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 …

Unmanned aerial vehicles (UAVs) mounted with remote sensors have been widely used in
architectural, civil, and environmental engineering fields. In particular, UAVs are applied for …

When training or evaluating deep learning models, two essential parts are picking the
proper loss function and deciding on performance metrics. In this paper, we provide a …

To restrict the entry of polluting components into water bodies, particularly rivers, it is critical
to undertake timely monitoring and make rapid choices. Traditional techniques of assessing …

Deep learning has achieved remarkably positive results and impacts on medical diagnostics
in recent years. Due to its use in several proposals, deep learning has reached sufficient …

Regular water quality monitoring is becoming desirable due to the increase in water
pollution caused by both climate change and the generation of industrial chemicals …

The quantity and quality of surface water are inherently connected yet are overwhelmingly
studied separately in the field of remote sensing. Remotely observable water quantity (eg …

River water quality is of utmost importance because the river is not only one of the key water
resources but also a natural habitat serving its surrounding environment. In a bid to address …

Water quality monitoring of medium-sized inland water is important for water environment
protection given the large number of small-to-medium size water bodies in China. A case …

Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for
effective management and early warning of marine ecological systems. However, previous …