Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …

Landslide susceptibility map (LSM) plays an important role in providing the knowledge of
slopes prone to future landslides. However, the applicability of LSM is often hindered due to …

The significant increase in the amount of satellite data in recent years along with the
increase in computing resources has opened up new possibilities for Earth Observation …

Rockfalls, a common geohazard in mountainous areas, have destructive impact capacity
and may cause failure of dangerous rock masses in their runout range. For slope risk …

Rapid detection of damages occurring as a result of natural disasters is vital for emergency
response. In recent years, remote sensing techniques have been commonly used for the …

Transferability of knowledge from well-investigated areas to a new study region is gaining
importance in landslide hazard research. Considering the time-consuming compilation of …

Rapid and accurate extraction of landslide areas triggered by earthquakes has far-reaching
significance for geological disaster risk assessment and emergency rescue. At present …

Hitherto, image-level classification on remote sensing landslide images has been paid
attention to, but the accuracy of traditional deep learning-based methods still have room for …

Landslides are recognized as high-impact natural hazards in different regions around the
world; therefore, they are extensively researched by experts. Landslide inventories are …

Wildfires are one of the most commonly occurring natural disasters in the world, posing
significant threats to ecosystems and human settlements alike. One of the most important …