Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis
Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …
learning methods have shifted the paradigm of image classification from pixel-based and …
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 …
Advanced multi-sensor optical remote sensing for urban land use and land cover classification: Outcome of the 2018 IEEE GRSS data fusion contest
This paper presents the scientific outcomes of the 2018 Data Fusion Contest organized by
the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and …
the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and …
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 …
Infrastructure requirement for autonomous vehicle integration for future urban and suburban roads–Current practice and a case study of Melbourne, Australia
Autonomous vehicle technology and its enabled mobility services are evolving at a more
rapid pace than the understanding of the infrastructure required for them to be efficiently and …
rapid pace than the understanding of the infrastructure required for them to be efficiently and …
Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction
Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
A review of high-definition map creation methods for autonomous driving
Autonomous driving has been among the most popular and challenging topics in the past
few years. Among all modules in autonomous driving, High-definition (HD) map has drawn …
few years. Among all modules in autonomous driving, High-definition (HD) map has drawn …
Fastdraw: Addressing the long tail of lane detection by adapting a sequential prediction network
J Philion - Proceedings of the IEEE/CVF conference on …, 2019 - openaccess.thecvf.com
The search for predictive models that generalize to the long tail of sensor inputs is the
central difficulty when developing data-driven models for autonomous vehicles. In this …
central difficulty when developing data-driven models for autonomous vehicles. In this …
Vehicle detection from aerial images using deep learning: A comparative study
This paper addresses the problem of car detection from aerial images using Convolutional
Neural Networks (CNNs). This problem presents additional challenges as compared to car …
Neural Networks (CNNs). This problem presents additional challenges as compared to car …
[HTML][HTML] Applications, databases and open computer vision research from drone videos and images: a survey
Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an
emerging application attracting significant attention from researchers in various areas of …
emerging application attracting significant attention from researchers in various areas of …