[HTML][HTML] 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 …
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 …
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 …
[HTML][HTML] 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 …
Commonroad scenario designer: An open-source toolbox for map conversion and scenario creation for autonomous vehicles
S Maierhofer, M Klischat… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Maps are essential for testing autonomous driving functions. Several map and scenario
formats are available. However, they are usually not compatible with each other, limiting …
formats are available. However, they are usually not compatible with each other, limiting …
[HTML][HTML] Hybridizing cross-level contextual and attentive representations for remote sensing imagery semantic segmentation
Semantic segmentation of remote sensing imagery is a fundamental task in intelligent
interpretation. Since deep convolutional neural networks (DCNNs) performed considerable …
interpretation. Since deep convolutional neural networks (DCNNs) performed considerable …
[HTML][HTML] Automatic road marking extraction and vectorization from vehicle-borne laser scanning data
L Yao, C Qin, Q Chen, H Wu - Remote Sensing, 2021 - mdpi.com
Automatic driving technology is becoming one of the main areas of development for future
intelligent transportation systems. The high-precision map, which is an important …
intelligent transportation systems. The high-precision map, which is an important …
Aerial-PASS: Panoramic annular scene segmentation in drone videos
Aerial pixel-wise scene perception of the surrounding environment is an important task for
UAVs (Unmanned Aerial Vehicles). Previous research works mainly adopt conventional …
UAVs (Unmanned Aerial Vehicles). Previous research works mainly adopt conventional …
Strengthen the feature distinguishability of geo-object details in the semantic segmentation of high-resolution remote sensing images
J Chen, H Wang, Y Guo, G Sun… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Semantic segmentation is one of the hot topics in the field of remote sensing image
intelligent analysis. Deep convolutional neural network (DCNN) has become a mainstream …
intelligent analysis. Deep convolutional neural network (DCNN) has become a mainstream …