4 citations · 18 across the 5 of their papers we have counts for
5 papers
Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal
Filip Sabo, Martin Claverie, Michele Meroni +1
This paper investigated the potential of a multivariate Transformer model to forecast the temporal trajectory of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR…
Quantum Machine Learning for Remote Sensing: Exploring potential and challenges
Artur Miroszewski, Jakub Nalepa, Bertrand Le Saux +1
The industry of quantum technologies is rapidly expanding, offering promising opportunities for various scientific domains. Among these emerging technologies, Quantum Machine Learn…
Super-resolved rainfall prediction with physics-aware deep learning
S. Moran, B. Demir, F. Serva +1
Rainfall prediction at the kilometre-scale up to a few hours in the future is key for planning and safety. But it is challenging given the complex influence of climate change on cl…
Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data
J. F. Amieva, A. Austoni, M. A. Brovelli +4
Change detection (CD) methods have been applied to optical data for decades, while the use of hyperspectral data with a fine spectral resolution has been rarely explored. CD is app…
EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification
Maja Schneider, Amelie Broszeit, Marco Körner
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of…