AI for Earth Sciences

Earth sciences or geosciences encompasses understanding the physical characteristics of our planet, including its lithosphere, hydrosphere, atmosphere and biosphere, applying all fields of natural and computational sciences. As Earth sciences enters the era of increasing volumes and variety of geoscientific data from sensors, as well as high performance computing simulations, machine learning methods are poised to augment and in some cases replace traditional methods. Interest in the application of machine learning, deep learning, reinforcement learning, computer vision and robotics to the geosciences is growing rapidly at major Earth science and machine learning conferences.

Our workshop proposal AI for Earth sciences seeks to bring cutting edge geoscientific and planetary challenges to the fore for the machine learning and deep learning communities. We seek machine learning interest from major areas encompassed by Earth sciences which include, atmospheric physics, hydrologic sciences, cryosphere science, oceanography, geology, planetary sciences, space weather, geo-health (i.e. water, land and air pollution), volcanism, seismology and biogeosciences. We call for papers demonstrating novel machine learning techniques in remote sensing for meteorology and geosciences, generative Earth system modeling, and transfer learning from geophysics and numerical simulations and uncertainty in Earth science learning representations. We also seek theoretical developments in interpretable machine learning in meteorology and geoscientific models, hybrid models with Earth science knowledge guided machine learning, representation learning from graphs and manifolds in spatiotemporal models and dimensionality reduction in Earth sciences. In addition, we seek Earth science applications from vision, robotics and reinforcement learning. New labelled Earth science datasets and visualizations with machine learning is also of particular interest.