Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Automatic land use/land cover (LULC) classification from very high resolution (VHR) remote
sensing images can provide us with rapid, large-scale, and fine-grained understanding of …

Landscape perspectives in riverine ecology have been undertaken increasingly in the last
30 years, leading aquatic ecologists to develop a diverse set of approaches for …

Convolutional neural network (CNN) comprises the most common and extensively used
network in the field of deep learning (DL). The design of CNNs was influenced by neurons …

This paper reviews the current GeoAI and machine learning applications in hydrological and
hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial …

The accurate classification of forest types is critical for sustainable forest management. In
this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was …

Rapid and accurate detection of maritime military targets is of great significance for
maintaining national defense security. Few studies have used high-resolution optical …

The ecological value of tropical forests in water conservation district has been of great
interest because of their rich vegetation types and higher biomass density than any other …

The identification of landscape classes facilitates the implementation of planning strategies.
Although landscape patterns are key distinctive features of landscape classes, existing …