02/2024 – 02/2027

HYPER-AMPLIFAI

The HYPER-AMPLIFAI project is set on harmonizing visual foundation models of AI with hyperspectral imagery through self-supervised learning. Our primary objective is to augment these models, enabling them to accommodate a broader range of input sizes and channel dimensions seamlessly. By embarking on this endeavor, we intend to explore two key applications: forest biomass estimation and soil nutrient prediction from hyperspectral data. This holistic approach aims to amplify the utility and versatility of hyperspectral data, presenting novel solutions to pressing environmental challenges.

The HYPER-AMPLIFAI project, a three-year Helmholtz Imaging initiative, marks a collaborative endeavor between DLR and GFZ.
With a focus on elevating the capabilities of hyperspectral data interpretation via advanced AI techniques, this project gains also support from a Precision Agriculture company, QZ Solutions. Their expertise will be instrumental in the field studies, synergizing the EnMAP hyperspectral data with soil chemical analyses to push the boundaries of Earth observation insights.
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Kontakt

Dr. Andrés Camero Unzueta

Co-Head of Department EO Data Science
German Aerospace Center (DLR)
Remote Sensing Technology Institute (IMF)
EO Data Science
Oberpfaffenhofen, 82234 Weßling