Home Chang Liu 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 PhD candidate in Surveying and Geospatial Engineering at the University of New South Wales (UNSW) Sydney. She completed a Bachelor of Civil Engineering degree at University of Science and Technology Beijing (USTB) and a Master of Philosophy in Built Environment at UNSW Sydney. Her research is focused on data semantic segmentation for building damage assessment using artificial intelligence methods with 2D satellite images and 3D LiDAR point cloud. She received ‘Best Poster’ award of SmartSat CRC Conference 2022. She also worked as a UNSW demonstrator for multiple courses. In addition to research and teaching, she has demonstrated her strong leadership and excellent communication and teamwork skills as the vice-president of Civil and Environment Engineering Research Student Association (CERSA) at UNSW Sydney. CERSA aims at connecting students across the school by organizing various events that promote social gatherings and provide a healthy working environment. Project Title: Building Damage Estimation After Natural Disaster Using Multi Satellite Source Data Based on Machine Learning Publications Journal Article 19/02/2024 Demystifying the Influencing Factors of Construction 4.0 Technology Implementation from a Sustainability Starting Point: Current Trends and Future Research Roadmap Journal Article 01/12/2023 Channel Attention and Normal-Based Local Feature Aggregation Network (CNLNet): A Deep Learning Method for Predisaster Large-Scale Outdoor Lidar Semantic Segmentation Conference Presentation 20/10/2023 The Influence of Changing Features on the Accuracy of Deep Learning-Based Large-Scale Outdoor Lidar Semantic Segmentation Conference Presentation 16/07/2023 Flood Assessment and Mapping Based on SAR and QUAV Vertical Remote Sensing Framework: A Case Study of 2022 Australia Moama Floods Conference Presentation 16/07/2023 An Improved Luminance Contrast Saliency Map for Burned Area Mapping Based INSAR Coherence Difference Image Journal Article 31/03/2023 Dielectric Fluctuation and Random Motion over Ground Model (DF-RMoG): An Unsupervised Three-Stage Method of Forest Height Estimation Considering Dielectric Property Changes Journal Article 01/11/2022 Bibliometric Analysis of Interferometric Synthetic Aperture Radar (InSAR) Application in Land Subsidence from 2000 to 2021 Journal Article 29/04/2022 A Novel Attention-based Deep Learning Method for Post-disaster Building Damage Classification