Raul Zurita-Milla

Head of department/prof.dr.

University of Twente

Netherlands

Research Interests

Explore related searches

Professors in Netherlands

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Raul Zurita-Milla is the Head of Department at the University of Twente in the Netherlands. His research interests include geospatial machine learning, spatial autocorrelation, and environmental modeling, as evidenced by his recent articles focusing on topics such as adversarial validation, wastewater variability, and land cover classification. He is actively contributing to advancements in quantitative methods for geospatial data analysis.

Articles (13)

Year:

2025

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Collaborators (7)

Ourania Kounadi

Universität Wien

AUSTRIA

View details

Michelle Picoli

Université catholique de Louvain

BELGIUM

View details

Lorenzo Carlos Quesada Ruiz

Profesor Contratado Doctor

Universidad de Las Palmas de Gran Canaria

SPAIN

View details

Victor F. Rodriguez-Galiano

Associate Professor

Universidad de Sevilla

SPAIN

View details

Mahdi Farnaghi

University of Twente

NETHERLANDS

View details

Frank Ostermann

Associate Professor

NETHERLANDS

View details

Emma Izquierdo-Verdiguier

University of Natural Resources and Life Sciences

AUSTRIA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Raul Zurita-Milla

Research Interests

Explore related searches

Contact this professor

About

Articles (13)

On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction

A multi-decadal 1 km gridded database of continental-scale spring onset products

Modeling spatiotemporal domestic wastewater variability

Spatial+: A new cross-validation method to evaluate geospatial machine learning models

Spatiotemporal domestic wastewater variability: Assessing implications of population mobility in pollutants dynamics

Area and Feature Guided Regularised Random Forest: a novel method for predictive modelling of binary phenomena. The case of illegal landfill in Canary Island

CGC: a Scalable Python Package for Co- and Tri-Clustering of Geodata Cubes

Incorporating Spatial Autocorrelation in Machine Learning Models Using Spatial Lag and Eigenvector Spatial Filtering Features

Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

Land Cover Classification Using Extremely Randomized Trees: A Kernel Perspective

Exploring Spring Onset at Continental Scales: Mapping Phenoregions and Correlating Temperature and Satellite-Based Phenometrics

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

A Multiscale Random Forest Kernel for Land Cover Classification

Collaborators (7)

Ourania Kounadi

Michelle Picoli

Lorenzo Carlos Quesada Ruiz

Victor F. Rodriguez-Galiano

Mahdi Farnaghi

Frank Ostermann

Emma Izquierdo-Verdiguier

Make your next move.