Chang Liu

Postdoc, Senseable City Lab

Dr. Chang Liu is a Postdoc at MIT Senseable City Lab. She completed her PhD at the University of New South Wales (UNSW), Sydney, Australia. Her research focused 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.

She is the lead guest editor of the Special Issue of Electronics "New Insights in 2D and 3D Object Detection and Semantic Segmentation". Welcome to submit your papers: https://www.mdpi.com/journal/electronics/special_issues/20N991827I

Publications

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

Liu C* (corresponding author)

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

SmartSat CRC website

Current Projects