This software library and tool provides a fast and robust solution to extract various railroad infrastructure from dense (MLS) LiDAR point clouds. Primary focus is given to cable and railtrack detection.
Above pipeline result:
HeightGrowth pipeline result:
RailTrack pipeline result:
PoleDetection pipeline result:
CableStaggerCheckingFirstClass pipeline result:
Combined cable and rail detection result:
Track fragmentation cutlines with various algorithms:
- Máté Cserép, Péter Hudoba, Zoltán Vincellér: Robust Railroad Cable Detection in Rural Areas from MLS Point Clouds, In Proceedings of Free and Open Source Software for Geospatial (FOSS4G) Conference, Vol. 18 , Article 2, 2018, DOI: 10.7275/z46z-xh51
- Friderika Mayer: Powerline tracking and extraction from dense LiDAR point clouds, MSc thesis, Eötvös Loránd University, 2020, PDF
- Adalbert Demján: Object extraction of rail track from VLS LiDAR data, MSc thesis, Eötvös Loránd University, 2020, PDF
- Máté Cserép, Adalbert Demján, Friderika Mayer, Tábori Balázs, Péter Hudoba: Effective Railroad Fragmentation And Infrastructure Recognition Based On Dense LiDAR Point Clouds, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, pp. 103–109, 2022, DOI: 10.5194/isprs-annals-V-2-2022-103-2022
- Dénes Ertl: Automatic rail tie recognition and error detection using LiDAR point clouds, MSc thesis, Eötvös Loránd University, 2023, PDF
- Attila Láber: Catenary segmentation and error detection in LiDAR point clouds, MSc thesis, Eötvös Loránd University, 2023, PDF
Please read CONTRIBUTING.md for details on coding conventions.
This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.