Network Rail and ZSL leverage AI for biodiversity monitoring
The UK’s Network Rail, in partnership with the Zoological Society of London (ZSL) and Google Cloud, has embarked on an ambitious project aimed at incorporating biodiversity data into rail operations.
The partners have leveraged machine learning models and an extensive array of sensors throughout the rail estate. Network Rail is one of the UK’s most significant public landholders, with a total estate of approximately 52,000 hectares across its 20,000 kilometres of railway corridor, and has been striving to integrate its vast landholdings with operational efficiencies.
“Great Britain’s vast railway network has the potential to play a critical role in conservation of British wildlife, as well as providing a greener way to travel,” Anthony Dancer, Monitoring & Technology Lead, Conservation & Policy, at ZSL, commented. “Rail networks frequently involve extensive tracts of green space, including verges, unused tracks, and other lineside landholdings. As a result, railways can have multiple, important effects on biodiversity, from acting as refugia for wildlife in urban or intensive agricultural landscapes, to connecting fragmented habitats and enabling species movement via corridors, or even acting as barriers to species dispersal where tracks divide habitats.”
Machine learning models from Google Cloud were used to analyse real-time data from 33 acoustic monitors spread across South London’s Network Rail estate. Using Cloud Storage FUSE, the data was transformed in BigQuery to calculate the frequency of each species for each geographic location, and other trends.
“ZSL intends to use the methods and tools developed during this project to help Network Rail monitor, understand and improve its impact on lineside biodiversity, with a focus on technologies which allow safe, rapid, remote monitoring,” Omer Mahmood, Executive Sponsor ZSL Partnership, at Google Cloud, said.
For rail operators, the partners predict that the results can offer multifaceted benefits for rail operators through better understanding of environmental conditions, helping to enhance safety, reduce maintenance costs, and optimize rail schedules.