131 citations · 509 across the 18 of their papers we have counts for
5 papers · 1 filter
Confidence-Filtered Relevance (CFR): An Interpretable and Uncertainty-Aware Machine Learning Framework for Naturalness Assessment in Satellite Imagery
Ahmed Emam, Ribana Roscher
Protected natural areas play a vital role in ecological balance and ecosystem services. Monitoring these regions at scale using satellite imagery and machine learning is promising,…
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing
Hiba Najjar, Miro Miranda, Marlon Nuske +2
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is e…
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…
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…
Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte +1
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively rece…