Zobrazeno 1 - 10
of 2 156
pro vyhledávání: '"Doulamis, A"'
In this paper an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D Semantic Segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of the 3DSS deep lear
Externí odkaz:
http://arxiv.org/abs/2411.02104
To facilitate effective decision-making, gridded satellite precipitation products should include uncertainty estimates. Machine learning has been proposed for issuing such estimates. However, most existing algorithms for this purpose rely on quantile
Externí odkaz:
http://arxiv.org/abs/2407.01623
The environmental hazards and climate change effects causes serious problems in land and coastal areas. A solution to this problem can be the periodic monitoring over critical areas, like coastal region with heavy industrial activity (i.e., ship-buil
Externí odkaz:
http://arxiv.org/abs/2405.06730
Autor:
Kavouras, Ioannis, Rallis, Ioannis, Sardis, Emmanuel, Protopapadakis, Eftychios, Doulamis, Anastasios, Doulamis, Nikolaos
The scarcity of green spaces, in urban environments, consists a critical challenge. There are multiple adverse effects, impacting the health and well-being of the citizens. Small scale interventions, e.g. pocket parks, is a viable solution, but comes
Externí odkaz:
http://arxiv.org/abs/2404.15492
Predictions in the form of probability distributions are crucial for decision-making. Quantile regression enables this within spatial interpolation settings for merging remote sensing and gauge precipitation data. However, ensemble learning of quanti
Externí odkaz:
http://arxiv.org/abs/2403.10567
Autor:
Tzortzis, Ioannis N., Makantasis, Konstantinos, Rallis, Ioannis, Bakalos, Nikolaos, Doulamis, Anastasios, Doulamis, Nikolaos
Limited amount of data and data sharing restrictions, due to GDPR compliance, constitute two common factors leading to reduced availability and accessibility when referring to medical data. To tackle these issues, we introduce the technique of Learni
Externí odkaz:
http://arxiv.org/abs/2402.06379
A methodology based on deep recurrent models for maritime surveillance, over publicly available Automatic Identification System (AIS) data, is presented in this paper. The setup employs a deep Recurrent Neural Network (RNN)-based model, for encoding
Externí odkaz:
http://arxiv.org/abs/2406.09966
Publikováno v:
Machine Learning: Science and Technology 5 (2024) 035044
Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostl
Externí odkaz:
http://arxiv.org/abs/2311.07511
Publikováno v:
Remote Sensing 15 (2023) 4912
Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependen
Externí odkaz:
http://arxiv.org/abs/2307.06840
Autor:
Katsamenis, Iason, Protopapadakis, Eftychios, Bakalos, Nikolaos, Doulamis, Anastasios, Doulamis, Nikolaos, Voulodimos, Athanasios
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying no dynamic adaptation to users' feedback. To address this drawback, we
Externí odkaz:
http://arxiv.org/abs/2303.01582