With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for
revolutionizing data analysis and applications in many domains of Earth sciences. This …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

The 6G vision of creating authentic digital twin representations of the physical world calls for
new sensing solutions to compose multi-layered maps of our environments. Radio sensing …

As remote sensing (RS) data obtained from different sensors become available largely and
openly, multimodal data processing and analysis techniques have been garnering …

The construction industry is currently going through an intelligent revolution. The profound
transformation of the Industry 4.0 era is made possible by contemporary technologies such …

Ripe with possibilities offered by deep-learning techniques and useful in applications
related to remote sensing, computer vision, and robotics, 3D point cloud semantic …

Deep convolution neural network (DCNN) is among the most effective ways of performing
land use classification of high-resolution remote sensing images. Land use classification by …

Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN)
data aimed at generating an outcome with the same spatial resolution of the PAN data and …

The discipline of land change science has been evolving rapidly in the past decades.
Remote sensing played a major role in one of the essential components of land change …