activity
20242026
most citedComparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data

13 citations · 30 across the 7 of their papers we have counts for

collaborators

7 papers

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.CV2025

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…

cs.LG2025★ 4 cited

Improving Long-term Autoregressive Spatiotemporal Predictions: A Proof of Concept with Fluid Dynamics

Hao Zhou, Sibo Cheng

Data-driven methods are emerging as efficient alternatives to traditional numerical forecasting, offering fast inference and lower computational cost. Yet, for complex systems, lon…

cs.CV2025★ 13 cited

Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data

Yihang Zhou, Ruige Kong, Zhengsen Xu +2

Facing the escalating threat of global wildfires, numerous computer vision techniques using remote sensing data have been applied in this area. However, the selection of deep learn…

cs.CE2025★ 8 cited

PyTorchFire: A GPU-Accelerated Wildfire Simulator with Differentiable Cellular Automata

Zeyu Xia, Sibo Cheng

Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges…

cs.LG2024

Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations

Yilin Zhuang, Sibo Cheng, Karthik Duraisamy

Diffusion models have gained attention for their ability to represent complex distributions and incorporate uncertainty, making them ideal for robust predictions in the presence of…

cs.LG2024★ 5 cited

Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data

Zhengsen Xu, Jonathan Li, Sibo Cheng +8

Wildfires pose a significant threat to ecosystems, wildlife, and human communities, leading to habitat destruction, pollutant emissions, and biodiversity loss. Accurate wildfire ri…