Semantic segmentation is a fundamental but challenging problem of pixel-level remote
sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite …

Among the most difficult difficulties in contemporary satellite image-processing subjects is
cloud and cloud shade segmentation. Due to substantial background noise interference …

Unmanned aerial vehicles (UAVs) are widely used for small target detection of forest fires
due to its low-risk rate, low cost and high ground coverage. However, the detection accuracy …

The semantic segmentation of land use and land cover (LULC) is a crucial and widely
employed remote sensing task. Conventional convolutional neural networks and vision …

High spatial resolution remote sensing images (HRRSIs) contain intricate details and varied
spectral distributions, making their semantic segmentation a challenging task. To address …

Concerning the ever-changing wetland environment, the efficient extraction of wetland
information holds great significance for the research and management of wetland …

Automatically extracting water bodies is a significant task in interpreting remote sensing
images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance …

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

Semantic segmentation of remote sensing images (RSIs) is vital for numerous geospatial
applications, including land-use mapping, urban planning, and environmental monitoring …

In the field of remote sensing, semantic segmentation of unmanned aerial vehicle (UAV)
imagery is crucial for tasks such as land resource management, urban planning, precision …