GF-1 WFV satellite images based forest cover mapping in China supported by open land use/cover datasets.

The United Nations sustainable development agenda emphasizes the importance of forests. China's forests cover 5% of the world's forest area, significantly influencing global climate and ecology. In recent decades, China's forests have undergone notable changes.

Robust Building Extraction for High Spatial Resolution Remote Sensing Images with Self-Attention Network.

Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the backbone of the network, which is used to learn the local features (short distance context information around the pixel) of buildings from the image; the other part is the pyramid self-attention module, which is used to obtain the global features (long distance context information with other pixels in the image) and the comprehensive features (includes color, texture, geometric and high-level semantic feature) of the building.

Potentiality of Using Luojia 1-01 Nighttime Light Imagery to Investigate Artificial Light Pollution.

The successful launch of Luojia 1-01 complements the existing nighttime light data with a high spatial resolution of 130 m. This paper is the first study to assess the potential of using Luojia 1-01 nighttime light imagery for investigating artificial light pollution. Eight Luojia 1-01 images were selected to conduct geometric correction.