Markus Reichstein — accepted AI-conference papers

2024

Bridging Remote Sensors with Multisensor Geospatial Foundation Models

Boran Han, Shuai Zhang, Xingjian Shi, Markus Reichstein

CVPR 2024poster

In the realm of geospatial analysis the diversity of remote sensors encompassing both optical and microwave technologies offers a wealth of distinct observational capabilities. Recognizing this we present msGFM a multisensor geospatial foundation model that effectively unifies data from four key sen…

Cited by 22 Source ↗PDF ↗code ↗Scholar ↗

2024

Multi-modal Learning for Geospatial Vegetation Forecasting

Vitus Benson, Claire Robin, Christian Requena-Mesa, Lazaro Alonso, Nuno Carvalhais, José Cortés +4

CVPR 2024poster

Precise geospatial vegetation forecasting holds potential across diverse sectors including agriculture forestry humanitarian aid and carbon accounting. To leverage the vast availability of satellite imagery for this task various works have applied deep neural networks for predicting multispectral im…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗