Alfred Stein

Professor

University of Twente Faculty of Geo-Information Science and Earth Observation ITC

Netherlands

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Professor Alfred Stein is a faculty member at the University of Twente Faculty of Geo-Information Science and Earth Observation ITC in the Netherlands. His research areas include remote sensing applications in agriculture, land use mapping, and environmental monitoring, as evidenced by his recent publications on topics such as yield predictions, crop mapping, and sinkhole detection. He is also involved in innovative methodologies like few-shot learning and the analysis of spatiotemporal patterns using advanced satellite imagery.

Articles (9)

Year:

2024

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2024

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2024

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2022

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2021

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2020

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2020

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2020

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2020

Collaborators (4)

Ling Chang

Associate professor

University of Twente

NETHERLANDS

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Luis Gomez

Associate Professor

Universidad de Las Palmas de Gran Canaria

SPAIN

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Mariana Belgiu

Assistant Professor

University of Twente Faculty of Geo-Information Science and Earth Observation ITC

NETHERLANDS

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Hossein Aghababaei

University of Twente

NETHERLANDS

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Alfred Stein

Research Interests

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Articles (9)

Mapping Integrated Crop–Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning

Few-Shot Learning for Crop Mapping from Satellite Image Time Series

Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data

Polarization Optimization for the Detection of Multiple Persistent Scatterers Using SAR Tomography

Sinkhole Scanner: A New Method to Detect Sinkhole-Related Spatio-Temporal Patterns in InSAR Deformation Time Series

Automatic Detection of Individual Trees from VHR Satellite Images Using Scale-Space Methods

Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

Investigating the Retention of Solar Wind Implanted Helium-3 on the Moon from the Analysis of Multi-Wavelength Remote Sensing Data

Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China

Collaborators (4)

Ling Chang

Luis Gomez

Mariana Belgiu

Hossein Aghababaei

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