1.3k citations · 1.7k across the 15 of their papers we have counts for
15 papers
Weakly-semi-supervised object detection in remotely sensed imagery
Ji Hun Wang, Jeremy Irvin, Beri Kohen Behar +4
Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models oft…
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez +14
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 do…
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems
Cara Van Uden, Jeremy Irvin, Mars Huang +4
Self-supervised learning (SSL) enables label efficient training for machine learning models. This is essential for domains such as medical imaging, where labels are costly and time…
Improving debris flow evacuation alerts in Taiwan using machine learning
Yi-Lin Tsai, Jeremy Irvin, Suhas Chundi +3
Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfa…
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping
Bryan Zhu, Nicholas Lui, Jeremy Irvin +7
Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is…
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Alexandre Lacoste, Evan David Sherwin, Hannah Kerner +9
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downst…