31,653 research outputs found

    Determination of forest road surface roughness by kinect depth imaging

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    Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities. Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect\u2122 depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness. The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles

    Semi-automated detection of surface degradation on bridges based on a level set method

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    Due to the effect of climate factors, natural phenomena and human usage, buildings and infrastructures are subject of progressive degradation. The deterioration of these structures has to be monitored in order to avoid hazards for human beings and for the natural environment in their neighborhood. Hence, on the one hand, monitoring such infrastructures is of primarily importance. On the other hand, unfortunately, nowadays this monitoring effort is mostly done by expert and skilled personnel, which follow the overall data acquisition, analysis and result reporting process, making the whole monitoring procedure quite expensive for the public (and private, as well) agencies. This paper proposes the use of a partially user-assisted procedure in order to reduce the monitoring cost and to make the obtained result less subjective as well. The developed method relies on the use of images acquired with standard cameras by even inexperienced personnel. The deterioration on the infrastructure surface is detected by image segmentation based on a level sets method. The results of the semi-automated analysis procedure are remapped on a 3D model of the infrastructure obtained by means of a terrestrial laser scanning acquisition. The proposed method has been successfully tested on a portion of a road bridge in Perarolo di Cadore (BL), Italy

    Integration of LIDAR and IFSAR for mapping

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    LiDAR and IfSAR data is now widely used for a number of applications, particularly those needing a digital elevation model. The data is often complementary to other data such as aerial imagery and high resolution satellite data. This paper will review the current data sources and the products and then look at the ways in which the data can be integrated for particular applications. The main platforms for LiDAR are either helicopter or fixed wing aircraft, often operating at low altitudes, a digital camera is frequently included on the platform, there is an interest in using other sensors such as 3 line cameras of hyperspectral scanners. IfSAR is used from satellite platforms, or from aircraft, the latter are more compatible with LiDAR for integration. The paper will examine the advantages and disadvantages of LiDAR and IfSAR for DEM generation and discuss the issues which still need to be dealt with. Examples of applications will be given and particularly those involving the integration of different types of data. Examples will be given from various sources and future trends examined

    Towards the optimal Pixel size of dem for automatic mapping of landslide areas

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    Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

    Improving Displacement Measurement for Evaluating Longitudinal Road Profiles

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    2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper introduces a half-wavelength peak matching (HWPM) model, which improves the accuracy of vehicle based longitudinal road profilers used in evaluating road unevenness and mega-textures. In this application, the HWPM model is designed for profilers which utilize a laser displacement sensor with an accelerometer for detecting surface irregularities. The process of converting acceleration to displacement by double integration (which is used in most rofilers) is error-prone, and although there are techniques to minimize the effect of this error, this paper proposes a novel approach for improving the generated road profile results. The technique amends the vertical displacement derived from the accelerometer samples, by using data from the laser displacement sensor as a reference. The vehicle based profiler developed for this experiment (which uses the HWPM model) shows a huge improvement in detected longitudinal irregularities when compared with pre-processed results, and uses a 3-m rolling straight edge as a benchmark.Peer reviewe

    A Platform for Proactive, Risk-Based Slope Asset Management, Phase II

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    INE/AUTC 15.0

    Multi-technique approach to rockfall monitoring in the Montserrat massif (Catalonia, NE Spain)

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    Montserrat Mountain is located near Barcelona in Catalonia, in the northeast of Spain, and its massif is formed by conglomerate interleaved by siltstone/sandstone with steep slopes very prone to rockfalls. The increasing number of visitors in the monastery area, reaching 2.4 million per year, has highlighted the risk derived from rockfalls for this building area and also for the terrestrial accesses, both roads and the rack railway. A risk mitigation plan has been launched, and its first phase during 2014-2016 has been focused largely on testing several monitoring techniques for their later implementation. The results of the pilot tests, performed as a development from previous sparse experiences and data, are presented together with the first insights obtained. These tests combine four monitoring techniques under different conditions of continuity in space and time domains, which are: displacement monitoring with Ground-based Synthetic Aperture Radar and characterization at slope scale, with an extremely non-uniform atmospheric phase screen due to the stepped topography and atmosphere stratification; Terrestrial Laser Scanner surveys quantifying the frequency of small or even previously unnoticed rockfalls, and monitoring rock block centimetre scale displacements; the monitoring of rock joints implemented through a wireless sensor network with an ad hoc design of ZigBee loggers developed by ICGC; and, finally, monitoring singular rock needles with Total Station.Peer ReviewedPostprint (author's final draft
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