Active landslide monitoring using remote sensing data, GPS measurements and cameras on board UAV

Nikolakopoulos, Konstantinos G.; Kavoura, Katerina; Depountis, Nikolaos; Argyropoulos, N. G.; Koukouvelas, Ioannis; Sabatakakis, N.

doi:10.1117/12.2195394

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…Sin embargo, el método más reciente para el monitoreo de deslizamiento de tierra en laderas es la utilización de imágenes de ultra alta resolución capturadas desde UAV. Lucier, De Jong y Turner [46], además de Nikolakopoulos et al [47] destacan que el uso de UAV con fines de investigación se ha vuelto posible y asequible debido a desarrollos tecnológicos como sistemas de piloto automático, cámaras de acción livianas, GNSS en miniatura, avances en fuselajes de fibra de carbono y el desarrollo simultáneo de nuevas metodologías de procesamiento basadas en visión por computadora como la Structure from Motion. Con la Structure from Motion se pueden obtener fotografías del objeto de interés con superposición suficiente (por ejemplo, 80-90 %) desde múltiples posiciones y / o ángulos [44].…”

Section: Sensores Remotosunclassified

Uso de drones y sensores remotos para el monitoreo de laderas: una revisión

Chaves-Pabón

2021

Ing.USBMed

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Los deslizamientos de tierra son uno de los peligros y riesgos más costoso y fatales para cualquier infraestructura vial, que frecuentemente amenazan e influyen en la situación socioeconómica, principalmente, de los países en vía de desarrollo. Frecuentemente, los estudios de deslizamiento se hacen de manera directa, in situ, siendo muchos de ellos costosos y un reto dada la dificultad que se pueden presentar al acceder a las zonas de estudio. Los datos de los sensores remotos se pueden usar en el monitoreo de deslizamientos de tierra, mapeo, simulación, predicción y evaluación de peligros y otras investigaciones. El presente artículo presenta una revisión bibliográfica en estudios relacionados con el uso de drones y sensores remotos para el seguimiento y monitoreo de laderas, de una manera descriptiva y que permita por medio de la documentación incorporar información relevante para poder abordar futuros estudios más específicos que permitan tener en cuenta el monitoreo de deslizamientos de tierra, las técnicas de sensores remotos, drones y la aplicación global de este conjunto de nuevas tecnologías.

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Section: Sensores Remotosunclassified

Uso de drones y sensores remotos para el monitoreo de laderas: una revisión

Chaves-Pabón

2021

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“…Airborne Light Detection and Ranging (LiDAR), combined with interference elimination data preprocessing, is mainly used in landslide investigation to provide high-resolution point clouds of the topography, find ancient landslides, detect mass movements and monitor landslides, as well as to assess hazards [13,14]. In recent years, the Global Navigation Satellite System (GNSS) has become fully operational and can broadly monitor the surface displacement of slopes [15][16][17][18]. Distributed Optical Fiber Sensors (DOFS) are currently widely used for the monitoring of landslides [19].…”

Section: Introductionmentioning

confidence: 99%

Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Dong

Hu³

et al. 2020

Sensors

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With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

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“…Several recent studies have also demonstrated the power of Structure from Motion algorithms for landslide mapping and monitoring (Niethammer, Rothmund, Schwaderer, Zeman, & Joswig, 2011;Akca, 2013;Lucieer et al, 2014;Nikolakopoulos et al, 2015a;Turner, Lucieer, & De Jong, 2015). Different studies demonstrating the use of UAV data for landslide monitoring have been published (Lin, Tao, Wang, & Huang, 2010;Rau, Jhan, Lob, & Linb, 2011;Turner et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The use of UAVs for research purposes has become possible and cost affordable due to technological developments such as autopilot systems, lightweight action cameras, miniature GNSS, advances in carbon fiber airframes and the simultaneous development of new processing methodologies based on computer vision like the Structure from Motion (Lucieer, De Jong, & Turner, 2014;Nikolakopoulos et al, 2015a). Instead of using the analog airphoto stereopairs with overlap along the flight path, the Structure from Motion process starts by acquiring photographs of the object of interest with sufficient overlap (e.g.…”

Section: Introductionmentioning

confidence: 99%

Preliminary results from active landslide monitoring using multidisciplinary surveys

Nikolakopoulos

Kavoura

Depountis

et al. 2017

European Journal of Remote Sensing

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This study presents the synergy of multidisciplinary surveys for the monitoring of an active landslide in Western Greece. The aim of this paper is to highlight and validate a methodology based on multiple sensors data integration which can successfully be used to manage natural disasters or to improve the knowledge of a specific phenomenon in order to prevent and mitigate the risk. Photogrammetric and interferometric processing has been applied to a complex set of remote sensing data such as high resolution satellite images, digital airphotos, aerial photos acquired from an Unmanned Aerial Vehicle and radar data. Global Navigation Satellite System measurements and continuous inclinometer measurements are being performed. The multifunctional technology of Geographic Information Systems is used in order to collect, storage, manage, process, analyze and cartographically represent the previously described complex geoscientific information. ARTICLE HISTORY

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Active landslide monitoring using remote sensing data, GPS measurements and cameras on board UAV

Cited by 13 publications

References 15 publications

Uso de drones y sensores remotos para el monitoreo de laderas: una revisión

Uso de drones y sensores remotos para el monitoreo de laderas: una revisión

Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Preliminary results from active landslide monitoring using multidisciplinary surveys

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