activity
20202023
most citedFoundation Models for Generalist Geospatial Artificial Intelligence

16 citations · 34 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023★ 16 cited

Foundation Models for Generalist Geospatial Artificial Intelligence

Johannes Jakubik, Sujit Roy, C. E. Phillips +30

Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote se…

cs.CV2023★ 1 cited

Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation

Johannes Jakubik, Michal Muszynski, Michael Vössing +2

Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natur…

cs.LG2022★ 12 cited

A reconfigurable integrated electronic tongue and its use in accelerated analysis of juices and wines

Gianmarco Gabrieli, Michal Muszynski, Patrick W. Ruch

Potentiometric electronic tongues (ETs) leveraging trends in miniaturization and internet of things (IoT) bear promise for facile mobile chemical analysis of complex multicomponent…

cs.LG2021

Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data

Paul Pu Liang, Terrance Liu, Anna Cai +7

Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily col…

cs.LG2020★ 5 cited

Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study

Terrance Liu, Paul Pu Liang, Michal Muszynski +5

Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily co…