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Izar Azpiroz
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2020 – today
- 2024
- [c6]Antonio Nappa
, Juan Luis Ferrando Chacón
, Izar Azpiroz
, Pedro José Arrazola
:
An Autoencoder-Based Approach for Anomaly Detection of Machining Processes Using Acoustic Emission Signals. EANN 2024: 28-40 - 2023
- [c5]Pietro Soglia, Paula Gonzalez, Izar Azpiroz, Urtzi Otamendi, Marco Quartulli, Sergio Salata:
Estimating NDVI from SAR Images Using Conditional Generative Adversarial Networks. IGARSS 2023: 5222-5225 - 2022
- [c4]Urtzi Otamendi
, Izar Azpiroz, Marco Quartulli, Igor G. Olaizola:
Integrating Pre-Processing Pipelines in ODC Based Framework. IGARSS 2022: 4094-4097 - [c3]Izar Azpiroz, Marco Quartulli, Igor G. Olaizola:
Methodology for Online Phenology Prediction Service Creation. IGARSS 2022: 5917-5920 - [i2]Urtzi Otamendi, Izar Azpiroz, Marco Quartulli, Igor G. Olaizola, Francisco J. Perez, David Alda, Xabier Garitano:
Geo-imagery management and statistical processing in a regional context using Open Data Cube. CoRR abs/2210.01470 (2022) - [i1]Urtzi Otamendi, Izar Azpiroz, Marco Quartulli, Igor G. Olaizola:
Integrating pre-processing pipelines in ODC based framework. CoRR abs/2210.01528 (2022) - 2021
- [j3]Izar Azpiroz
, Noelia Oses, Marco Quartulli
, Igor G. Olaizola
, Diego Guidotti, Susanna Marchi:
Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods. Remote. Sens. 13(6): 1224 (2021) - [c2]Urtzi Otamendi
, Izar Azpiroz, Marco Quartulli, Igor G. Olaizola, Francisco J. Perez, David Alda, Xabier Garitano:
Geo-Imagery Management and Statistical Processing in a Regional Context Using Open Data Cube. IGARSS 2021: 5676-5679 - 2020
- [j2]Izar Azpiroz, Hélène Barucq
, Julien Diaz
, Rabia Djellouli:
An effective numerical strategy for retrieving all characteristic parameters of an elastic scatterer from its FFP measurements. J. Comput. Phys. 419: 109683 (2020) - [j1]Noelia Oses
, Izar Azpiroz
, Susanna Marchi, Diego Guidotti, Marco Quartulli
, Igor G. Olaizola
:
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 20(21): 6381 (2020) - [c1]Noelia Oses
, Izar Azpiroz, Marco Quartulli, Igor G. Olaizola, Susanna Marchi, Diego Guidotti:
Machine Learning for olive phenology prediction and base temperature optimisation. GIoTS 2020: 1-6
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