12 papers
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,…
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…
Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
Mabel Heffring, Lincoln Linlin Xu
Although high-resolution mapping of pan-Arctic sea ice with reliable corresponding uncertainty is essential for operational sea ice concentration (SIC) charting, it is a difficult…
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…
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…
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…
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…
Multitask GLocal OBIA-Mamba for Sentinel-2 Landcover Mapping
Zack Dewis, Yimin Zhu, Zhengsen Xu +5
Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key d…
Knowledge-Aware Mamba for Joint Change Detection and Classification from MODIS Times Series
Zhengsen Xu, Yimin Zhu, Zack Dewis +4
Although change detection using MODIS time series is critical for environmental monitoring, it is a highly challenging task due to key MODIS difficulties, e.g., mixed pixels, spati…
Bayesian Transformer for Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
Mabel Heffring, Lincoln Linlin Xu
Although high-resolution mapping of Pan-Arctic sea ice with reliable corresponding uncertainty is essential for operational sea ice concentration (SIC) charting, it is a difficult…
Spatial-Temporal-Spectral Mamba with Sparse Deformable Token Sequence for Enhanced MODIS Time Series Classification
Zack Dewis, Zhengsen Xu, Yimin Zhu +3
Although MODIS time series data are critical for supporting dynamic, large-scale land cover land use classification, it is a challenging task to capture the subtle class signature…
Sparse Deformable Mamba for Hyperspectral Image Classification
Lincoln Linlin Xu, Yimin Zhu, Zack Dewis +4
Although Mamba models significantly improve hyperspectral image (HSI) classification, one critical challenge is the difficulty in building the sequence of Mamba tokens efficiently.…