In remotely sensed images, high intraclass variance and interclass similarity are ubiquitous
due to complex scenes and objects with multivariate features, making semantic …

Easy and efficient acquisition of high-resolution remote sensing images is of importance in
geographic information systems. Previously, deep neural networks composed of …

In this study, we introduce an improved semisupervised deep learning approach, and
demonstrate its suitability for modeling the relationship between forest structural parameters …

Land-cover classification especially global map** has become a new trend in recent
years. Traditional convolutional neural network (CNN) methods for land-cover classification …

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

The segmentation of gastrointestinal (GI) organs is crucial in radiation therapy for treating GI
cancer. It allows for develo** a targeted radiation therapy plan while minimizing radiation …

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth
observation (EO) images. However, in practical forest inventories, reference datasets are …

Land cover classification is one of the most fundamental tasks in the field of remote sensing.
In recent years, fully supervised fully convolutional network (FCN)-based semantic …

Dynamic technological progress has contributed to the development of systems imaging of
the Earth's surface as well as data mining methods. One such example is super-resolution …

Floods occur throughout the world and are becoming increasingly frequent and dangerous.
This is due to different factors, among which climate change and land use stand out. In …