Building extraction from very high-resolution remote-sensing images still faces two main
issues:(1) small buildings are severely omitted and the extracted building shapes have a low …

Semantic segmentation forms the foundation for understanding very high resolution (VHR)
remote sensing images, with extensive demand and practical application value. The …

When humans delineate objects of interest from high-resolution remote sensing (RS)
images, they first determine the existence and locations of these objects. Then, they identify …

As urbanization accelerates, the evolving dynamics of village growth and decline have
garnered widespread attention. Rural housing, as the most significant asset in villages …

Semantic segmentation is one of the most important tasks in the field of remote sensing. As
the spatial resolution increases, the remote sensing images can capture more detailed …

Cultivated land is crucial for food production and security. In complex environments like
mountainous regions, the fragmented nature of the cultivated land complicates rapid and …

Heterogeneous high-resolution remote sensing image matching will be disturbed by the
differences in sensor type, imaging angle, height, and imaging time, and the matching …

This paper addresses challenges in building extraction from remote sensing imagery,
including ambiguous edge definition, limited shadow recognition, and heavy reliance on …

Remote sensing images are crucial for acquiring ground information, with buildings being
significant ground targets. This paper addresses the limitations of traditional methods in …

The High-Spatial-Resolution (HSR) remote sensing is categorized through its ability to
generate fine-grained spatial data. The image resolution is typically calculated through …