K
Konstantinos Karantzalos
Researcher at National Technical University of Athens
Publications - 110
Citations - 2772
Konstantinos Karantzalos is an academic researcher from National Technical University of Athens. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 22, co-authored 92 publications receiving 1929 citations. Previous affiliations of Konstantinos Karantzalos include École des ponts ParisTech & National Technical University.
Papers
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Proceedings ArticleDOI
Deep supervised learning for hyperspectral data classification through convolutional neural networks
TL;DR: This work proposes a deep learning based classification method that hierarchically constructs high-level features in an automated way and exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task.
Proceedings ArticleDOI
Building detection in very high resolution multispectral data with deep learning features
TL;DR: An automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks based on a supervised classification procedure employing a very large training dataset is proposed.
Journal ArticleDOI
Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection
TL;DR: A novel recognition-driven variational framework that estimates the number of buildings as well as their pose from the observed data and can address multiple building extraction from a single optical image, a highly demanding task of fundamental importance in various geoscience and remote-sensing applications.
Proceedings ArticleDOI
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
Maria Papadomanolaki,Sagar Verma,Maria Vakalopoulou,Siddharth Gupta,Konstantinos Karantzalos +4 more
TL;DR: A novel deep learning framework for urban change detection which combines state-of-the-art fully convolutional networks (similar to U-Net) for feature representation and powerful recurrent networks (such as LSTMs) for temporal modeling is presented.
Proceedings ArticleDOI
Vehicle detection and traffic density monitoring from very high resolution satellite video data
TL;DR: The quite promising results indicate the potentials of the proposed approach, while parallel GPU implementations can allow for real-time performance.