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04/16/2021
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
The modelling of Earth observation data is a challenging problem, typica...
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04/15/2021
Learning User's confidence for active learning
In this paper, we study the applicability of active learning in operativ...
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04/15/2021
A survey of active learning algorithms for supervised remote sensing image classification
Defining an efficient training set is one of the most delicate phases fo...
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12/22/2020
Learning Structures in Earth Observation Data with Gaussian Processes
Gaussian Processes (GPs) has experienced tremendous success in geoscienc...
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12/11/2020
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
In this work we evaluate multi-output (MO) Gaussian Process (GP) models ...
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12/11/2020
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Developing accurate models of crop stress, phenology and productivity is...
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12/07/2020
Randomized kernels for large scale Earth observation applications
Dealing with land cover classification of the new image sources has also...
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12/07/2020
Physics-Aware Gaussian Processes in Remote Sensing
Earth observation from satellite sensory data poses challenging problems...
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12/07/2020
Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
Computationally expensive Radiative Transfer Models (RTMs) are widely us...
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12/07/2020
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
Kernel-based machine learning regression algorithms (MLRAs) are potentia...
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12/05/2020
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes
The availability of satellite optical information is often hampered by t...
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10/18/2020
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, w...
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10/16/2017
Fair Kernel Learning
New social and economic activities massively exploit big data and machin...
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11/21/2013
