Automatic object detection by satellite remote sensing images is of great significance for
resource exploration and natural disaster assessment. To solve existing problems in remote …

Rapid identification of crop lodging has implications for optimizing disaster prevention and
mitigation measures. This study proposes a sequentially integrated algorithm for crop …

Forest fires have become a major concern in the northern parts of Morocco, particularly in
the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the …

The synergistic use of remote sensing and unsupervised machine learning has emerged as
a potential tool for addressing a variety of environmental monitoring applications, such as …

The occurrence of forest fires has increased significantly in recent years across the planet.
Events of this nature have resulted in the leveraging of new automated methodologies to …

Forest fires, as severe natural disasters, pose significant threats to ecosystems and human
societies, and their spread is characterized by constant evolution over time and space. This …

The integrated use of remote sensing and machine learning stands out as a powerful and
well-established approach for dealing with various environmental monitoring tasks …

We consider the paradigm of unsupervised anomaly detection, which involves the
identification of anomalies within a dataset in the absence of labeled examples. Though …

The Encontro das Águas State Park (EASP), renowned as the world's largest refuge for
Jaguars (Panthera onca), is located within the Brazilian portion of the Pantanal biome, and it …

Climate change is a critical concern that has been greatly affected by human activities,
resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both …