The extraction of building footprints, as a highly challenging task in remote sensing (RS)
image-based geospatial object detection and recognition, holds significant importance. Due …

Deep learning plays an important role in crack segmentation, but most work utilize off-the-
shelf or improved models that have not been specifically developed for this task. High …

Technological progress and the rapid increase in geochemical data often create bottlenecks
in many studies, because current methods are designed using limited number of data and …

This paper describes a pixel-wise detection algorithm to reconstruct rock cracks using CT
images. Through reviewing the shortcomings of previous studies, a pixel-level labeled …

With the goal of automatic sea ice mapping during the summer sea ice melt cycle, this study
involved designing a fully automatic sea ice segmentation method based on a deep learning …

With the development of deep learning, Remote Sensing Image (RSI) semantic
segmentation has produced significant advances. However, due to the sparse distribution of …

Iris recognition models that can be deployed on mobile devices have further requirements
for both model scale and accuracy. We note that iris segmentation and localization tasks are …

Abstract Infrared Small Target Detection (IRSTD) aims to detect small and dim targets in
complex backgrounds. However, the low signal-to-noise ratio and reduced contrast in the …

Multi-spectral information is crucial for remote sensing crop monitoring, but current methods
struggle with inadequate feature extraction, leading to poor generalization and incomplete …

Face recognition has been one of the most studied researches in computer vision, and facial
feature extraction, is one of the cores of face recognition. In this paper, we focus on semantic …