3 papers
cs.CV2025
EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images
Huili Huang, Chengeng Liu, Danrong Zhang +7
Rapid post-earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and pro…
cs.CV2025
From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images
Danrong Zhang, Huili Huang, N. Simrill Smith +2
In the aftermath of earthquakes, social media images have become a crucial resource for disaster reconnaissance, providing immediate insights into the extent of damage. Traditional…
cs.CV2023
ScaleNet: An Unsupervised Representation Learning Method for Limited Information
Huili Huang, M. Mahdi Roozbahani
Although large-scale labeled data are essential for deep convolutional neural networks (ConvNets) to learn high-level semantic visual representations, it is time-consuming and impr…