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Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes u...

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Autores principales: Al-Rawabdeh, Abdulla, Moussa, Adel, Foroutan, Marzieh, El-Sheimy, Naser, Habib, Ayman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677266/
https://www.ncbi.nlm.nih.gov/pubmed/29057847
http://dx.doi.org/10.3390/s17102378
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author Al-Rawabdeh, Abdulla
Moussa, Adel
Foroutan, Marzieh
El-Sheimy, Naser
Habib, Ayman
author_facet Al-Rawabdeh, Abdulla
Moussa, Adel
Foroutan, Marzieh
El-Sheimy, Naser
Habib, Ayman
author_sort Al-Rawabdeh, Abdulla
collection PubMed
description Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.
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spelling pubmed-56772662017-11-17 Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications Al-Rawabdeh, Abdulla Moussa, Adel Foroutan, Marzieh El-Sheimy, Naser Habib, Ayman Sensors (Basel) Article Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research. MDPI 2017-10-18 /pmc/articles/PMC5677266/ /pubmed/29057847 http://dx.doi.org/10.3390/s17102378 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Al-Rawabdeh, Abdulla
Moussa, Adel
Foroutan, Marzieh
El-Sheimy, Naser
Habib, Ayman
Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications
title Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications
title_full Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications
title_fullStr Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications
title_full_unstemmed Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications
title_short Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications
title_sort time series uav image-based point clouds for landslide progression evaluation applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677266/
https://www.ncbi.nlm.nih.gov/pubmed/29057847
http://dx.doi.org/10.3390/s17102378
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