Evidence map›Paper›PMID 37407602›Full record

ArticleScientific reports2023

A new point cloud processing method unveiled hidden coastal boulders from deep vegetation.

Koki Nakata, Hideaki Yanagisawa, Kazuhisa Goto

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.6field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 13 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Koki NakataDepartment of Earth and Planetary Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. nakata-koki864@g.ecc.u-tokyo.ac.jp.
Hideaki YanagisawaDepartment of Regional Design, Faculty of Liberal Art, Tohoku Gakuin University, 2-1-1 Tenjinzawa, Izumi-ku, Sendai, Miyagi, 981-3193, Japan.
Kazuhisa GotoDepartment of Earth and Planetary Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
The University of Tokyo · JPTohoku Gakuin University · JP

Funding

Japan Society for the Promotion of Science 21H04508
6 · The paper itself

Abstract

Huge coastal boulders are useful to reconstruct the size of past extreme waves such as those associated with tsunamis and storms using inverse-type or forward-type boulder transport models. These models fundamentally require the precise shape of boulders. Traditionally, they have often been assumed to be rectangular or ellipsoidal with three axes measured in the field. However, if the boulder's shape is complex, this method is unable to represent the actual shape accurately. Therefore, it prevents estimation of the tsunami or storm size reasonably using models. For this reason, boulders have recently been surveyed using 3D scanning techniques such as LiDAR. However, coastal boulders now on land in tropical and subtropical areas such as Japan and Tonga are often covered by deep vegetation, which makes 3D surveys difficult. This report presents new methods to ascertain boulder shapes when they are obscured by vegetation. First, using UAV-type and mobile-type LiDAR, we scanned well-known tsunami boulders in southwestern Japan that had been covered with deep vegetation. Then, we developed a new method to extract only boulders and filter out vegetation from a point cloud. Thereby, we created 3D models of the boulders. We improved the boulder transport model further to assume the 3D boulder model accurately. In addition to coastal boulders, this filtering method is expected to be useful for unveiling any object, such as an archaeological structure, that is hidden in deep vegetation.

Identifiers

PMID37407602
PMCPMC10322928
OpenAlexW4383218660

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.