jose adsuara

About jose adsuara

jose adsuara, With an exceptional h-index of 11 and a recent h-index of 11 (since 2020), a distinguished researcher at Universidad de Valencia, specializes in the field of machine learning, numerical relativity.

His recent articles reflect a diverse array of research interests and contributions to the field:

Supervised Machine Learning for the Automatic Classification of Triggers from ASIM/MXGS on board the ISS

Leveraging Crowd-sourced Biodiversity Data for an Enhanced Plant Functional Trait Mapping

Interpretable long short-term memory networks for crop yield estimation

Assessing the Impact of Using Short Videos for Teaching at Higher Education: Empirical evidence from log-files in a Learning Management System

Learning About Student Performance from Moodle Logs in a Higher Education Context

Herramientas y recursos de motivación online para actividades en clase

Fomento del razonamiento crítico mediante la evaluación cruzada: estudio de casos en asignaturas de ciencias

Inferring causal relations from observational long-term carbon and water fluxes records

jose adsuara Information

University

Position

researcher at image and signal processing group ()

Citations(all)

1464

Citations(since 2020)

840

Cited By

946

hIndex(all)

11

hIndex(since 2020)

11

i10Index(all)

15

i10Index(since 2020)

14

Email

University Profile Page

Google Scholar

jose adsuara Skills & Research Interests

machine learning

numerical relativity

Top articles of jose adsuara

Supervised Machine Learning for the Automatic Classification of Triggers from ASIM/MXGS on board the ISS

2024/3/7

Leveraging Crowd-sourced Biodiversity Data for an Enhanced Plant Functional Trait Mapping

2024/3/7

Interpretable long short-term memory networks for crop yield estimation

IEEE Geoscience and Remote Sensing Letters

2023/2/10

Assessing the Impact of Using Short Videos for Teaching at Higher Education: Empirical evidence from log-files in a Learning Management System

IEEE Revista Iberoamericana de Tecnologias del Aprendizaje

2023/8/2

Herramientas y recursos de motivación online para actividades en clase

In-Red 2022-VIII Congreso Nacional de Innovación Educativa y Docencia en Red

2022/10/28

Fomento del razonamiento crítico mediante la evaluación cruzada: estudio de casos en asignaturas de ciencias

2022/10/28

Inferring causal relations from observational long-term carbon and water fluxes records

Scientific Reports

2022/1/31

Learning main drivers of crop progress and failure in Europe with interpretable machine learning

International Journal of Applied Earth Observation and Geoinformation

2021/12/15

Learning unsupervised feature representations of remote sensing data with sparse convolutional networks

Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences

2021/9/27

Global Upscaling of the MODIS Land Cover with Google Earth Engine and Landsat Data

2021/7/11

Physics-aware machine learning for geosciences and remote sensing

2021/7/11

Upscaling plant traits to ecosystem level: blending local biodiversity, global traits databases, and remote sensing data.

EGU General Assembly Conference Abstracts

2021/4

Observations and Geoinformation

2022

Satellite and model-based data integration for crop yield estimation and interpretability in Europe

AGU Fall Meeting Abstracts

2020/12

Living in the physics and machine learning interplay for earth observation

arXiv preprint arXiv:2010.09031

2020/10/18

Learning main drivers of crop dynamics and production in Europe

EGU General Assembly Conference Abstracts

2020/5

Down-scaling MODIS operational vegetation products with machine learning and fused gap-free high resolution reflectance data in Google Earth Engine

EGU General Assembly Conference Abstracts

2020/5

Learning ordinary differential equations from remote sensing data

2020/3/9

Interpretability of Recurrent Neural Networks in Remote Sensing

2020/9/26

See List of Professors in jose adsuara University(Universidad de Valencia)

Co-Authors

academic-engine