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Ribana Roscher

Head of Data Science for Crop Systems Group

Forschungszentrum Jülich

Germany

Research Interests

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About

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.

Articles (29)

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Collaborators (16)

Masahiro Ryo

Professor, Chair of Environmental Data Science

Freie Universität Berlin

GERMANY

Ribana Roscher

Research Interests

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About

Articles (29)

LC-SLab — An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels

PanX.4: A Gyrocopter-Borne Six-Band VNIR Multicamera System for Sentinel-2-Aligned Multitemporal Vegetation Monitoring

An assessment of data-centric methods for label noise identification in remote sensing data sets

How to enrich training data for machine learning-based landslide hazard prediction with spatio-temporal precipitation information?

A Decade of Deep Learning in Remote Sensing: A Systematic Review of Reviews

Confidence-Filtered Relevance (CFR): An Interpretable and Uncertainty-Aware Machine Learning Framework for Naturalness Assessment in Satellite Imagery

Core-Set Selection for Data-Efficient Land Cover Segmentation

NaT-ReX: Naturalness Assessment with Transformer-Based Reliable Explainability

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 Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning

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 (16)

Masahiro Ryo

Lasse Klingbeil

Michael Kampffmeyer

Caroline Gevaert

Anil Yildiz

Onno Muller

Devis Tuia

Begüm Demir

Keiller Nogueira

Christian Bauckhage

Uwe Rascher

Michael Schmitt

Elif Sertel

Marc Rußwurm

Julia Kowalski

Jefersson dos Santos

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