13 Results Found

Remote sensing experts have been actively using deep neural networks to solve extraction tasks in high-resolution aerial imagery by means of supervised semantic segmentation operations. However, the extraction operation is imperfect, due to the compl...

Most existing road extraction approaches apply learning models based on semantic segmentation networks and consider reduced study areas, featuring favorable scenarios. In this work, an end-to-end processing strategy to extract the road surface areas...

Unmanned Surface Vehicles (USVs) play a critical role in maritime monitoring, environmental protection, and emergency response, necessitating accurate scene understanding in complex aquatic environments. Conventional semantic segmentation methods oft...

Fast and accurate detection of ship objects in remote sensing images must overcome two critical problems: the complex content of remote sensing images and the large number of small objects reduce ship detection efficiency. In addition, most existing...

Pack-ice seals are key indicator species in the Southern Ocean. Their large size (2–4 m) and continent-wide distribution make them ideal candidates for monitoring programs via very-high-resolution satellite imagery. The sheer volume of imagery...

There is a growing interest in scene text detection for arbitrary shapes. The effectiveness of text detection has also evolved from horizontal text detection to the ability to perform text detection in multiple directions and arbitrary shapes. Howeve...

Ensuring railway safety requires reliable monitoring of trains in critical safety areas, such as station throat zones and railway crossings. Compared with cameras, roadside LiDAR can more reliably capture the geometry of trains under low-light, high-...

Semantic segmentation on Landsat-8 data is crucial in the integration of diverse data, allowing researchers to achieve more productivity and lower expenses. This research aimed to improve the versatile backbone for dense prediction without convolutio...

In recent decades, technological developments in archaeological geophysics have led to growing data volumes, so that an important bottleneck is now at the stage of data interpretation. The manual delineation and classification of anomalies are time-c...

Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and a...

This paper presents an end-to-end deep convolutional neural network (CNN) model for carrier signal detection in the broadband power spectrum, so-called spectrum center net (SCN). By regarding the broadband power spectrum sequence as a one-dimensional...

For most cities, municipal governments have constructed basic building footprint datasets that need to be updated regularly for the management and monitoring of urban development and ecology. Cities are capable of changing in a short period of time,...

Building extraction from remotely sensed imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Several published contributions dedicated to the ap...