Chang Liu

Postdoc, Senseable City Lab

Dr. Liu is currently a postdoctoral researcher at MIT. She completed her PhD at the University of New South Wales (UNSW), Sydney, Australia, in 2024. She is a member of the editorial board of the newly founded journal: Urban and Building Science (UBS)-Scilight

Publications

Unveiling the internal structures of informal settlements through AI-driven automated segmentation of LiDAR data

npj Urban Sustainability

Tabatabaeiasl M

A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins

Science of the Total Environment

Rapid Large-scale Building Damage Level Classification After Earthquakes Using Deep Learning with Lidar and Satellite Optical Data

International Journal of Digital Earth

Huang Y

Sanatani R

Liu C

Kang Y

Zhang F

Liu Y

Duarte F*

Ratti C

No “True” Greenery: Deciphering the Bias of Satellite and Street View Imagery in Urban Greenery Measurement

Building and Environment

2024

Mapping Sidewalk Accessibility with Smartphone Imagery and Visual AI: A Participatory Approach

Philosophical Transactions A

Demystifying the Influencing Factors of Construction 4.0 Technology Implementation from a Sustainability Starting Point: Current Trends and Future Research Roadmap

Channel Attention and Normal-Based Local Feature Aggregation Network (CNLNet): A Deep Learning Method for Predisaster Large-Scale Outdoor Lidar Semantic Segmentation

IEEE Transactions on Geoscience and Remote Sensing

Dielectric Fluctuation and Random Motion over Ground Model (DF-RMoG): An Unsupervised Three-Stage Method of Forest Height Estimation Considering Dielectric Property Changes

A novel attention-based deep learning method for post-disaster building damage classification

Expert Systems with Applications

Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms

Construction Innovation

PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data

ISPRS Journal of Photogrammetry and Remote Sensing

Improved Model-Based Forest Height Inversion Using Airborne L-Band Repeat-Pass Dual-Baseline Pol-InSAR Data

Bibliometric analysis of interferometric synthetic aperture radar (InSAR) application in land subsidence from 2000 to 2021

Journal of Sensors

Towards a deep-learning-based framework of Sentinel-2 imagery for automated active fire detection

Evaluation of classical operators and fuzzy logic algorithms for edge detection of panels at exterior cladding of buildings

Contact

chang967@mit.edu

Links

Completed Project: "Data Clouds"

Completed Project: "Sidewalk AI"

Completed Project: "Green Bias"

Current Projects