- [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)
- 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)
- TinyEuroCrops. (other entry) more… Full text ( DOI )
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.
Winter term 2022/23
Supervised projects and theses
- “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