31 citations · 83 across the 8 of their papers we have counts for
8 papers
Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
Mojgan Madadikhaljan, Jonathan Prexl, Isabelle Wittmann +2
In this work, we present LIANet (Location Is All You Need Network), a coordinate-based neural representation that models multi-temporal spaceborne Earth observation (EO) data for a…
SARFormer -- An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data
Jonathan Prexl, Michael Recla, Michael Schmitt
This manuscript introduces SARFormer, a modified Vision Transformer (ViT) architecture designed for processing one or multiple synthetic aperture radar (SAR) images. Given the comp…
SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining
Jonathan Prexl, Michael Schmitt
This paper introduces SenPa-MAE, a transformer architecture that encodes the sensor parameters of an observed multispectral signal into the image embeddings. SenPa-MAE can be pre-t…
GlobalBuildingMap -- Unveiling the Mystery of Global Buildings
Xiao Xiang Zhu, Qingyu Li, Yilei Shi +3
Understanding how buildings are distributed globally is crucial to revealing the human footprint on our home planet. This built environment affects local climate, land surface albe…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc Rußwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…
Interpreted machine learning in fluid dynamics: Explaining relaminarisation events in wall-bounded shear flows
Martin Lellep, Jonathan Prexl, Bruno Eckhardt +1
Machine Learning (ML) is becoming increasingly popular in fluid dynamics. Powerful ML algorithms such as neural networks or ensemble methods are notoriously difficult to interpret.…