Evidence map›Paper›PMID 37809392›Full record

ArticleHeliyon2023

Hybrid system model for wind abrasion segmentation using semi-automatic classification of remote sensing multispectral areas.

Marta Terrados-Cristos, Francisco Ortega-Fernández, Marina Díaz-Piloñeta, Vicente Rodríguez Montequín, José Valeriano Álvarez Cabal

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 1 institution in 1 country.

Marta Terrados-CristosProject Engineering Department, University of Oviedo, 33004, Oviedo, Spain.
Francisco Ortega-FernándezProject Engineering Department, University of Oviedo, 33004, Oviedo, Spain.
Marina Díaz-PiloñetaProject Engineering Department, University of Oviedo, 33004, Oviedo, Spain.
Vicente Rodríguez MontequínProject Engineering Department, University of Oviedo, 33004, Oviedo, Spain.
José Valeriano Álvarez CabalProject Engineering Department, University of Oviedo, 33004, Oviedo, Spain.
Universidad de Oviedo · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wind abrasion, caused by particles transported by strong winds impacting on structures, can lead to their degradation. Although this phenomenon has hardly been studied in this context, it is becoming increasingly important due to new trends in infrastructure location, especially in renewable energy terms. Metallic structures are particularly vulnerable to degradation by the action of windblown sand particles. However, characterising such secluded sites is complicated, and remote sensing systems and satellite information become crucial. The objective of this research is to identify and delineate the geographic areas that are vulnerable to this phenomenon by employing a hybrid model with historical data and the semi-automatic classification of multispectral satellite images. The model is based on critical variables identified by the scientific community and case studies documented in the literature. The methodology used for the study consists of four phases, including creating a scientifically robust database, downloading and managing satellite and historical long-term information, segmenting the regions of interest, and modelling using supervised classification techniques. The proposed algorithm shows very accurate results (R

Indexed as

AbrasionGISMetallic resilient structuresRemote sensingWind erosion

Identifiers

PMID37809392
PMCPMC10558917
OpenAlexW4386402051

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.