Auflistung Fakultät für Bauingenieurwesen und Geodäsie nach Schlagwort "Machine learning"

Auflistung Fakultät für Bauingenieurwesen und Geodäsie nach Schlagwort "Machine learning"

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  • Automated damage detection for port structures using machine learning algorithms in heightfields 
    Hake, Frederic; Lippmann, Paula; Alkhatib, Hamza; Oettel, Vincent; Neumann, Ingo (Berlin ; Heidelberg : Springer, 2023)
    Marine infrastructures such as harbours, bridges, and locks are particularly exposed to salt water and are therefore subject to increasing deterioration. This makes regular inspection of the structures necessary. The ...
  • Boosted unsupervised multi-source selection for domain adaptation 
    Vogt, Karsten; Paul, A.; Ostermann, Jörn; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2017)
    Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but ...
  • Building generalization using deep learning 
    Sester, Monika; Feng, Yu; Thiemann, Frank (Göttingen : Copernicus GmbH, 2018)
    Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g. simplification, displacement, ...
  • Identification of similarities and prediction of unknown features in an urban street network 
    Feuerhake, Udo; Wage, O.; Sester, M.; Tempelmeier, N.; Nejdl, W.; Demidova, E. (Göttingen : Copernicus GmbH, 2018)
    Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and ...
  • Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India 
    Singha, Chiranjit; Swain, Kishore Chandra; Moghimi, Armin; Foroughnia, Fatemeh; Swain, Sanjay Kumar (Amsterdam [u.a.] : Elsevier Science, 2024)
    Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily ...
  • Prediction of individual's movement based on interesting places 
    Feuerhake, Udo (Göttingen : Copernicus GmbH, 2012)
    In general, the development of prediction methods is a quite challenging field. However, as difficult the development is, as useful those methods can be in a large variety of use cases. Whether the weather of tomorrow or ...
  • Probabilistic multi-person tracking using dynamic bayes networks 
    Klinger, Tobias; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2015)
    Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations ...
  • Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy): a comparative study 
    Otto, Philipp; Fusta Moro, Alessandro; Rodeschini, Jacopo; Shaboviq, Qendrim; Ignaccolo, Rosaria; Golini, Natalia; Cameletti, Michela; Maranzano, Paolo; Finazzi, Francesco; Fassò, Alessandro (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2024)
    This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest ...
  • Transfer learning based on logistic regression 
    Paul, Andreas; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2015)
    In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer ...
  • Using Semantic Distance to Support Geometric Harmonisation of Cadastral and Topographical Data 
    Schulze, Malte Jan; Thiemann, Frank; Sester, Monika (Göttingen : Copernicus GmbH, 2014)
    In the context of geo-data infrastructures users may want to combine data from different sources and expect consistent data. If both datasets are maintained separately, different capturing methods and intervals leads to ...

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