22 citations · 38 across the 4 of their papers we have counts for
4 papers · 1 filter
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Daniela Szwarcman, Sujit Roy, Paolo Fraccaro +33
This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time…
Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
Sam Khallaghi, Rahebe Abedi, Hanan Abou Ali +12
The accuracy of mapping agricultural fields across large areas is steadily improving with high-resolution satellite imagery and deep learning (DL) models, even in regions where fie…
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…
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Sam Khallaghi, J. Ronald Eastman, Lyndon D. Estes
Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider…