Konstantin Klemmer - Learning Neural Representations of our Planet from Earth Observation Data скачать в хорошем качестве

Konstantin Klemmer - Learning Neural Representations of our Planet from Earth Observation Data

Earth observation data captures the natural and built environment of our planet. It comes in different forms, from satellite images to in-situ measurements, but can be mapped into the same geometric space: the sphere of planet Earth. It can be crucial in supporting decision making in the private and public sector alike, from improving flood resilience to forecasting crop yields. But making sense of the vast amounts of Earth observation data is difficult as it is often unstructured, unlabeled and multi-modal. Neural networks have emerged as a powerful tool for processing such large quantities of unstructured data but are not equipped to handle the spatial and temporal dependencies and spherical geometry of Earth observation data. In this talk, I will present a new class of neural network models purpose-built for Earth observation data: geographic location encoders. These models combine the scalability of neural networks with geospatial domain knowledge and traditional intuitions from geodesy and spatial statistics. I will highlight how location encoders can be used for fast and accurate predictive modeling on the sphere, with applications in climate modeling and species distribution modeling. I will then present means of training location encoders in the absence of labels—using globally distributed satellite imagery and contrastive self-supervised learning. The resulting pretrained location encoders produce general-purpose location embeddings that learn the natural and physical characteristics of locations around the world. These embeddings are powerful features in downstream modeling and can help tackle long-standing challenges in geospatial machine learning such as geographic distribution shift. Finally, I will outline future challenges in AI for Earth and present an ambitious goal: “digitally twinning” our planet with the support of large-scale, self-supervised learning and Earth observation data. This session is brought to you by the Cohere For AI Open Science Community - a space where ML researchers, engineers, linguists, social scientists, and lifelong learners connect and collaborate with each other. Thank you to our Community Leads for organizing and hosting this event. If you’re interested in sharing your work, we welcome you to join us! Simply fill out the form at https://forms.gle/ALND9i6KouEEpCnz6 to express your interest in becoming a speaker. Join the Cohere For AI Open Science Community to see a full list of upcoming events: https://tinyurl.com/C4AICommunityApp.