Building extraction from remote sensing (RS) images is a fundamental task for geospatial
applications, aiming to obtain morphology, location, and other information about buildings …

Deep learning technology has been extensively explored in pattern recognition and image
processing areas. A multi-mode medical image fusion with deep learning will be proposed …

Building footprint extraction is a basic task in the fields of mapping, image understanding,
computer vision, and so on. Accurately and efficiently extracting building footprints from a …

Deep learning methods have achieved considerable progress in remote sensing image
building extraction. Most building extraction methods are based on Convolutional Neural …

Automatic building extraction from high-resolution remote sensing imagery has various
applications, such as urban planning and land use management. However, the existing …

Abstract Convolutional Neural Network (CNN) has been increasingly used for land cover
mapping of remotely sensed imagery. However, large-area classification using traditional …

Synthetic aperture radar (SAR) images provide all-weather and all-time capabilities for Earth
observation, which becomes highly beneficial in the field of intelligent remote sensing (RS) …

The identification and counting of plant individuals is essential for environmental monitoring.
UAV based imagery offer ultra-fine spatial resolution and flexibility in data acquisition, and …

Building roofs are crucial elements that must be extracted from satellite images for use in
services like updating geodatabases, risk analysis and rescue maps. The diversity and …

Building footprint segmentation from high-resolution remote sensing (RS) images plays a
vital role in urban planning, disaster response, and population density estimation …