Artificial Intelligence for improving natural capital management and decision support

Hosted by Paula Harrison, Centre for Ecology & Hydrology and Mark Rounsevell, Karlsruhe Institute of Technology

Artificial Intelligence (AI) offers many emerging opportunities to improve natural capital management and to influence real-world natural capital decisions and outcomes. AI methods, such as machine learning, can be applied with the aid of modern cloud computing power to make sense of the transformative potential of ‘big’ data including close to real time satellite imagery, other remote sensing data, and Internet of Things (IoT) enabled devices.

This session will explore how researchers, businesses, government agencies, land managers and others are using these approaches to support decision-making on natural capital. It will consist of short presentations on case studies which are applying AI approaches for specific natural capital decisions followed by an interactive session with participants to identify other areas of potential application, challenges in application, and suggested next steps to push forward the opportunities in this area.


Session introduction – Paula Harrison, Centre for Ecology & Hydrology

Practitioner’s perspective on the potential for AI approaches supporting decision-making on natural capital – David Askew, Natural England

Artificial Intelligence and machine learning adds value to applied ecosystem services modelling – Simon Willcock, Bangor University

Earth observation, machine learning and cloud computing to assess soil-related natural capital and benefits – Alessandro Gimona, The James Hutton Institute

Quantifying cultural ecosystem services in Europe using crowd-sourced photos – Heera Lee, Karlsruhe Institute of Technology

Agent-based modelling of adaptive responses to environmental change amongst land managers to explore the balance of ecosystem supply and demand – Mark Rounsevell, Karlsruhe Institute of Technology

Interactive/Panel discussion


This session is scheduled for Day 1, 21 May 2019, 1pm-2.30pm

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