Self-supervised learning for geospatial data

Claire Monteleoni (Inria Paris and University of Colorado Boulder)
January 26, 2024 — 11:00 — "new L2S location (IBM building), 3rd floor room" (and Teams)

Abstract

Digitized data has become abundant, especially in the geosciences, but preprocessing it to create the “labeled data” needed for supervised machine learning is often costly, time-consuming, or even impossible. Fortuitously, in very large-scale data domains, “self-supervised” machine learning methods are now actually outperforming supervised learning methods. In this talk, I will first define self-supervised deep learning, including the notion of a “pretext task.” Then I will survey our lab’s work developing self-supervised learning approaches for several tasks in the geosciences, such as downscaling spatiotemporal data and detecting anomalies in remotely-sensed imagery.

Bio

Claire Monteleoni is a Choose France Chair in AI and a Research Director at INRIA Paris, a Professor in the Department of Computer Science at the University of Colorado Boulder, and the founding Editor in Chief of Environmental Data Science, a Cambridge University Press journal, launched in December 2020. She joined INRIA in 2023 and has previously held positions at University of Paris-Saclay, CNRS, George Washington University, and Columbia University. She completed her PhD and Masters in Computer Science at MIT and was a postdoc at UC San Diego. She holds a Bachelor’s in Earth and Planetary Sciences from Harvard. Her research on machine learning for the study of climate change helped launch the interdisciplinary field of Climate Informatics. She co-founded the International Conference on Climate Informatics, which turned 12 years old in 2023, and has attracted climate scientists and data scientists from over 20 countries and 30 U.S. states. She gave an invited tutorial: Climate Change: Challenges for Machine Learning, at NeurIPS 2014. She currently serves on the NSF Advisory Committee for Environmental Research and Education.