Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Current state-of-the-art two-stage detectors generate oriented proposals through time-
consuming schemes. This diminishes the detectors' speed, thereby becoming the …

Recently, object detection in aerial images has gained much attention in computer vision.
Different from objects in natural images, aerial objects are often distributed with arbitrary …

Deep learning-based algorithms have seen a massive popularity in different areas of remote
sensing image analysis over the past decade. Recently, transformer-based architectures …

Oriented object detection is a practical and challenging task in remote sensing image
interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to …

Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

The past decade has witnessed significant progress on detecting objects in aerial images
that are often distributed with large-scale variations and arbitrary orientations. However …