Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive
performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial …

The quantity and quality of cropland are the key to ensuring the sustainable development of
national agriculture. Remote sensing technology can accurately and timely detect the …

In recent times, deep neural networks have drawn much attention in ground-based cloud
recognition. Yet such kind of approaches simply center upon learning global features from …

As an inevitable phenomenon in most optical remote-sensing images, the effect of shadows
is prominent in urban scenes. Shadow detection is critical for exploiting shadows and …

The aircraft system has recently gained its reputation as a reliable and efficient tool for
sensing and parsing aerial scenes. However, accurate and fast semantic segmentation of …

Quickly and accurately extracting buildings from remote sensing images is essential for
urban planning, change detection, and disaster management applications. In particular …

Objective Due to the small proportion of target pixels in computed tomography (CT) images
and the high similarity with the environment, convolutional neural network-based semantic …

Scene parsing of high-resolution remote-sensing images (HRRSIs) refers to parsing
different semantic regions from the images, which is an important fundamental task in image …

Precision agriculture-based orchard management relies heavily on the accurate delineation
of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote …

Utilizing magnetic anomaly data for effective edge detection of source bodies can provide
crucial evidence for the delineation of geological units and the division of fault structures …