The annotations used during the training process are crucial for the inference results of
remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be …

With many platforms and sensors continuously observing the earth surface, the large
amount of remote sensing data presents a big data challenge. While remote sensing data …

Semantic segmentation methods based on deep neural networks have achieved great
success in recent years. However, training such deep neural networks relies heavily on a …

Green plastic cover (GPC) is a kind of green plastic fine mesh primarily used for covering
construction sites and mitigating large amounts of dust during construction. Accurate GPC …

Open-source land cover products (LCPs) are essential for many areas of scientific research.
However, they have deficiencies such as low accuracy, low resolution, and poor timeliness …

Accurate change detection of built-up areas (BAs) fosters a comprehensive understanding of
urban development. The post-classification comparison (PCC) is a widely-used change …

Land-use/land-cover map** in coastal areas is a foundational yet significant pixel-wise
classification work. Fully supervised semantic segmentation models have recently achieved …

Recent years have witnessed the superiority of deep learning-based algorithms in the field
of HSI classification. However, a prerequisite for the favorable performance of these …

Semantic segmentation for remote sensing images (RSI) is critical for the Earth monitoring
system. However, the covariate shift between RSI datasets under different capture …

In this article, we propose to build up a collaboration between a deep neural network and a
human in the loop to swiftly obtain accurate segmentation maps of remote sensing images …