22 citations · 73 across the 10 of their papers we have counts for
8 papers · 1 filter
GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI
Naomi Simumba, Nils Lehmann, Paolo Fraccaro +9
Geospatial Foundation Models (GeoFMs) are transforming Earth Observation (EO), but evaluation lacks standardized protocols. GEO-Bench-2 addresses this with a comprehensive framewor…
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…
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…
LandCoverNet: A global benchmark land cover classification training dataset
Hamed Alemohammad, Kevin Booth
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and val…
Generating Synthetic Multispectral Satellite Imagery from Sentinel-2
Tharun Mohandoss, Aditya Kulkarni, Daniel Northrup +2
Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications. Building supervised machine learning models based on…
Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks
Aditya Kulkarni, Tharun Mohandoss, Daniel Northrup +2
Semantic segmentation of satellite imagery is a common approach to identify patterns and detect changes around the planet. Most of the state-of-the-art semantic segmentation models…
Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020
Yannis Kalantidis, Laura Sevilla-Lara, Ernest Mwebaze +3
This is the proceedings of the Computer Vision for Agriculture (CV4A) Workshop that was held in conjunction with the International Conference on Learning Representations (ICLR) 202…
Generating a Training Dataset for Land Cover Classification to Advance Global Development
Yoni Nachmany, Hamed Alemohammad
Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datase…