Adapting to climate change: exploring the efficacy of transformer models in forecasting wildfires in Australia

Transformer-driven spatiotemporal deep learning for environmental wildfire risk assessment: spatiotemporal wildfire risk modeling

A multimodal transformer neural network framework for fine-scale wildfire occurrence forecasting

Wildfires have emerged as a major natural hazard under ongoing climate change, posing significant risks to ecosystems, infrastructure, and human life. While ignition events are often anthropogenic, wildfire propagation is predominantly governed by environmental drivers. These include (i) atmospheric conditions (e.g., t...

Seasonality-Aware Hybrid Convolutional Transformer for Antarctic Sea Ice Concentration Forecasting

Antarctic sea ice concentration (SIC) forecasting is an important yet challenging task due to the coexistence of complex spatial structure, long-range temporal dependencies, and strong seasonal variability. Conventional convolution-based models are effective at capturing local spatial patterns, but often have limited a...

Lightweight Deep Learning Models for Real Time Wildfire Detection

Transformer-based Deep Learning for Spatiotemporal Forecasting of Natural Resource Availability

ST-ResFormer is proposed, a spatiotemporal transformer architecture that fuses a graph-attention spatial encoder with a patch-based sparse-attention temporal encoder through a cross-attention fusion block, enabling joint multi-horizon forecasting of three coupled resource variables from heterogeneous data sources.

Evaluation of CNN and U-Net Models for Segmenting and Predicting Wildfires from Satellite Images

Monitoring the environment and disaster management has become difficult due to increasing wildfires. A system that can predict accurately and effectively. Predicting forest fires from remote sensor data is difficult when there is class imbalance, spatial variability and changing situations. The studies has been done on...