The application of AI and machine learning is rapidly growing across the environmental research field. State-of-the-art machine learning techniques can be used to analyse and exploit environmental data, to produce greater insight into the current data captured, and enable better understanding of the environment.
To effectively exploit the benefits of machine learning, NEODAAS has brought together key resources required to enable environmental research excellence. NEODAAS provides AI expertise, support for development of AI/ML pipelines, and access to specialized hardware to support the environmental research community. Through development services, we work with individuals and external collaborators to create bespoke AI solutions for environmental research problems. Throughout model development and deployment, we provide expert advice and knowledge to support users in identifying the specific challenges of their science area, how best to approach their research task, and which technologies would be most appropriate. We also work with users to identify areas where existing AI models and pipelines can be improved, supporting best practices for model accuracy, applicability, and efficiency. This could be through advice, practical support and implementation or optimisation of pre-existing code. We also run regular training courses focusing on practical applications of Machine Learning to environmental datasets. We have supported a variety of academic research into with environmental applications including algal bloom segmentation, tree monitoring from remote sensing and terrestrial LIDAR, glacier front detection, vehicle pollution tracking and underwater image analysis.