3 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring
Maja Schneider, Marco Körner
With leaps in machine learning techniques and their applicationon Earth observation challenges has unlocked unprecedented performance across the domain. While the further developme…
Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data
Valentin Barriere, Martin Claverie, Maja Schneider +2
Accurate in-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patter…
EuroCrops: All you need to know about the Largest Harmonised Open Crop Dataset Across the European Union
Maja Schneider, Tobias Schelte, Felix Schmitz +1
EuroCrops contains geo-referenced polygons of agricultural croplands from 16 countries of the European Union (EU) as well as information on the respective crop species grown there.…
Challenges and Opportunities of Large Transnational Datasets: A Case Study on European Administrative Crop Data
Maja Schneider, Christian Marchington, Marco Körner
Expansive, informative datasets are vital in providing foundations and possibilities for scientific research and development across many fields of study. Assembly of grand datasets…
EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification
Maja Schneider, Amelie Broszeit, Marco Körner
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of…