The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Compared with disturbance maps produced at annual or multi-year time steps, monthly
mapping of forest harvesting can provide more temporal details needed for studying the …

Deriving the extent of areas affected by wildfires is critical to fire management, protection of
the population, damage assessment, and better understanding of the consequences of fires …

Active fire detection in satellite imagery is of critical importance to the management of
environmental conservation policies, supporting decision-making and law enforcement. This …

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems,
and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for …

The heat release rate (HRR) is the most critical parameter in characterizing the fire behavior
and thermal effects of a burning item. However, traditional fire calorimetry methods are not …

Wildfires have major ecological, social and economic consequences. Information about the
extent of burned areas is essential to assess these consequences and can be derived from …

Wildfires are increasing in intensity and frequency across the globe due to climate change
and rising global temperature. Development of novel approach to Monitor wildfire …

In this paper, we present an in-depth analysis of the use of convolutional neural networks
(CNN), a deep learning method widely applied in remote sensing-based studies in recent …