Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends
Deep learning (DL) has great influence on large parts of science and increasingly
established itself as an adaptive method for new challenges in the field of Earth observation …
established itself as an adaptive method for new challenges in the field of Earth observation …
Remote sensing object detection in the deep learning era—a review
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 …
and segmentation have been a consistent need in Earth observation (EO). However, objects …
Objects segmentation from high-resolution aerial images using U-Net with pyramid pooling layers
Extracting manufactured features such as buildings, roads, and water from aerial images is
critical for urban planning, traffic management, and industrial development. Recently …
critical for urban planning, traffic management, and industrial development. Recently …
Comparison of fully convolutional networks (FCN) and U-Net for road segmentation from high resolution imageries
Segmentation is one of the most popular classification techniques which still have semantic
labels. In this context, the segmentation of different objects such as cars, airplanes, ships …
labels. In this context, the segmentation of different objects such as cars, airplanes, ships …
Deep learning in object recognition, detection, and segmentation
X Wang - Foundations and Trends® in Signal Processing, 2016 - nowpublishers.com
As a major breakthrough in artificial intelligence, deep learning has achieved very
impressive success in solving grand challenges in many fields including speech recognition …
impressive success in solving grand challenges in many fields including speech recognition …
A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images
Y Li, L Xu, J Rao, L Guo, Z Yan, S Jin - Remote sensing letters, 2019 - Taylor & Francis
Road segmentation from high-resolution visible remote sensing images provides an
effective way for automatic road network forming. Recently, deep learning methods based …
effective way for automatic road network forming. Recently, deep learning methods based …
Segment-before-detect: Vehicle detection and classification through semantic segmentation of aerial images
Like computer vision before, remote sensing has been radically changed by the introduction
of deep learning and, more notably, Convolution Neural Networks. Land cover classification …
of deep learning and, more notably, Convolution Neural Networks. Land cover classification …
Road segmentation using u-net architecture
NYQ Abderrahim, S Abderrahim… - 2020 IEEE international …, 2020 - ieeexplore.ieee.org
The detection of objects has become a critical step to update ground cover information, and
the availability of very high-resolution satellite images made us discover new classification …
the availability of very high-resolution satellite images made us discover new classification …
[HTML][HTML] Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields
Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and
satellite (or high-resolution, HR) images, has been applied to many application domains …
satellite (or high-resolution, HR) images, has been applied to many application domains …