Automated snow avalanche monitoring for Austria: State of the art and roadmap for future work

被引:7
|
作者
Kapper, Kathrin Lisa [1 ]
Goelles, Thomas [1 ,2 ]
Muckenhuber, Stefan [1 ,2 ]
Trugler, Andreas [1 ,3 ,4 ]
Abermann, Jakob [1 ]
Schlager, Birgit [1 ,2 ]
Gaisberger, Christoph [1 ]
Eckerstorfer, Markus [5 ]
Grahn, Jakob [6 ]
Malnes, Eirik [6 ]
Prokop, Alexander [7 ,8 ]
Schoner, Wolfgang [1 ]
机构
[1] Graz Univ, Inst Geog & Reg Sci, Graz, Austria
[2] Virtual Vehicle Res GmbH, E E & Software, Graz, Austria
[3] Know Ctr GmbH, Graz, Austria
[4] Graz Univ Technol, Inst Interact Syst & Data Sci, Graz, Austria
[5] Norwegian Water Resources & Energy Directorate, Oslo, Norway
[6] NORCE Norwegian Res Ctr, Tromso, Norway
[7] Univ Vienna, Dept Geol, Vienna, Austria
[8] Snow Scan GmbH, Vienna, Austria
来源
FRONTIERS IN REMOTE SENSING | 2023年 / 4卷
关键词
remote sensing; synthetic aperture radar; machine learning; sentinel-1; Austrian alps; U-net; snow avalanches; SAR IMAGES; DEPTH; LIDAR; SEGMENTATION; METHODOLOGY; PERFORMANCE; NETWORKS;
D O I
10.3389/frsen.2023.1156519
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Avalanches pose a significant threat to the population and infrastructure of mountainous regions. The mapping and documentation of avalanches in Austria is mostly done by experts during field observations and covers usually only specific localized areas. A comprehensive mapping of avalanches is, however, crucial for the work of local avalanche commissions as well as avalanche warning services to assess, e.g., the avalanche danger. Over the past decade, mapping avalanches from satellite imagery has proven to be a promising and rapid approach to monitor avalanche activity in specific regions. Several recent avalanche detection approaches use deep learning-based algorithms to improve detection rates compared to traditional segmentation algorithms. Building on the success of these deep learning-based approaches, we present the first steps to build a modular data pipeline to map historical avalanche cycles in Copernicus Sentinel-1 imagery of the Austrian Alps. The Sentinel-1 mission has provided free all-weather synthetic aperture radar data since 2014, which has proven suitable for avalanche mapping in a Norwegian test area. In addition, we present a roadmap for setting up a segmentation algorithm, in which a general U-Net approach will serve as a baseline and will be compared with the mapping results of additional algorithms initially applied to autonomous driving. We propose to train the U-Net using labeled training dataset of avalanche outlines from Switzerland, Norway and Greenland. Due to the lack of training and validation data from Austria, we plan to compile the first avalanche archive for Austria. Meteorological variables, e.g., precipitation or wind, are highly important for the release of avalanches. In a completely new approach, we will therefore consider weather station data or outputs of numerical weather models in the learning-based algorithm to improve the detection performance. The mapping results in Austria will be complemented with pointwise field measurements of the MOLISENS platform and the RIEGL VZ-6000 terrestrial laser scanner.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] State of the art of 4D seismic monitoring: the technique, the record, and the future
    Fanchi, John R.
    Pagano, Theodore A.
    Davis, Thomas L.
    Oil and Gas Journal, 1999, 97 (22): : 38 - 40
  • [42] Biological monitoring in occupational and environmental medicine - The present state of the art and future prospects
    Angerer, J
    BIOLOGICAL MONITORING: PROSPECTD IN OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, 2002, : 5 - 15
  • [43] Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges
    Wallscheid, Oliver
    IEEE OPEN JOURNAL OF INDUSTRY APPLICATIONS, 2021, 2 : 204 - 223
  • [44] State of the art of 4D seismic monitoring: The technique, the record, and the future
    Fanchi, JR
    Pagano, TA
    Davis, TL
    OIL & GAS JOURNAL, 1999, 97 (22) : 38 - +
  • [45] EU-Russia Energy Relations, EU Energy Integration, and Energy Security: the State of the Art and a Roadmap for Future Research
    Kustova, Irina
    JOURNAL OF CONTEMPORARY EUROPEAN RESEARCH, 2015, 11 (03) : 287 - 295
  • [46] A survey of blockchain consensus safety and security: State-of-the-art, challenges, and future work?
    Bao, Qihao
    Li, Bixin
    Hu, Tianyuan
    Sun, Xueyong
    JOURNAL OF SYSTEMS AND SOFTWARE, 2023, 196
  • [47] Does shared leadership always work? A state-of-the-art review and future prospects
    Chen, Wei
    Zhang, Jun-Hui
    JOURNAL OF WORK-APPLIED MANAGEMENT, 2023, 15 (01) : 51 - 66
  • [48] Systematic literature review on the state of the art and future research work in anonymous communications systems
    Nia, Mehran Alidoost
    Ruiz-Martinez, A.
    COMPUTERS & ELECTRICAL ENGINEERING, 2018, 69 : 497 - 520
  • [49] Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions
    Wischnewski, Magdalena
    Kraemer, Nicole
    Mueller, Emmanuel
    PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), 2023,
  • [50] Electromagnetic field monitoring and control systems: State-of-the-art and work-in-progress
    Fabbri, S
    Frigo, F
    Violanti, S
    Andreucetti, D
    Bini, M
    RADIATION PROTECTION DOSIMETRY, 2001, 97 (04) : 395 - 400