Ling Chang

Associate professor

University of Twente

Netherlands

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About

Ling Chang is an Associate Professor at the University of Twente in the Netherlands. Her research focuses on remote sensing, spatial analysis, and machine learning applications, as demonstrated in her recent articles on deformation detection, SAR datasets, and health assessment of railway transition zones. Additionally, she explores advanced techniques for monitoring landslides and structural health using InSAR methods. Through her work, Professor Chang contributes to innovative solutions in environmental and engineering challenges.

Articles (20)

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2025

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2021

Collaborators (10)

Alfredo Nunez

Delft University of Technology

NETHERLANDS

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Freek van Leijen

Delft University of Technology

NETHERLANDS

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Andrew Nelson

University of Twente

NETHERLANDS

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Luigi Lombardo

University of Twente

NETHERLANDS

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Lyndon Estes

Clark University

UNITED STATES

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Alfred Stein

Professor

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

NETHERLANDS

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Hakan Tanyas

Assistant Professor

University of Twente

NETHERLANDS

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Michael Marshall

Assistant Professor

University of Twente

NETHERLANDS

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Mingshu Wang

University of Glasgow

UNITED KINGDOM

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Ramon Hanssen

Delft University of Technology

NETHERLANDS

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Ling Chang

Research Interests

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About

Articles (20)

Localized deformation detection and prediction in wide-range urban environments

Creating and leveraging SAR benchmark datasets to facilitate machine learning application

A Hybrid Neural Model Approach for Health Assessment of Railway Transition Zones With Multiple Data Sources

A Joint Real- and Complex-Valued Network for Classification of Pol(In)SAR Images

Conditional Dual Diffusion for Multimodal Clustering of Optical and SAR Images

Improved Landslide Monitoring in Low-Coherence Mountainous Areas: A Coherence-Enhanced Multitemporal InSAR Approach

An integrated approach for mapping slow-moving hillslopes and characterizing their activity using InSAR, slope units and a novel 2-D deformation scheme

Unveiling spatial pattern in slope units-based landslides using a data-driven GAMM and InSAR techniques

Remote Sensing-Based Structural Health Monitoring and Damage Assessment

Despeckling SAR Images With Log-Yeo–Johnson Transformation and Conditional Diffusion Models

Measuring polycentric urban development with multi-temporal Sentinel-1 SAR imagery: A case study in Shanghai, China

Cropland Mapping in Tropical Smallholder Systems with Seasonally Stratified Sentinel-1 and Sentinel-2 Spectral and Textural Features

Modelling InSAR-derived hillslope velocities with multivariate statistics: A first attempt to generate interpretable predictions

Extraction and Analysis of Radar Scatterer Attributes for PAZ SAR by Combining Time Series InSAR, PolSAR, and Land Use Measurements

A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the Groningen gas field using multi-platform SAR imagery

Combined Detection of Surface Changes and Deformation Anomalies Using Amplitude-Augmented Recursive InSAR Time Series

Dielectric characterization and polarimetric analysis of lunar north polar crater Hermite-A using Chandrayaan-1 Mini-SAR , Lunar Reconnaissance Orbiter (LRO) Mini-RF, and Chandrayaan-2 DFSAR data

Use of LSTM for Sinkhole-Related Anomaly Detection and Classification of InSAR Deformation Time Series

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

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

Collaborators (10)

Alfredo Nunez

Freek van Leijen

Andrew Nelson

Luigi Lombardo

Lyndon Estes

Alfred Stein

Hakan Tanyas

Michael Marshall

Mingshu Wang

Ramon Hanssen

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