Chang Liu
Building Damage Estimation After Natural Disaster Using Multi Satellite Source Data Based on Machine Learning
University New South Wales Sydney
Chang Liu is a Postdoc at MIT Senseable City Lab. She completed her PhD at the University of New South Wales (UNSW) Sydney in Australia, with the SmartSat CRC full scholarship. Her research focuses on the application of artificial intelligence (AI) in geospatial engineering and remote sensing for urban-related research, and have been keeping up to date with the development of state-of-the-art technologies in the discipline. Her interests include earth observation, remote sensing, Lidar, semantic segmentation, urban analysis, and natural disaster analysis.
Publications
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- Journal Article
Demystifying the Influencing Factors of Construction 4.0 Technology Implementation from a Sustainability Starting Point: Current Trends and Future Research Roadmap
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- Journal Article
Channel Attention and Normal-Based Local Feature Aggregation Network (CNLNet): A Deep Learning Method for Predisaster Large-Scale Outdoor Lidar Semantic Segmentation
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- Conference Presentation
The Influence of Changing Features on the Accuracy of Deep Learning-Based Large-Scale Outdoor Lidar Semantic Segmentation
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- Conference Presentation
An Improved Luminance Contrast Saliency Map for Burned Area Mapping Based INSAR Coherence Difference Image
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- Conference Presentation
Flood Assessment and Mapping Based on SAR and QUAV Vertical Remote Sensing Framework: A Case Study of 2022 Australia Moama Floods
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- Journal Article
Dielectric Fluctuation and Random Motion over Ground Model (DF-RMoG): An Unsupervised Three-Stage Method of Forest Height Estimation Considering Dielectric Property Changes
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- Journal Article
Bibliometric Analysis of Interferometric Synthetic Aperture Radar (InSAR) Application in Land Subsidence from 2000 to 2021
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- Journal Article
A Novel Attention-based Deep Learning Method for Post-disaster Building Damage Classification
