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Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks
Lukas Drees, Dereje T. Demie, Madhuri R. Paul +4
Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific…
Data-Centric Digital Agriculture: A Perspective
Ribana Roscher, Lukas Roth, Cyrill Stachniss +1
In response to the increasing global demand for food, feed, fiber, and fuel, digital agriculture is rapidly evolving to meet these demands while reducing environmental impact. This…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc Rußwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…
Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery
Ahmed Emam, Timo T. Stomberg, Ribana Roscher
Natural protected areas are vital for biodiversity, climate change mitigation, and supporting ecological processes. Despite their significance, comprehensive mapping is hindered by…
Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness
Ahmed Emam, Mohamed Farag, Ribana Roscher
Protected natural areas are regions that have been minimally affected by human activities such as urbanization, agriculture, and other human interventions. To better understand and…
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower
Jana Kierdorf, Ribana Roscher
Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can…