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High-Resolution Point-Cloud for Landslides in the 21st Century: From Data Acquisition to New Processing Concepts

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Understanding and Reducing Landslide Disaster Risk (WLF 2020)

Part of the book series: ICL Contribution to Landslide Disaster Risk Reduction ((CLDRR))

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Abstract

The economic downturn at the start of the 21st Century combined with the rise of new economies have helped propelling the need for cheaper, rapid and yet accurate data acquisition methods, including point-cloud producing technologies. As population is ageing in countries like Japan or Taiwan, and as climate change is bound to increase the frequency and number of landslides (at least until sediment stocks are depleted), the need of rapid and low-manpower data acquisition and processing has become increasingly essential. It is within this framework that the present contribution aims (1) to present research on present technologies for landslide monitoring, as well as emerging systems, and (2) to propose “new” ideas for point-cloud processing and data usage without having to grid or interpolate data. The authors thus use a variety of field locations around the pacific and in France using as a method ALS (Airborne Laser Scanning), TLS (Terrestrial Laser Scanning) and SfM-MVS (Structure from Motion—Multiple View Stereophotogrammetry). Then the authors present recent advances in landslide monitoring technology with the YellowScan UAV-ALS. As the resulting mounting amount of data presents constrains to the data processing steps, the authors also present ideas to progress processing: (1) developing a point-cloud signature to avoid gridding, and (2) a conceptual algorithm to process the point-clouds as vectors between a machine and a reflective object, recording the free space and the objects. In this way catastrophic landslides can be characterized from the signature of the data and the distribution of fill and voids without going through the traditional analysis on a Cartesian grid. The authors have worked on those techniques to provide fast and accurate data for landslides hazards and disaster risk.

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Author information

Authors and Affiliations

  1. Graduate School of Maritime Sciences, Laboratory of Sediment Hazards and Disaster Risk, Kobe University, Kobe City, Japan

    C. Gomez

  2. Faculty of Geography, Gadjah Mada University, Yogyakarta, Indonesia

    C. Gomez, D. S. Hadmoko & A. Setiawan

  3. Yellow-Scan, Montpellier, France

    T. Allouis

  4. Department of Geography, Caen University, Caen, France

    C. Lissak

  5. Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan

    N. Hotta

  6. Department of Forest Environmental Sciences Miyazaki, University of Miyazaki, Miyazaki, Japan

    Y. Shinohara

  7. Department of Geography, Charles University, Prague, Czech Republic

    V. Vilimek

  8. Geography Department and CNRS LGP Laboratory, Sorbonne University, Paris, France

    P. Wassmer & F. Lavigne

  9. Faculty of Agriculture, Gadjah Mada University, Yogyakarta, Indonesia

    J. Sartohadi

  10. Faculty of Geography, University Muhammadiyah Surakarta, Surakarta, Indonesia

    A. Saputra

  11. Department of Geography, University of Canterbury, Christchurch, New Zealand

    T. Rahardianto

Authors
  1. C. Gomez
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  2. T. Allouis
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  3. C. Lissak
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  6. D. S. Hadmoko
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  7. V. Vilimek
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  8. P. Wassmer
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  9. F. Lavigne
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  10. A. Setiawan
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  12. A. Saputra
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  13. T. Rahardianto
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Corresponding author

Correspondence to C. Gomez .

Editor information

Editors and Affiliations

  1. Faculty of Civil Engineering, University of Rijeka, Rijeka, Croatia

    Željko Arbanas

  2. Geological Survey of Canada, Sidney, BC, Canada

    Peter T. Bobrowsky

  3. International Consortium on Landslides, Kyoto, Japan

    Kazuo Konagai

  4. International Consortium on Landslides, Kyoto University, Uji Campus, Kyoto, Japan

    Kyoji Sassa

  5. Graduate School of Advanced Integrated Studies in Human Survivability (Shishu-kan), Kyoto University, Kyoto, Japan

    Kaoru Takara

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Gomez, C. et al. (2021). High-Resolution Point-Cloud for Landslides in the 21st Century: From Data Acquisition to New Processing Concepts. In: Arbanas, Ž., Bobrowsky, P.T., Konagai, K., Sassa, K., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60713-5_22

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