Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …

This study evaluates the performance of convolutional neural networks for semantic
segmentation of water bodies in very high-resolution satellite and aerial images from …

Deep learning-based segmentation of very high-resolution (VHR) satellite images is a
significant task providing valuable information for various geospatial applications …

Accurate vehicle classification and tracking are increasingly important subjects for intelligent
transport systems (ITSs) and for planning that utilizes precise location intelligence. Deep …

Transformer has been widely applied in image processing tasks as a substitute for
convolutional neural networks (CNNs) for feature extraction due to its superiority in global …

Currently, there is a growing population around the world, and this is particularly true in
developing countries, where food security is becoming a major problem. Therefore …

Blue carbon ecosystems (mangroves, seagrasses and saltmarshes) are highly productive
coastal habitats, and are considered some of the most carbon-dense ecosystems on the …

Deep learning has achieved remarkably positive results and impacts on medical diagnostics
in recent years. Due to its use in several proposals, deep learning has reached sufficient …

Forest fire is one of the foremost environmental disasters that threatens the Australian
community. Recognition of the occurrence patterns of fires and the identification of fire risk is …