Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …

Remote sensing images pose distinct challenges for downstream tasks due to their inherent
complexity. While a considerable amount of research has been dedicated to remote sensing …

The spatial resolution of remote sensing images is becoming increasingly higher, posing
challenges in handling large very-high-resolution (VHR) remote sensing images for dense …

Recently, deep learning (DL) models have become the main focus for the remote sensing
change detection tasks. Numerous publications on supervised and unsupervised DL-based …

Vision foundation models (VFMs), such as the segment anything model (SAM), allow zero-
shot or interactive segmentation of visual contents; thus, they are quickly applied in a variety …

Change detection (CD) is a critical task to observe and analyze dynamic processes of land
cover. Although numerous deep-learning (DL)-based CD models have performed …

Convolutional neural networks (CNN) and Transformers have made impressive progress in
the field of remote sensing change detection (CD). However, both architectures have their …

With the rapid advancement of remote sensing technology, bitemporal remote sensing
change detection (CD) techniques have also seen significant progress. However, existing …

Monitoring land changes triggered by mining activities is crucial for industrial control,
environmental management, and regulatory compliance, yet it poses significant challenges …

In land cover change detection tasks, extracting universal features of changing targets is
crucial for achieving precise detection results. A larger receptive field helps the model …