Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …

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

The rapid development in deep learning and computer vision has introduced new
opportunities and paradigms for building extraction from remote sensing images. In this …

Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban
development. Earth Observation (EO) is a valuable data source to cover this demand. In …

Although efforts and progress have been made in crop classification using optical remote
sensing images, it is still necessary to make full use of the high spatial, temporal, and …

Nowadays, different machine learning approaches, either conventional or more advanced,
use input from different remote sensing imagery for land cover classification and associated …

In this paper we propose multi-resolution data fusion methods for deep learning-based high-
resolution land cover map** from aerial imagery. The land cover map** problem, at …

In recent years, multi-classifier learning is of significant interest in industrial and economic
fields. Moreover, neural network is a popular approach in multi-classifier learning. However …

In this study, the effectiveness of several convolutional neural network architectures
(AlbuNet-34/FCN/DeepLabV3+/U-Net/U-Net++) for water and flood map** using Sentinel …

Classification of very high resolution imagery (VHRI) is challenging due to the difficulty in
mining complex spatial and spectral patterns from rich image details. Various object-based …