Remote sensing imagery allows temporal and large-scale observation of the Earth, and
advanced techniques such as deep learning have been developed to deal with the massive …

Semantic change detection (SCD) refers to the task of simultaneously extracting changed
areas and their semantic categories (before and after the changes) in remote sensing …

Change detection is one of the main problems in remote sensing, and is essential to the
accurate processing and understanding of the large scale Earth observation data available …

With the advent of very-high-resolution remote sensing images, semantic change detection
(SCD) based on deep learning has become a research hotspot in recent years. SCD aims to …

With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor
Earth surface changes at fine scale and in great detail. Thus, semantic change detection …

Multi-temporal high spatial resolution earth observation makes it possible to detect complex
urban land surface changes, which is a significant and challenging task in remote sensing …

In recent years, change detection has been one of the hot research topics within the field of
remote sensing. Previous studies have concentrated on binary change detection (BCD), but …

Change detection is a very important technique for remote sensing data analysis. Its
mainstream solutions are either supervised or unsupervised. In supervised methods, most of …

Given a pair of bitemporal very high resolution (VHR) remote sensing images, the semantic
change detection task aims to locate land surface changes and identify their semantic …

In the field of remote sensing applications, semantic change detection (SCD) simultaneously
identifies changed areas and their change types by jointly conducting bitemporal image …