Izar Azpiroz

Orcid: 0000-0003-0401-8139

According to our database1, Izar Azpiroz authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

2020
2021
2022
2023
2024
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Links

On csauthors.net:

Bibliography

2024
Probabilistic Bayesian Neural Networks for olive phenology prediction in precision agriculture.
Ecol. Informatics, 2024

Effect of Multitasking on Cognitive Functions, Performance, and Bio-Signals in First Responders.
IEEE Access, 2024

Averaging Strategy to Improve SAR-To-NDVI Estimations in A Region of Interest.
Proceedings of the IGARSS 2024, 2024

Pixel-Level Quality Indicator for Image Data Annotation.
Proceedings of the IGARSS 2024, 2024

Adaptation of Diffusion Models for Remote Sensing Imagery.
Proceedings of the IGARSS 2024, 2024

An Autoencoder-Based Approach for Anomaly Detection of Machining Processes Using Acoustic Emission Signals.
Proceedings of the Engineering Applications of Neural Networks, 2024

Deep Learning Models to Estimate High Resolution NDVI for Multiple Augmentation Factors.
Proceedings of the IEEE International Conference on Omni-layer Intelligent Systems, 2024

2023
Estimating NDVI from SAR Images Using Conditional Generative Adversarial Networks.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

2022
Integrating Pre-Processing Pipelines in ODC Based Framework.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

Methodology for Online Phenology Prediction Service Creation.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods.
Remote. Sens., 2021

Geo-Imagery Management and Statistical Processing in a Regional Context Using Open Data Cube.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

2020
Analysis of Copernicus' ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction.
Sensors, 2020

An effective numerical strategy for retrieving all characteristic parameters of an elastic scatterer from its FFP measurements.
J. Comput. Phys., 2020

Machine Learning for olive phenology prediction and base temperature optimisation.
Proceedings of the 2020 Global Internet of Things Summit, 2020


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