Maja Schneider, M.Sc.


Maja Schneider was born in Augsburg, Germany, in 1994. After completing an interdisciplinary bachelor's degree in the fields of computer science, electrical engineering and geodesy at the Munich University of Applied Sciences, she then went on to continue her studies at the Technical University of Munich (TUM). There she graduated in March 2020 from the M.Sc. Robotics, Cognition, Intelligence course with a focus on machine learning. Since July 2020 she has been working as a research associate and PhD student at the Chair of Remote Sensing Technology of TUM as a member of the Computer Vision Research Group.

Her research interests include the theoretical aspects and explainability of both machine learning and deep learning models, and their applicability to Earth observation data.


Focus and interests

  • Specializing in: Time-series, Explainability
  • Computer Vision, Machine Learning, Deep Learning
  • Remote Sensing (earth observation, multi-spectral, optical)



EuroCrops is a dataset for automatic vegetation classification from multi-spectral and multi-temporal satellite data, annotated with official LIPS reporting data from countries of the European Union, curated by the Technical University of Munich and GAF AG. The project is funded by the German Space Agency at DLR on behalf of the BMWI (Federal Ministry for Economic Affairs and Energy). This dataset is publicly available for research causes with the idea in mind to assist in the subsidy control of agricultural self-declarations.

Global Earth Monitor

The Global Earth Monitor (GEM) project is addressing the challenge of continuous monitoring of large areas in a sustainable and cost-effective way. The goal of the project is to establish a new disruptive Earth Observation Data: Exploitation model which will dramatically enhance the utilisation of Copernicus data.



  • Valentin Barriere, Martin Claverie, Maja Schneider, Guido Lemoine, Raphaël d’Andrimont: Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data. Remote Sensing of Environment 305, 2024 more… Full text ( DOI )
  • Schneider, Maja; Schelte, Tobias; Schmitz, Felix; Körner, Marco: EuroCrops: The Largest Harmonized Open Crop Dataset Across the European Union. Nature Scientific Data 10 (1), 2023 more… Full text ( DOI )
  • Bénédicte Bucher, Marcin Grudzień, Nathalie Delattre, Jordi Escriu Paradell, Erwin Folmer, Antonin Garrone, Antje Kügeler, Ángel Lopez, Ed Parsons, Andrea Perego, Jiri Pilar, Jari Reini, Hannes I Reuter, Jill Saligoe-Simmel, Maja Schneider, Jeroen Ticheler: Geodata Discoverabiltity. , Ed.: European Spatial Data Research (EuroSDR): European Spatial Data Research (EuroSDR), 2023, more… Full text (mediaTUM)
  • Chan, Ayshah Xuan; Schneider, Maja; Körner, Marco: XAI for Early Crop Classification. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2023 more…
  • Schneider, Maja; Körner, Marco: Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 2022 more… Full text ( DOI )
  • Schneider, Maja; Marchington, Christian; Körner, Marco: Challenges and Opportunities of Large Transnational Datasets: A Case Study on European Administrative Crop Data. Workshop on Broadening Research Collaborations, 2022Advances in Neural Information Processing Systems (NeurIPS) more… Full text ( DOI )
  • Schneider, Maja; Körner, Marco: [Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention. ReScience C 7 (2), 2021 more… Full text ( DOI ) Full text (mediaTUM)
  • Schneider, Maja; Broszeit, Amelie; Körner, Marco: EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification. Proceedings of the 2021 Conference on Big Data from Space (BiDS '21), Publications Office of the European Union, 2021, 125-128 more… Full text ( DOI ) Full text (mediaTUM)
  • Schneider, Maja ; Körner, Marco: TinyEuroCrops. (other entry) more… Full text ( DOI )


Supervised projects and theses

Specialisation Projects:

  • “Super-Resolution with implicit neural representations” Johann Maximilian Zollner, Specialization Project Photogrammetry and Remote Sensing, Winter Term 20/21
  • “Uncertainty and Unknown classes for Land Cover Classification” Yixuan Wang, Specialization Project Photogrammetry and Remote Sensing, Winter Term 20/21