Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …

Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Monitoring global biodiversity from space through remotely sensing geospatial patterns has
high potential to add to our knowledge acquired by field observation. Although a framework …

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

Riverside landslides present a significant geohazard globally, posing threats to
infrastructure and human lives. In line with the United Nations' Sustainable Development …

Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images
from only RGB images, which can effectively overcome the high acquisition cost and low …

This paper provides a comprehensive state-of-the-art investigation of the recent advances in
data science in emerging economic applications. The analysis is performed on the novel …