Ribana Roscher

Professor of Data Science for Crop Systems

Forschungszentrum Jülich

Germany

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Ribana Roscher is a Professor of Data Science for Crop Systems at Forschungszentrum Jülich in Germany. Her research focuses on enhancing decision support in crop production, data-centric machine learning, and the application of explainable artificial intelligence in remote sensing. Recent articles coalesce around themes such as uncertainty quantification in crop production, climate-conditional satellite image editing, and the assessment of soil salinity using machine learning techniques.

Ribana Roscher

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Articles (20)

Enhancing decision support in crop production: Analyzing conformal prediction for uncertainty quantification

Better, Not Just More: Data-centric machine learning for Earth observation

ClimSat – A diffusion autoencoder model for climate-conditional satellite image editing

Explainability of Subfield Level Crop Yield Prediction Using Remote Sensing

A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning

Assessment of soil salinity using explainable machine learning methods and Landsat 8 images

Meta-learning to address diverse Earth observation problems across resolutions

Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness

Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery

Better, Not Just More: Data-centric machine learning for Earth observation

GrowliFlower: An image time‐series dataset for GROWth analysis of cauLIFLOWER

MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets]

Explainable Machine Learning

Location-Aware Adaptive Normalization: A Deep Learning Approach for Wildfire Danger Forecasting

Reliability Scores From Saliency Map Clusters for Improved Image-Based Harvest-Readiness Prediction in Cauliflower

Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

Image-based analysis of yield parameters in viticulture

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY

Collaborators (7)

Anil Yildiz

Onno Muller

Devis Tuia

Keiller Nogueira

Christian Bauckhage

Michael Schmitt

Jefersson dos Santos

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