Cable Mapping refers to the process of identifying and documenting the physical layout and connectivity of cables within a network or system. This is crucial for managing and troubleshooting network infrastructure effectively. Cable mapping typically involves creating a visual or written representation of how cables are routed, connected, and labeled.
The growing demand for data, driven by factors like the upcoming 5G technology, has led global telecom carriers to construct extensive optical fiber networks. Accurate localization and visualization of underground fiber cables are crucial for efficient facility maintenance. This is especially important for older cables lacking GPS coordinates and up-to-date route information. While traditional methods rely on vibration sensors like geophones and accelerometers, which are limited to detecting large objects deep underground. A new technology to conduct cable mapping of existing fiber optic cables called Distributed Fiber Optic Sensing (DFOS) technology. By leveraging this approach, meter-level accuracy in cable localization can be achieved in complex underground environments.
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We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field work. To effectively accommodate the weak informativeness of ambient data, a selective data sampling scheme and an attention-based deep multiple instance classification model are adopted, which only requires weakly annotated data. The proposed approach is validated on field data collected by a fiber sensing system over multiple existing fiber networks.
