Multisource-Data-Fusion for the Digitization of Critical Infrastructural Elements

被引:2
|
作者
Stemmler, S. [1 ,2 ]
Kaufmann, T. [3 ]
Bange, M. J.
Merkle, D. [1 ,2 ]
Reiterer, A. [1 ,2 ]
Klemt-Albert, K. [6 ]
Marx, S. [4 ,5 ]
机构
[1] Fraunhofer Inst Phys Measurement Techn, IPM, Freiburg, Germany
[2] Albert Ludwigs Univ Freiburg, Dept Sustainable Syst Engn, INATECH, Freiburg, Germany
[3] Kaulquappe GmbH, Berlin, Germany
[4] MKP GmbH, Hannover, Germany
[5] Tech Univ Dresden, Fac Civil Engn, Inst Concrete Struct, Dresden, Germany
[6] Rhein Westfal TH Aachen, Chair & Inst Construct Management, Digital Engn & Robot Construct, Aachen, Germany
关键词
UAV; LiDAR Multi-Sensor Fusion; Infrastructure Monitoring;
D O I
10.1117/12.2634711
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Due to the relatively high average age of the rail infrastructure in Germany and the thus often historic plans, as-built documentation has a very high priority at Deutsche Balm AG. The inventory and updating of existing plans represent an enormous challenge for the operator, DB Netz AG. More than 4 6 million inventory plans must be continuously checked to ensure that they are up to date,correct,adjusted and supplemented as necessary. The most fragile structures are railroad bridges. These are the focus of this paper. For now, all information of bridges such as planning documents, statics, status reports of bridge examination, etc. are collected in decentral locations of the owner or operator. The existing information is available in a wide variety of formats, e.g. pdf files, plans on paper, scanned paper plans, digitally created plans, SAPdata and photos. We tackled this problem of non-uniform and decentralized data management within the mdfBIM project. Within the scope of this project, a process model was developed that describes the merging of the various data sources in the planning process and attempts to identify the primary data source in each case. The validation and adaptation of this model was carried out continuously after it had been set up based on a railway bridge in Hannover, Germany. We used machine learning algorithms to enable an automated object classification for the most common objects to derive the highest possible degree of automation. Another important step towards automation was the consolidation of the numerous data sources. This existing, inhomogeneous data was homogenized in a defined process. During this homogenization, the data sets - ranging from existing as-built plans, photo documentation, maintenance and conversion reports, SAP extracts, construction books and construction plans to the newly recorded laser point cloud - was evaluated. In this paper; the complete process chain and the first results are presented. Furthermore, an outlook is given on further research tasks and the further development of the elaborated process chain.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] Digitization of critical infrastructure structures using multi- data fusion
    Jaekel, Jan-Iwo
    Goelzhaeuser, Peter
    Schmitt, Annette
    Bange, Justine
    Klemt-Albert, Katharina
    Reiterer, Alexander
    Marx, Steffen
    [J]. BAUTECHNIK, 2023, 100 (11) : 667 - 673
  • [2] Data fusion and multisource image classification
    Amarsaikhan, D
    Douglas, T
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2004, 25 (17) : 3529 - 3539
  • [3] Data infrastructural design for informing critical evaluation
    Kroenlein, Kenneth
    [J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2016, 251
  • [4] Multisource data fusion for documenting archaeological sites
    Knyaz, Vladimir
    Chibunichev, Alexander
    Zhuravlev, Denis
    [J]. IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXIII, 2017, 10427
  • [5] A neurophysiological paradigm for data fusion in a multisource environment
    Fisher, PS
    Minton, DH
    Fisher, HP
    [J]. MULTISENSOR, MULTISOURCE INFORMATION FUSION: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS 2003, 2003, 5099 : 241 - 250
  • [6] Classification of multisource and hyperspectral data based on decision fusion
    Benediktsson, Jon Atli
    [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37 (3 I): : 1367 - 1377
  • [7] Classification of multisource and hyperspectral data based on decision fusion
    Benediktsson, JA
    Kanellopoulos, I
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1999, 37 (03): : 1367 - 1377
  • [8] Multisource data fusion for bandlimited signals:: a Bayesian perspective
    Jalobeanu, A.
    Gutierrez, J. A.
    [J]. BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING, 2006, 872 : 391 - +
  • [9] Multisource traffic data fusion with entropy based method
    Sun Zhanquan
    Guo Mu
    Liu Wei
    Feng Jinqiao
    Hu Jiaxing
    [J]. 2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL IV, PROCEEDINGS, 2009, : 506 - +
  • [10] A Multisource Heterogeneous Data Fusion Method for Pedestrian Tracking
    Shi, Zhenlian
    Sun, Yanfeng
    Xiong, Linxin
    Hu, Yongli
    Yin, Baocai
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, 2015