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 currently a 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

Selected Honors and Awards:

2024 Geospatial Excellence Award Winner NSW, Geospatial Council of Australia

2022 Best Poster, SmartSat CRC Conference, Australia

2022 Female PhD Personal Development Grant, SmartSat CRC, Australia

2022-2023 Development and Research Training Grant, UNSW Sydney, Australia

2020-2023 PhD Full Scholarship, SmartSat CRC, Australia

Publications

Chang Liu
L. G.*
W. X.
Z. D.
Q. Z
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
2023
Chang Liu
Q. Z.*
L. G.
S. ME S.
Z. S
Dielectric Fluctuation and Random Motion over Ground Model (DF-RMoG): An Unsupervised Three-Stage Method of Forest Height Estimation Considering Dielectric Property Changes
Remote Sensing
2023
Chang Liu
S. ME S.
Q. Z.
L. G.*
A novel attention-based deep learning method for post-disaster building damage classification
Expert Systems with Applications
2022
Chang Liu
S. ME S.*
S. S.
G. M
Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms
Construction Innovation
2022
Chang Liu
S. S.
S. ME S.*
A. K
Evaluation of classical operators and fuzzy logic algorithms for edge detection of panels at exterior cladding of buildings
Buildings
2019
Q. Z.
Chang Liu* (corresponding author)
W. Z.
S. M
Demystifying the Influencing Factors of Construction 4.0 Technology Implementation from a Sustainability Starting Point: Current Trends and Future Research Roadmap
Buildings
2024
Q. Z.
L. G
S. H.
G. M.
Chang Liu
R. Z.*
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
2022
Q. Z.
S. H.
R. Z.*
Chang Liu
L. G
Improved Model-Based Forest Height Inversion Using Airborne L-Band Repeat-Pass Dual-Baseline Pol-InSAR Data
Remote Sensing
2022
Y. W.
Chang Liu
Q. Z.*
L. G.*
Bibliometric analysis of interferometric synthetic aperture radar (InSAR) application in land subsidence from 2000 to 2021
Journal of Sensors
2022
Q. Z.
L. G.
R. Z.*
G. M.
Chang Liu
Z. D
Towards a deep-learning-based framework of Sentinel-2 imagery for automated active fire detection
Remote Sensing
2021
Headshot of Chang Liu

Contact

chang967@mit.edu

Current Projects

Favelas 4D