Pivotal technique harnesses cutting-edge AI capabilities to model and
map the natural environment
Date:
March 16, 2022
Source:
University of Exeter
Summary:
Scientists have developed a pioneering new technique that harnesses
the cutting-edge capabilities of AI to model and map the natural
environment in intricate detail.
FULL STORY ========================================================================== Scientists have developed a pioneering new technique that harnesses the cutting-edge capabilities of AI to model and map the natural environment
in intricate detail.
==========================================================================
A team of experts, including Charlie Kirkwood from the University of
Exeter, has created a sophisticated new approach to modelling the Earth's natural features in greater detail and accuracy.
The new technique can recognise intricate features and aspects of the
terrain far beyond the capabilities of more traditional methods and use
these to generate enhanced-quality environmental maps.
Crucially, the new system could also pave the way to unlocking new
discoveries of the relationships within the natural environment, that
may help tackle some of the greater climate and environment issues of
the 21st century.
The study is published in leading journal Mathematical Geosciences,
as part of a special issue on geostatistics and machine learning.
Modelling and mapping the environment is a lengthy, time consuming and expensive process. Cost limits the number of observations that can be
obtained, which means that creating comprehensive spatially-continuous
maps depends upon filling in the gaps between these observations.
========================================================================== Scientists can use a range of information sources to help fill in these observation gaps, such as terrain elevation data and satellite imagery.
However, conventional modelling methods rely on users to manually engineer predictive features from these datasets -- for example generating slope
angles and curvatures from terrain elevation data in the hope that these
can help explain the spatial distribution of the variable being mapped.
However, scientists believe there are likely to be many more nuanced relationships at play within the natural environment that models based
on traditional manual feature-engineering approaches may simply miss.
The pioneering new AI approach, developed in the study, poses
environmental information extraction as an optimisation problem. Doing
so allows it to automatically recognise and make use of relationships
which may otherwise go unnoticed and unutilised by humans using more traditional modelling methods.
In addition to improving map quality, this also unlocks the potential
for the discovery of new relationships in the natural environment by
AI, while simultaneously eliminating huge amounts of trial-and-error experimentation in the modelling process.
Charlie Kirkwood, a postgraduate student at the University of Exeter
said:"To be useful for decision making, we need our models to provide
answers that are as specific as possible while also being trustworthy --
and that means creating accurate measures of the uncertainty associated
with our estimates, which in this case are predictions at unmeasured locations." "Our AI approach is set within a Bayesian statistical
framework which allows us to quantify these uncertainties and provide a
range of uncertainty measures, including credible intervals, exceedance probabilities and other more bespoke products that will feed directly
into decision making processes. Crucially, all this is provided whilst harnessing any available information more effectively than traditional approaches allow -- which you can see coming through in the detail of
the map"
==========================================================================
The new approach was demonstrated using stream sediment calcium
concentration observations from the British Geological Survey's
Geochemical Baseline Survey of the Environment (G-BASE) project.
The distribution of calcium in the environment, which has standalone
importance for its impact on soil fertility, is controlled primarily by
geology -- with different rock types containing different proportions
of calcium -- but also by hydrological processes at the surface.
Calcium therefore provides a challenging use case for the AI approach,
which must learn to recognise and utilise features relating to both
bedrock geology (e.g. differing terrain textures, breaks of slope)and
surface hydrology (e.g.
drainage, river channels).
The method, the scientists say, has produced a spectacularly detailed
and accurate map which, despite depicting just one element -- calcium,
reveals the geology of Britain in arguably a new level of detail thanks
to the information- extracting power of the new AI approach. The team
believe that by combining the research skills, expertise and data
resources of its partners -- the University of Exeter, Met Office,
and British Geological Survey -- this work presents a new dawn for environmental mapping practices in the age of AI.
Professor Gavin Shaddick, from the University of Exeter added "This is a fantastic example of Environmental Intelligence, the use of AI to help
solve challenges in environmental science. This work is an exemplar in integrating technical knowledge of AI and machine learning with expertise
in geosciences to produce new methodology that directly addresses
crucial questions in mapping environmental information. The resulting methodological advances could be used to produce detailed maps of a
wide variety of environmental hazards and have the potential to provide
a rich source of information for both scientists and decision makers."
Garry Baker, Interim Chief Digital Officer, British Geological Survey
added: "This paper is an excellent demonstration of how environmental information such as the BGS geochemical database can be re-assessed via
new approaches (AI spatial interpolation). It exemplifies the benefits of ongoing environmental research and how this can draw upon the extensive datasets available to everyone through the National Geoscience Data
Centre and wider NERC, and UKRI data repositories." Dr Kirstine Dale,
the Met Office's Principal Fellow for Data Science and Co- Director for
Joint Centre for Excellence in Environmental Intelligence commented on
the value of this work: "This is an important example of how data science
has the potential to transform our understanding of the natural world.
Critically, it highlights what can be achieved by working across
disciplines, in this case bringing together mathematicians, weather
specialists and computer scientists enriches our knowledge of the natural
world in a way that no single discipline can."
========================================================================== Story Source: Materials provided by University_of_Exeter. Note: Content
may be edited for style and length.
========================================================================== Journal Reference:
1. Charlie Kirkwood, Theo Economou, Nicolas Pugeault, Henry
Odbert. Bayesian
Deep Learning for Spatial Interpolation in the Presence of
Auxiliary Information. Mathematical Geosciences, 2022; DOI:
10.1007/s11004-021- 09988-0 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/03/220316132637.htm
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