AI for Earth and Space Science

Natasha Dudek · Karianne Bergen · Stewart Jamieson · Valentin Tertius Bickel · Will Chapman · Johanna Hansen

When will the San Andreas faultline next experience a massive earthquake? What can be done to reduce human exposure to zoonotic pathogens such as coronaviruses and schistosomiasis? How can robots be used to explore other planets in the search for extraterrestrial life? AI is posed to play a critical role in answering Earth and Space Sciences questions such as these, boosted by continually expanding, massive volumes of data from geo-scientific sensors, remote sensing data from satellites and space probes, and simulated data from high performance climate and weather simulations. The complexity of these datasets, however, poses an inherent challenge to AI, as they are often noisy, may contain time and/or geographic dependencies, and require substantial interdisciplinary expertise to collect and interpret.This workshop aims to highlight work being done at the intersection of AI and the Earth and Space Sciences, with a special focus on model interpretability at the ICLR 2022 iteration of the workshop (formerly held at ICLR 2020 and NeurIPS 2020). Notably, we do not focus on climate change as this specialized topic is addressed elsewhere and our scope is substantially broader. We showcase cutting-edge applications of machine learning to Earth and Space Science problems, including study of the atmosphere, biosphere (ecology), hydrosphere (water), lithosphere (solid Earth), sensors and sampling, and planetary science. We cultivate areas where Earth and planetary science is informing and inspiring new developments in AI, including theoretical developments in interpretable AI models, hybrid models with knowledge-guided AI, augmenting physics-based models with AI, representation learning from graphs and manifolds in spatiotemporal models, and dimensionality reduction. For example, the application of physics-informed AI to fluid dynamics is leading to major advances in weather forecasting, in turn inspiring exciting new hybrid model-based/model-free methods.

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Timezone: America/Los_Angeles »