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pro vyhledávání: '"Prexl, Jonathan"'
SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining
Autor:
Prexl, Jonathan, Schmitt, Michael
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-trained on imagery of different satellites with non-matching spectral o
Externí odkaz:
http://arxiv.org/abs/2408.11000
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 albedo, resource distribution, and many other key factors that influence w
Externí odkaz:
http://arxiv.org/abs/2404.13911
Autor:
Roscher, Ribana, Rußwurm, Marc, Gevaert, Caroline, Kampffmeyer, Michael, Santos, Jefersson A. dos, Vakalopoulou, Maria, Hänsch, Ronny, Hansen, Stine, Nogueira, Keiller, Prexl, Jonathan, Tuia, Devis
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on
Externí odkaz:
http://arxiv.org/abs/2312.05327
Publikováno v:
Journal of Fluid Mechanics, 942, A2
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. Here, we introduce the novel Shapley Additive Explanations (SHAP) alg
Externí odkaz:
http://arxiv.org/abs/2102.05541
Publikováno v:
Chaos: An Interdisciplinary Journal of Nonlinear Science 30.1 (2020): 013113
Machine Learning (ML) inspired algorithms provide a flexible set of tools for analyzing and forecasting chaotic dynamical systems. We here analyze the performance of one algorithm for the prediction of extreme events in the two-dimensional H\'enon ma
Externí odkaz:
http://arxiv.org/abs/2002.10268
Fully automatic large-scale land cover mapping belongs to the core challenges addressed by the remote sensing community. Usually, the basis of this task is formed by (supervised) machine learning models. However, in spite of recent growth in the avai
Externí odkaz:
http://arxiv.org/abs/2002.08254
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