In this article, we propose a multimodal co-learning framework for building change detection.
This framework can be adopted to jointly train a Siamese bitemporal image network and a …

In this study, a Semi-Supervised Learning (SSL) method for improved urban change
detection from bi-temporal image pairs is presented. The proposed method employs a Dual …

Two main issues are faced when using very-high-spatial-resolution (VHR) satellite images
for building change detection: 1) the boundaries of detected changes are hard to be …

Building change detection is a primary task in the application of remote sensing images,
especially in city land resource management and urbanization process assesment. Due to …

Building change detection is a very important application in the field of remote sensing.
Recently, deep learning (DL) has been introduced to solve the change detection task and …

Over the past decade, deep learning techniques have significantly advanced the field of
building change detection in remote sensing imagery. However, existing deep learning …

Building change detection (BCD) using high-resolution remote sensing images aims to
identify change areas during different time periods, which is a significant research focus in …

Building change detection (BCD) is a widely used method for monitoring human activities.
Despite advancements in deep learning (DL) in computer vision, recent DL-based BCD …

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

The self-attention mechanism can break the limitation of the receptive field, model in a
global scope, and extract global information efficiently. In this work, we propose a lightweight …