papers

Publications (6)

cs.CV2026

A Novel Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series

Motasem Alkayid, Zhengsen Xu, Saeid Taleghanidoozdoozan +7

Although tree species classification from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data is critical for supporting various environmental applications, it i…

cs.CV2026

White-Box mHC: Electromagnetic Spectrum-Aware and Interpretable Stream Interactions for Hyperspectral Image Classification

Yimin Zhu, Lincoln Linlin Xu, Zhengsen Xu +6

In hyperspectral image classification (HSIC), most deep learning models rely on opaque spectral-spatial feature mixing, limiting their interpretability and hindering understanding…

cs.CV2026

Trustworthy Data-Driven Wildfire Risk Prediction and Understanding in Western Canada

Zhengsen Xu, Lanying Wang, Sibo Cheng +12

In recent decades, the intensification of wildfire activity in western Canada has resulted in substantial socio-economic and environmental losses. Accurate wildfire risk prediction…

cs.CV2026

mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification

Yimin Zhu, Zack Dewis, Quinn Ledingham +6

Recently, DeepSeek has invented the manifold-constrained hyper-connection (mHC) approach which has demonstrated significant improvements over the traditional residual connection in…

cs.CV2026

Clustering-Guided Spatial-Spectral Mamba for Hyperspectral Image Classification

Zack Dewis, Yimin Zhu, Zhengsen Xu +4

Although Mamba models greatly improve Hyperspectral Image (HSI) classification, they have critical challenges in terms defining efficient and adaptive token sequences for improve p…

cs.LG2026

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction

Quinn Ledingham, Zhengsen Xu, Yimin Zhu +6

Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas. However,…