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pro vyhledávání: '"Liebel, Lukas"'
Autor:
Qiu, Chunping, Liebel, Lukas, Hughes, Lloyd H., Schmitt, Michael, Körner, Marco, Zhu, Xiao Xiang
Human Settlement Extent (HSE) and Local Climate Zone (LCZ) maps are both essential sources, e.g., for sustainable urban development and Urban Heat Island (UHI) studies. Remote sensing (RS)- and deep learning (DL)-based classification approaches play
Externí odkaz:
http://arxiv.org/abs/2011.11452
City models and height maps of urban areas serve as a valuable data source for numerous applications, such as disaster management or city planning. While this information is not globally available, it can be substituted by digital surface models (DSM
Externí odkaz:
http://arxiv.org/abs/2004.02493
Fully automatic large-scale land cover mapping belongs to the core challenges addressed by the remote sensing community. Usually, the basis of this task is formed by (supervised) machine learning models. However, in spite of recent growth in the avai
Externí odkaz:
http://arxiv.org/abs/2002.08254
Autor:
Liebel, Lukas, Körner, Marco
We introduce MultiDepth, a novel training strategy and convolutional neural network (CNN) architecture that allows approaching single-image depth estimation (SIDE) as a multi-task problem. SIDE is an important part of road scene understanding. It, th
Externí odkaz:
http://arxiv.org/abs/1907.11111
Autor:
Liebel, Lukas, Körner, Marco
Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust
Externí odkaz:
http://arxiv.org/abs/1805.06334
While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited. We propose a set of novel quality criteria, allowing for a more detailed analysis by fo
Externí odkaz:
http://arxiv.org/abs/1805.01328
Publikováno v:
In Computer Vision and Image Understanding February 2020 191
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Publikováno v:
AGILE 2015; 2015, p343-362, 20p