote sensing image (MRSI), the first multimodal proximity remote sensing data set for rail scene understanding. MRSI consists of 27k images collected from freight rail and metro following the pixel and box annotations labeled and checked manually. We used a variety of sensing devices mounted on locomotives to record track scenes under different lighting and weather conditions, including straight, curve, and fork during daytime, dusk, and nighttime, as well as under rainy days. We also include an additional infrared thermometer in the metro environment, propose a new image registration method after synchronous acquisition, and thus construct MRSI combining spatial and radiometric properties. With this data set, we can achieve segmentation of the track area and recognition of obstacles by sensing the environment in front of the train, which lead to rail scene understanding. MRSI is publicly available at .

Automated summary

Rail transit plays a major role in modern urban and intercity transportation, and ensuring its safe operation is crucial for safeguarding lives and maintaining social stability. Current manual hazard monitoring in rail transit poses potential safety risks, highlighting the need for new solutions to achieve intelligent rail scene understanding.