AI Article Synopsis
- This research introduces a method for locating manholes using distributed fiber optic sensing combined with weakly supervised machine learning.* -
- It innovatively utilizes ambient environmental data for mapping underground cables, aiming to improve efficiency and minimize fieldwork.* -
- The method employs a selective data sampling scheme and an attention-based deep learning model to work effectively with limited annotations, and has been validated using real field data from various fiber networks.*
Article Abstract
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.
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| http://dx.doi.org/10.1364/OE.484083 | DOI Listing |
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