Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …

Given the large volume of remote sensing images collected daily, automatic object detection
and segmentation have been a consistent need in Earth observation (EO). However, objects …

As an important carrier of human productive activities, the extraction of buildings is not only
essential for urban dynamic monitoring but also necessary for suburban construction …

During the past decade, deep learning is one of the essential breakthroughs made in
artificial intelligence. In particular, it has achieved great success in image processing …

While most state-of-the-art instance segmentation methods produce binary segmentation
masks, geographic and cartographic applications typically require precise vector polygons …

This study proposes an automatic building footprint extraction framework that consists of a
convolutional neural network (CNN)-based segmentation and an empirical polygon …

In precision livestock farming, computer vision based approaches have been widely used to
obtain individual cattle health and welfare information such as body condition score, live …

Deep learning based methods have significantly boosted the study of automatic building
extraction from remote sensing images. However, delineating vectorized and regular …

Image processing via convolutional neural network (CNN) has been developed rapidly for
remote sensing technology. Moreover, techniques for accurately extracting building …

Accurate large-scale building detection is significant in monitoring urban development, map
updating, change detection, and digital city establishment. However, due to the complicated …