Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward
Earth observation (EO) is increasingly used for mapping and monitoring processes
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …
[HTML][HTML] A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research
Spectral Indices derived from multispectral remote sensing products are extensively used to
monitor Earth system dynamics (eg vegetation dynamics, water bodies, fire regimes). The …
monitor Earth system dynamics (eg vegetation dynamics, water bodies, fire regimes). The …
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …
Correlation constraints for regression models: Controlling bias in brain age prediction
In neuroimaging, the difference between chronological age and predicted brain age, also
known as brain age delta, has been proposed as a pathology marker linked to a range of …
known as brain age delta, has been proposed as a pathology marker linked to a range of …
AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
G Camps-Valls, MÁ Fernández-Torres… - arXiv preprint arXiv …, 2024 - arxiv.org
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth
system sciences. Here, AI improved weather forecasting, model emulation, parameter …
system sciences. Here, AI improved weather forecasting, model emulation, parameter …
Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and Aerial Robot Coordination for Search and Rescue Tasks
Humanoid robots offer significant advantages for search and rescue tasks, thanks to their
capability to traverse rough terrains and perform transportation tasks. In this study, we …
capability to traverse rough terrains and perform transportation tasks. In this study, we …
Improvement of variables interpretability in kernel PCA
Background Kernel methods have been proven to be a powerful tool for the integration and
analysis of high-throughput technologies generated data. Kernels offer a nonlinear version …
analysis of high-throughput technologies generated data. Kernels offer a nonlinear version …
Gaussian Derivative Change-point Detection for early warnings of industrial system failures
An early warning of future system failure is essential for conducting predictive maintenance
and enhancing system availability. This paper introduces a three-step framework for …
and enhancing system availability. This paper introduces a three-step framework for …
A Comparative Study of Higher Order Kernel Estimation and Kernel Density Derivative Estimation of the Gaussian Kernel Estimator with Data Application
SI Uzuazor, OS Amaju - Pakistan Journal of Statistics and Operation …, 2023 - pjsor.com
Higher-order kernel estimation and kernel density derivative estimation are techniques for
reducing the asymptotic mean integrated squared error in nonparametric kernel density …
reducing the asymptotic mean integrated squared error in nonparametric kernel density …
Nested Latin Hypercube-based Sampling for efficient Uncertainty Quantification using Sensitivity Assisted Least Squares SVM
KS Sidhu, R Khazaka - IEEE Transactions on Components …, 2024 - ieeexplore.ieee.org
In [1], we proposed the methodology to use the sensitivity information for building the Least-
Squares SVM-based surrogate model for uncertainty quantification in the context of circuit …
Squares SVM-based surrogate model for uncertainty quantification in the context of circuit …