We aim to harness the big data and the AI revolution to develop innovative technological approaches to assess nature recovery in both fine detail and at large spatial scales.

We are in the midst of an exponential proliferation of data about our environment from sources as varied as a new generation of satellite sensors, social media posts and time-lapse cameras with image recognition. In concert with this data richness, there is immense potential to use AI/machine learning to fuse, interpret and correlate data to work at both fine spatial resolution and large spatial scale in the midst of significant complexity.

For the first time we have the potential to map and model ecological connectivity across whole countries, map different farming approaches or infrastructure in fine detail and track the connectivity of biodiversity associated with different farming landscapes. We will advance state-of-the-art AI approaches to combine different sources of data, including drones, satellite, survey data and social media, that are robust to a range of environmental scenarios, data noise and model reliability.

We will initially focus on three machine learning tasks to address the information requirements of our programmes:

  • The exploitation of existing machine learning technology and subsequent identification and filling of methodological gaps
  • Novel methods for data interpretation
  • Requirements for hardware, imagery and human intervention for cost-efficient, scalable data analytics.


Theme outputs

See all outputs for this theme