Convolutional neural network (CNN) comprises the most common and extensively used
network in the field of deep learning (DL). The design of CNNs was influenced by neurons …

Recent advancements in deep learning have spurred the development of numerous novel
semantic segmentation models for land cover mapping, showcasing exceptional …

Today, multi-sensor fusion detection frameworks in autonomous driving, especially
sequence-based data-level fusion frameworks, face high latency and coupling issues and …

Urban road extraction is important for the applications of urban planning and transportation.
High-resolution image (HRI) has been one of the most popular data sources for extracting …

In recent years, the abundance of information in 3D data has made the semantic
segmentation of 3D point clouds a topic of great interest. However, current methods often …

It is very significant for rural planning to accurately count the number and area of rural
homesteads by means of automation. The development of deep learning makes it possible …

Automatic building instance extraction from high-resolution (HR) remote sensing imagery
(RSI) is crucial for urban planning and mapping. The dominant approaches are based on …

Accurate extraction of urban features such as buildings and roads lays the foundation for the
current trends of digital twins of urban systems to support planning, monitoring, navigation …

As an emerging technology that uses a pill‐sized camera to visualize images of the
digestive tract. Wireless capsule endoscopy (WCE) presents several advantages, since it is …

Building extraction from remote sensing images is a hot topic in the fields of computer vision
and remote sensing. In recent years, driven by deep learning, the accuracy of building …