3 citations · 9 across the 5 of their papers we have counts for
6 papers
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)…
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…
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…
XAI for Early Crop Classification
Ayshah Chan, Maja Schneider, Marco Körner
We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop cl…
BreizhCrops: A Time Series Dataset for Crop Type Mapping
Marc Rußwurm, Charlotte Pelletier, Maximilian Zollner +2
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosp…
FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANs
Sandra Aigner, Marco Körner
We introduce a new encoder-decoder GAN model, FutureGAN, that predicts future frames of a video sequence conditioned on a sequence of past frames. During training, the networks sol…