17 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 Geographic Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series
Motasem Alkayid, Zhengsen Xu, Saeid Taleghanidoozdoozan +7
Tree species classification from Moderate Resolution Imaging Spectroradiometer (MODIS) time series is critical for environmental monitoring, yet challenging due to subtle inter-spe…
Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification
Yimin Zhu, Lincoln Linlin Xu
Although hyperspectral image (HSI) classification is critical for supporting various environmental applications, it is a challenging task due to the spectral-mixture effect, the sp…
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…
Sparse Data Tree Canopy Segmentation: Fine-Tuning Leading Pretrained Models on Only 150 Images
David Szczecina, Hudson Sun, Anthony Bertnyk +3
Tree canopy detection from aerial imagery is an important task for environmental monitoring, urban planning, and ecosystem analysis. Simulating real-life data annotation scarcity,…
BCWildfire: A Long-term Multi-factor Dataset and Deep Learning Benchmark for Boreal Wildfire Risk Prediction
Zhengsen Xu, Sibo Cheng, Lanying Wang +4
Wildfire risk prediction remains a critical yet challenging task due to the complex interactions among fuel conditions, meteorology, topography, and human activity. Despite growing…
Diffusion Posterior Sampler for Hyperspectral Unmixing with Spectral Variability Modeling
Yimin Zhu, Lincoln Linlin Xu
Linear spectral mixture models (LMM) provide a concise form to disentangle the constituent materials (endmembers) and their corresponding proportions (abundance) in a single pixel.…
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.…
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model
Yimin Zhu, Lincoln Linlin Xu
Data augmentation effectively addresses the imbalanced-small sample data (ISSD) problem in hyperspectral image classification (HSIC). While most methodologies extend features in th…