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 …
Mapping for autonomous driving: Opportunities and challenges
This article provides a review of the production and uses of maps for autonomous driving
and a synthesis of the opportunities and challenges. For many years, maps have helped …
and a synthesis of the opportunities and challenges. For many years, maps have helped …
Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network
The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable
tool for the epileptic seizure detection. Recent deep learning models fail to fully consider …
tool for the epileptic seizure detection. Recent deep learning models fail to fully consider …
Automated pavement crack damage detection using deep multiscale convolutional features
W Song, G Jia, H Zhu, D Jia… - Journal of Advanced …, 2020 - Wiley Online Library
Road pavement cracks automated detection is one of the key factors to evaluate the road
distress quality, and it is a difficult issue for the construction of intelligent maintenance …
distress quality, and it is a difficult issue for the construction of intelligent maintenance …
Building extraction of aerial images by a global and multi-scale encoder-decoder network
Semantic segmentation is an important and challenging task in the aerial image community
since it can extract the target level information for understanding the aerial image. As a …
since it can extract the target level information for understanding the aerial image. As a …
Random noise attenuation based on residual convolutional neural network in seismic datasets
Seismic random noise attenuation is a key step in seismic data processing. The random
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …
Adaptive effective receptive field convolution for semantic segmentation of VHR remote sensing images
Convolutional neural networks (CNNs) have facilitated impressive improvements in the
semantic segmentation of very high-resolution (VHR) remote sensing images. The success …
semantic segmentation of very high-resolution (VHR) remote sensing images. The success …
Intensity thresholding and deep learning based lane marking extraction and lane width estimation from mobile light detection and ranging (LiDAR) point clouds
Lane markings are one of the essential elements of road information, which is useful for a
wide range of transportation applications. Several studies have been conducted to extract …
wide range of transportation applications. Several studies have been conducted to extract …
Lane detection: A survey with new results
Lane detection is essential for many aspects of autonomous driving, such as lane-based
navigation and high-definition (HD) map modeling. Although lane detection is challenging …
navigation and high-definition (HD) map modeling. Although lane detection is challenging …
An optimized deep neural network detecting small and narrow rectangular objects in Google Earth images
Object detection is an important task for rapidly localizing target objects using high-
resolution satellite imagery (HRSI). Although deep learning has been shown an efficient …
resolution satellite imagery (HRSI). Although deep learning has been shown an efficient …