3 papers
cs.LG2026
DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification
Joana Reuss, Ekaterina Gikalo, Marco Körner
Real-world agricultural monitoring is often hampered by severe class imbalance and high label acquisition costs, resulting in significant data scarcity. In few-shot learning (FSL)…
cs.LG2025
Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset
Joana Reuss, Jan Macdonald, Simon Becker +4
Accurate crop-type classification from satellite time series is essential for agricultural monitoring. While various machine learning algorithms have been developed to enhance perf…
cs.LG2025
EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification
Joana Reuss, Jan Macdonald, Simon Becker +2
We introduce EuroCropsML, an analysis-ready remote sensing machine learning dataset for time series crop type classification of agricultural parcels in Europe. It is the first data…