Artificial intelligence (AI) trials have shown that, it can monitor the lineside vegetation securely, inexpensively, quickly and at scale. On-train cameras obtain the images by identifying the vivid biodiversity of flora and fauna species.
It is extremely difficult to monitor plant and animals beside railway tracks to assist better management of lineside habitats. It requires the size of the 20,000-mile rail network in the United Kingdom and the number of specialist surveyors.
However, Network Rail has been working with the UK Centre for Ecology and Hydrology (UKCEH) and technology firm Keen AI to develop creative ways for remotely monitoring biodiversity. They’ve shown that AI can recognize invading species by their tracks, as well as native trees that may be threatened by diseases like ash dieback.
However, the Network Rail is trying to develop creative ways for remotely monitoring biodiversity. The UK Centre for Ecology and Hydrology (UKCEH) and technology firm Keen AI have engaged with this project. They’ve shown that AI can recognize invading species by their tracks. Further they identified that the diseases like ash dieback cause threats for native trees.
This information would enable railway staff to take necessary action to better manage lineside vegetation. This is a part of Network Rail’s aim to achieve biodiversity net gain on its land by 2035.
UKCEH and Keen AI have developed AI software that can recognize some varieties of plants. Those are ash trees, Japanese knotweed, Himalayan Balsam, and the poisonous plant Ragwort.
They’re now working on camera technology that can take clear photos of vegetation on a high-speed train. They’ve already completed two successful trials: one between Birmingham and Aberystwyth, and another between Weymouth and Moreton in Dorset.
“The trials demonstrated that we will be able to monitor lineside vegetation safely, cheaply, quickly and at scale. Our equipment was able to take thousands of clear images from a train travelling at up to 80mph. Our AI software can identify ash and other species to a high level of accuracy.” These words are by Dr Tom August, a computational ecologist at UKCEH.
Innovate UK funded the railway work builds on a previous study, which involved photographing and identifying roadside vegetation. The chief executive of Keen AI, Amjad Karim’s explained.
He reveals: “Network Rail spends £200 million each year on vegetation management; in order to keep the network operational. The aim of our work is to give staff at Network Rail the tools they need to safely and accurately identify where action may be required. We’ve been pushing the boundaries of what is possible when it comes to the speed of the camera, quality of images and size of the system, all while keeping it flexible and low-cost.”
The team plans to improve the system in the following months. They will increase the rate of image collection. Also, it will guarantee that each one is in precise mapping, even at speeds of up to 100 mph. This ensures that, it documents the great majority of trees and plants along a path.
There’s another way AI might potentially monitor lineside biodiversity. That is by identifying animal species from sound recordings or images collected by the remote monitoring stations. This would eliminate the need for on-the-ground surveyors. The UKCEH has tested the use of biodiversity monitoring stations to record bird song and bat noises. The existing AI software accurately identified those. AI software for classifying animal species from pictures is still in development around the world.
The AI effort, according to Network Rail, will complement its sustainability strategy. It outlines fundamental changes to the way the company manages its land. Hence, this will strike a better balance between operating a safe, reliable railway and assisting nature.
“With 52,000 hectares of land to manage and seven million people living close to our railway, monitoring, maintaining, and improving the biodiversity of our land effectively is a monumental and vital task that requires forward-thinking solutions,” says Dr Neil Strong, Network Rail’s Biodiversity Strategy Manager.
Further: “Our collaboration with UKCEH and Keen-AI has shown that using AI to monitor land around the railway can be a safer, faster, more cost-effective, and more comprehensive way of doing so, and we’re excited to see how this technology can be developed further to help us achieve our ultimate goal of achieving a biodiversity net gain by 2035.”
UKCEH is also assisting Network Rail in measuring lineside biodiversity using high-resolution photography from satellites and planes. The objective is to create a detailed national map of all lineside habitats, which is an innovative and safe method. Its researchers then looked at records of species in similar environments in each region. They observe what animals and plants might be present.