This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable
for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer …

Advances in deep learning and computer vision techniques have made impacts in the field
of remote sensing, enabling efficient data analysis for applications such as land cover …

Forests play a crucial role in Earth's system processes and provide a suite of social and
economic ecosystem services, but are significantly impacted by human activities, leading to …

Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing
various image interpretation tasks. Pretraining is an active research topic, encompassing …

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 …

Accurate urban green space (UGS) measurement has become crucial for landscape
analysis. This paper reviews the recent technological breakthroughs in deep learning (DL) …

With the flourishing of remote sensing (RS) platform techniques, very high-resolution (VHR)
images have become more and more popular in recent years, which benefit the task of …

Recently, remote sensing images have been widely used in many scenarios, gradually
becoming the focus of social attention. Nevertheless, the limited annotation of scarce …

Existing deep models have greatly enhanced the performance of semantic segmentation in
remote sensing (RS) images, but they are often limited by the scarcity of labeled samples …

Recently, deep learning based methods have revolutionized remote sensing image
segmentation. However, these methods usually rely on a pre-defined semantic class set …