Detecting seismic events in real-time for prompt alerts and responses is a challenging task that requires accurately capturing P-wave arrivals. This task becomes even more challenging in regions li…
The application of machine learning (ML) techniques to climate science has received significant attention, particularly in the field of climate predictions, ranging from sub-seasonal to decadal tim…
The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately…
Change detection (CD) is a meaningful and challenging task for remote sensing (RS) image analysis. Deep learning (DL) based methods have shown great potential in change detection tasks, there are s…
Digital geological mapping has experienced significant growth over the past three decades due to the advent of commercial geographical information systems, advances in global positioning systems, a…
Land subsidence is a worldwide threat that may cause irreversible damage to the environment and the infrastructures. Thus, identifying and mapping areas prone to land subsidence with accurate metho…
In geosciences, kriging is leading spatial interpolation, and co-kriging is the most commonly used method for accomplishing spatial interpolation of a target variable by incorporating information f…
Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected…
Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill…
Carbon-cycle modelling is essential for testing the main carbon sources and sinks as climate forcings, and we introduce and describe GEOCARB_NET, a graphical user interface for the geologic carbon …