- XAI for Early Crop Classification. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2023 more…
Ayshah Xuan Chan, M.Sc.
Ayshah Chan was born in Singapore in 1997. After completing her bachelor's studies in Evironmental Earth System Sciences at Nanyang Technological University in Singapore, she moved to the Technical University of Munich to continue her studies. In March 2023 she graduated from M.Sc. Earth Oriented Science and Technology and from April 2023 she started working as a research associate and PhD student at the Chair of Remote Sensing Technology of TUM.
Focus and Interests
- Machine Learning and Deep Learning
- Natural Language Processing
- Explainability and Interpretability in Deep Learning
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.
The amazing performance of deep learning models ignited the demand for more training data to train better models. An often overlooked data source comes from public service, where significant amounts of administrative data has been collected. These adminstrative data offers a wide variety of knowledge such as argricultural data and parking statistics that can be turned into labels for crop classification and training data for traffic prediction. However a huge challenge in realizing the full potential of administrative data is their inconsistent format which requires data alignment and harmonization. To this end, we propose using large language models to extract information from diverse administrative bodies and create ready to use data.