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Jeremy Andrew Irvin

2 accepted papers

2025

TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data

ICLR 2025poster

Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they are only able to handle single image inputs, limiting their use for many real-world tasks. In this work, we develop a ne…

2023

GEO-Bench: Toward Foundation Models for Earth Monitoring

NeurIPS 2023poster

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural lang…