Wildland fires are one of the most dangerous natural risks, causing significant economic
damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts …

In recent years, machine learning (ML) and deep learning (DL) have been the leading
approaches to solving various challenges, such as disease predictions, drug discovery …

Few-shot segmentation (FSS), which aims to determine specific objects in the query image
given only a handful of densely labeled samples, has received extensive academic attention …

Satellite imagery, specifically Landsat, have been widely used for mapping and monitoring
wildfire burned areas. The new Landsat-9 satellite–with higher radiometric resolution …

The powerful deep learning models in remote sensing are inseparable from the support of
massive data. However, the privacy and sensitivity of remote sensing data (RSD) restrict the …

Image fusion plays a crucial role in enhancing the quality and accuracy of semantic
segmentation, which is essential for autonomous driving systems. By merging information …

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant
influence on the global climate over geological timescales. Recent shifts in climate patterns …

The existing methods for burned area segmentation (BAS) in optical remote sensing images
(ORSIs) mainly adopt convolution neural network (CNN) as the backbone, which has limited …

Fire detection is a critical safety issue due to the major and irreversible consequences of fire,
from economic prejudices to loss of life. It is therefore of utmost importance to design …

The area burned in Spain exceeded historical records in 2022, when exceptionally warm
conditions influenced wildfire events. The predicted intensification of wildfire regimes …