SHARE

Building and Deploying a Train Mounted Camera for Remote Vegetation Management

Britain’s 20,000-mile rail network requires reliable monitoring of lineside vegetation. Together with the UK Centre for Ecology & Hydrology (UKCEH), Keen AI has developed an innovative solution to remotely monitor biodiversity using a high resolution camera system.

Hamzah Reta from Keen AI shares his experiences having worked on the project and plans for the future to further develop the camera system.

What Did You Do?

The project was undertaken as part of Network Rail’s Biodiversity Action Plan and it was our task to develop a camera system to help monitor flora along the rail-side. We developed a solution that can be mounted to the front of a train and captures high resolution imagery of lineside vegetation while the train is in transit. 

We worked closely with suppliers to make amendments to an existing camera housing box so it was able to accommodate the new camera system. With the help of CAD drawings for the redesign process, our new design made sure of a securely mounted camera system and a side-facing window, to allow us to capture imagery whilst the camera is facing perpendicular to the track and motion of the train.

CAD Drawings for Camera Housing

The camera system consisted of a number of components:

  • Mounting for a variety of cameras we were trialling
  • A small compute device to control the camera and receive remote commands
  • A GPS logger, kept in the train cabin to accurately log the image location within 10 meters
  • A power bank to power the camera and compute device

We were able to control the system remotely by sending commands to the compute device.

Web Application Merges Satellite and Train Captured Data

What Challenges Did You and Your Team Have?

The biggest challenge we faced was building a system robust enough to function reliably when mounted on a train. Initial trials highlighted extra factors, such as spotty mobile and GPS coverage, which affected the reliability of the system when the train is in motion. The process of trialling different cameras on a train was time-consuming and therefore costly.

We also encountered GPS positioning drift and moved the GPS from the mounted camera housing into the train cabin. It is important to have an accurate GPS location of the images within 10 metres, as this accuracy enables Network Rail to identify where action is required to protect and manage lineside vegetation.

What Are You Planning To Do Next?

In the coming months, we will work further with Network Rail and UKCEH to improve the camera system as well as deploying AI to classify tree species and habitats. Currently, taking one image every 1 second means missing chunks of the rail-side and where species are. We are developing a solution with a global electronic shutter to increase our coverage of the lineside significantly.

Furthermore, we aim to create models to accurately identify five key tree species in the UK. Keen AI data scientist, Petar Gyurov, has already successfully trained one model to identify species of Ash in winter and we hope to train four further models to identify other tree species.  

Newsletter Sign Up

Stay up to date with our work, and industry-related developments.

Sarah Bauroth

Sarah Bauroth

Sarah is passionate about sustainability, championing environmental causes to protect our natural world. She is excited by the potential of AI in supporting these goals.

More Posts from 

Sarah Bauroth

Blog
Sarah Bauroth

Keen AI Book Club

As part of our book club, we read a different book every month. The only rules are, that none of

Read More >
Computer Vision and Ecology
Podcasts
Sarah Bauroth

Computer vision, Ecology and Biodiversity

Network Rail is one of the biggest landholders in the UK.

Amjad hosts a conversation where they discuss how Network Rail is using train mounted cameras, satellite data and monitoring stations to get a baseline on the biodiversity along their tracks and then to protect that biodiversity whilst running a safe and reliable train network.

Tom talks about UKCEH’s work developing remote moth stations for detecting species of moths in a habitat and their work bringing AI and technology to the public and citizen scientists.

Read More >
The Future of Work and the Consolidation of Data
Podcasts
Sarah Bauroth

The Future of Work and the Consolidation of Data

In this episode, Amjad discusses the challenges societies face from companies who control the data upon which a lot of the applications and models we develop depend upon. We also discuss how AI and the rise of remote working mean for the future of work.

Read More >