Wei Liu

Leveraging explainable artificial intelligence and satellite time-series for unravelling nonlinear environmental drivers of post-wildfire vegetation recovery

Understanding post-wildfire vegetation recovery is critical for ecological restoration under climate change. Using the severe March 2024 Yajiang forest fire in China's alpine ecosystem as a case study, we integrated time-series forecasting with interpretable machine learning, aug...

Reliability-Aware Dual-Stream Self-Training for Semi-Supervised Semantic Segmentation of High-Resolution Remote Sensing Imagery

Semantic segmentation of high-resolution remote sensing imagery remains constrained by the high cost of pixel-level annotation. Semi-supervised learning can reduce this dependence by exploiting unlabeled images, but improving pseudo-label reliability often comes at the cost of re...