As one of the most significant components of the ecosystem, waterbody needs to be highly
monitored at different spatial and temporal scales. Nevertheless, waterbody variations in …

Water body extraction is a typical task in the semantic segmentation of remote sensing
images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional …

As a fundamental and extensively studied task in computer vision, image segmentation aims
to locate and identify different semantic concepts at the pixel level. Recently, inspired by In …

Lake extraction from remote sensing images is challenging due to the complex lake shapes
and inherent data noises. Existing methods suffer from blurred segmentation boundaries …

Surface water plays a pivotal role in the context of climate change, human activities, and
ecosystems, underscoring the significance of precise monitoring and observation of surface …

Large-scale information pertaining to surface water bodies is crucial for activities such as
flood monitoring. Deep learning algorithms have shown great potential in water-body …

The automatic lake water extraction method based on semantic segmentation is a research
hotspot in the field of remote sensing image processing. In remote sensing images, the …

Extracting water information from remote-sensing images is of great research significance
for applications such as water resource protection and flood monitoring. Current water …

As a fundamental and extensively studied task in computer vision, image segmentation aims
to locate and identify different semantic concepts at the pixel level. Recently, inspired by In …

Lake extraction from remote sensing imagery is a complex challenge due to the varied lake
shapes and data noise. Current methods rely on multispectral image datasets, making it …