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Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) have been used in various monitoring applications for the past two decades, as one of the very few options to provide absolute positions in a global reference frame. However, high performance GNSS stations are expensive, and sometimes may be impractical because of their size, power consumption or software requirements. Thus, the use of low-cost GNSS stations for structural health monitoring (SHM) has gained increasing attention. This paper presents a detailed experimental assessment of multiple combinations of GNSS receivers and antennas, and highlights an optimal cost-efficient solution for monitoring applications. Several sets of processing parameters and constraints are also evaluated using open source RTKLib software. The performance of the proposed solution is evaluated through two experimental dynamic scenarios, proving its ability to track quick displacements down to 4 mm and oscillations of 1 cm with a frequency up to 0.25 Hz with a 1 Hz receiver. Finally, a two-week dataset acquired from on a network of low-cost GNSS stations deployed on a suspended bridge is used to validate on-site performance. Results show good agreement between GNSS time series, traditional displacement sensors, and numerical simulations made using an operational mechanical model of the bridge, highlighting the potential of such low-cost solutions for structural health monitoring applications. [ABSTRACT FROM AUTHOR]

The statistical properties of atmospheric water vapor mixing ratio (WVMR) determined as the ratio of Raman lidar signals backscattered from water vapor and nitrogen molecules are studied. It is shown that WVMR estimates can be biased by a small percentage at low signal photon-counting rates due to fluctuations in the nitrogen signal in the denominator of the ratio, the magnitude of the bias being linked to the signal-to-noise ratio of the nitrogen signal. This is particularly important when unbiased estimates are required as in the case of climate studies and global positioning system (GPS) signal calibration. Different bias corrections and a modified ratio formulation are proposed in order to correct or eliminate this bias. The method is successfully applied in processing signals obtained with an experimental Raman lidar system devoted to calibrate GPS signals for slant path delays. It is shown to reduce biases into negligible values in both WVMR and wet path delay estimates in the range interval of 0-7 km. OCIS codes: 010.3920, 010.7030, 010.3640, 280.3640.

A wide-angle laser ranging system has been developed for high-precision muitilateration. A laser transmits short pulses in a widely diverging beam toward a network of cube-corner retroreflectors (CCR's). Returned echoes are detected, amplified, digitized, and timed. Impact of instrumental error sources, atmospheric refraction, and scintillation are discussed. The precision of the correlation method for the estimation of times of arrival and a least-squares adjustment method for the estimation of relative CCR coordinates is studied. Data from several ground-based experiments are analyzed. The positioning precision achieved is of 1-2.3 mm in relative radial coordinates and 1.8-3.8 cm in relative transverse coordinates. Results are consistent with a covariance analysis, demonstrating that, as follows: 1) systematic instrumental errors are well corrected; 2) outliers arising from coincident echoes are properly detected; 3) models for forward and inverse problems and estimation techniques are correct. A numerical simulation program based on these models and techniques can be used for optimizing instrumental aspects, providing extensive simulations in various conditions and processing data from future airborne experiments to which the system is devoted. Index Terms - Geodesy, laser ranging, multilateration.

Photogrammetric processing is available in various software solutions and can easily deliver 3D pointclouds as accurate as 1 pixel. Certain applications, e.g., very accurate shape reconstruction in industrial metrology or change detection for deformation studies in geosciences, require results of enhanced accuracy. The tie-point extraction step is the opening in the photogrammetric processing chain and therefore plays a key role in the quality of the subsequent image orientation, camera calibration and 3D reconstruction. Improving its precision will have an impact on the obtained 3D. In this research work we describe a method which aims at enhancing the accuracy of image orientation by adding a second iteration photogrammetric processing. The result from the classical processing is used as a priori information to guide the extraction of refined tie-points of better photogrammetric quality. Evaluated on indoor and UAV acquisitions, the proposed methodology shows a significant improvement on the obtained 3D point accuracy. [ABSTRACT FROM AUTHOR]

We have investigated calibration variations in the Rameau water vapor Raman lidar. This lidar system was developed by the Institut National de l'Information Géographique et Forestière (IGN) together with the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS). It aims at calibrating Global Navigation Satellite System (GNSS) measurements for tropospheric wet delays and sounding the water vapor variability in the lower troposphere. The Rameau system demonstrated good capacity in retrieving water vapor mixing ratio (WVMR) profiles accurately in several campaigns. However, systematic shortterm and long-term variations in the lidar calibration factor pointed to persistent instabilities. A careful testing of each subsystem independently revealed that these instabilities are mainly induced by mode fluctuations in the optic fiber used to couple the telescope to the detection subsystem and by the spatial nonuniformity of the photomultiplier photocathodes. Laboratory tests that replicate and quantify these instability sources are presented. A redesign of the detection subsystem is presented, which, combined with careful alignment procedures, is shown to significantly reduce the instabilities. Outdoor measurements were performed over a period of 5 months to check the stability of the modified lidar system. The calibration changes in the detection subsystem were monitored with lidar profile measurements using a common nitrogen filter in both Raman channels. A short-term stability of 2-3% and a long-term drift of 2-3% per month are demonstrated. Compared to the earlier Development of Methodologies forWater Vapour Measurement (DEMEVAP) campaign, this is a 3-fold improvement in the long-term stability of the detection subsystem. The overall water vapor calibration factors were determined and monitored with capacitive humidity sensor measurements and with GPS zenith wet delay (ZWD) data. The changes in the water vapor calibration factors are shown to be fairly consistent with the changes in the nitrogen calibration factors. The nitrogen calibration results can be used to correct the overall calibration factors without the need for additional water vapor measurements to within 1% per month. [ABSTRACT FROM AUTHOR]

Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles) exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l’information géographique) camera, which has an IMU (Inertial Measurement Unit) sensor and an SoC (System on Chip)/FPGA (Field-Programmable Gate Array). To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N-th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test) detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work. [ABSTRACT FROM AUTHOR]

In classical photogrammetric processing pipeline, the automatic tie point extraction plays a key role in the quality of achieved results. The image tie points are crucial to pose estimation and have a significant influence on the precision of calculated orientation parameters. Therefore, both relative and absolute orientations of the 3D model can be affected. By improving the precision of image tie point measurement, one can enhance the quality of image orientation. The quality of image tie points is under the influence of several factors such as the multiplicity, the measurement precision and the distribution in 2D images as well as in 3D scenes. In complex acquisition scenarios such as indoor applications and oblique aerial images, tie point extraction is limited while only image information can be exploited. Hence, we propose here a method which improves the precision of pose estimation in complex scenarios by adding a second iteration to the classical processing pipeline. The result of a first iteration is used as a priori information to guide the extraction of new tie points with better quality. Evaluated with multiple case studies, the proposed method shows its validity and its high potiential for precision improvement. [ABSTRACT FROM AUTHOR]

In Antarctica, locally grounded ice, such as ice rises bordering floating ice shelves, plays a major role in the ice mass balance as it stabilizes the ice sheet flow from the hinterland. When in direct contact with the ocean, the ice rise buttressing effect may be altered in response of changing ocean forcing. To investigate this vulnerable zone, four sites near the boundary of an ice shelf with an ice rise promontory in Dronning Maud Land, East-Antarctica were monitored for a month in early 2014 with new instruments that include both seismic and GPS sensors. Our study indicated that this transition zone experiences periodic seismic activity resulting from surface crevassing during oceanic tide-induced flexure of the ice shelf. The most significant finding is the observation of apparent fortnightly tide-modulated low-frequency, long-duration seismic events at the seaward front of the ice rise promontory. A basal origin of these events is postulated with the ocean water surge at each new spring tide triggering basal crevassing or basal slip on a local bedrock asperity. Detection and monitoring of such seismicity may help identifying ice rise zones vulnerable to intensified ocean forcing. [ABSTRACT FROM AUTHOR]

A detailed investigation of calibration variation sources in the instrumental part of the detection-fibered, water vapor Raman lidar, Rameau, is presented. This lidar has been developed by the Institut National de l'Information Géographique et Forestière (IGN) together with the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS) and aims at calibrating GNSS wet delay signals and thus at improving vertical positioning. Several measurements campaigns enabled to validate the capacity of the instrument to retrieve high accuracy water vapor measurements. However, in order to insure a good stability, regular calibrations were necessary and led us to seek for instability sources in each sub-system of the instrument. The calibration variations are shown to be induced by fiber mode fluctuations and spatial non-uniformity of the photomultiplier photocathodes, and are responsible for significant calibration coefficient drifts. Such drifts are incompatible with both the long term stability required for applications such as climatology and the absolute accuracy needed for wet path delay correction of GNSS signals. We show by means of experimental tests that variation sources can be mitigated by means of an optimization and re-design of the optical detection system, a careful alignment procedure, and the operational monitoring of the system with dedicated measurements. In order to validate the modifications of the system and the new procedure, measurements were repeated over a period of 5 months. The detection subsystem stability was monitored from lidar profiles measured with a unique nitrogen filter used for detecting the signal in the two measurement channels. Compared to the previous campaign (Development of Methodologies for Water Vapor Measurement, Demevap), we observe an improvement in the stability of the system based on the nitrogen measurements which showed a drift of less than 3 & % per month and a standard deviation of about 3 & % during the campaign. The water vapor calibration coefficients were determined from capacitive humidity sensor measurements and from GPS zenith wet delays measurements. They show a similar small drift of 3 & % per month and a standard deviation of ~ & 6 & %. Thanks to the N2 measurements, the drift can be completely removed. Lower standard deviation can be achieved by increasing the Signal to Noise Ratio and/or spatial and temporal integration window. [ABSTRACT FROM AUTHOR]

In this article, the author focuses on workplace air quality and the consensus standards organizations including American Society for Testing and Materials, Technical Committee 137 on Assessment of Workplace Exposure to Chemical and Biological Agents and International Organization for Standardization. It states current activities of the voluntary consensus standards organizations related to workplace atmospheres. It highlights continuous activities in developing harmonized standard.

Monitoring small size area deformations calls for increasingly precise data with greater space and time resolution. To this aim, a GPS data processing method designed for wireless networks of mono-frequency GPS receivers is proposed. The different steps of the method are explained: the GPS processing is first detailed with the design of parametric and stochastic models, their inversion by an extended Kalman filter and the mitigation of the main errors: multipath. A parsimonious radio data transfer protocol is then proposed. It allows a real-time positioning thanks to a wireless transfer of GPS carrier phases data from receivers to a processing computer. Finally the method is tested on a network of mono-frequency receivers developed by the French National Mapping Agency (IGN). Tests prove its ability for real-time positioning with a fifteen receivers network, and a precision under the centimeter level is reached. [ABSTRACT FROM AUTHOR]

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After 10 years of pioneering aerial digital camera development, IGN's CAMv2 project began in 2006 with the main goal of upgrading the then current system. This had been used for research applications since 1996 in more than six different configurations and in production since 2003 configured with four coloured channels. The new system is still highly modular, enabling combinations of several 'basic components', each consisting of one digital camera head with its storage and control unit. The new camera head was developed around the Kodak KAF-39 000, 39-megapixel array sensor (7216 × 5412 pixels). The system is also very versatile thanks to the possible choice of more than 10 different lenses (from 35 mm up to 180 mm focal length) and interchangeable spectral filters. The mechanical and electronic designs have been reduced in size so as to permit many different configurations including multiple camera heads on the same gyro-stabilised mount. The combination of the performance of camera heads and control and storage units allows a frame rate of one image per 2 s with storage redundancy and 1 s without, leading to a minimum ground sample distance (GSD) of about 9 cm with 60% overlap at an airspeed of 100 m/s. In order to use the new system to best advantage, the adjustment and calibration processes had to be improved. During the summer of 2009, three systems configured for colour imagery, each using four nadir-viewing camera heads (RGB and NIR), were used to acquire images for IGN production work. In January 2009, an eight-camera-head configuration was tested in order to achieve 155-megapixel pan-sharpened images with a pan-sharpening ratio of 2 × 2, and a final swath width of about 14 400 pixels. At that time this configuration needed two windows in the aircraft, but it is now installed on a single, bigger mount. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) are increasingly used for topographic mapping. The camera calibration for UAV image blocks can be performed a priori or during the bundle block adjustment (self-calibration). For an area of interest with flat scene and corridor configuration, the focal length of camera is highly correlated with the height of the camera. Furthermore, systematic errors of camera calibration accumulate on the longer dimension and cause deformation. Therefore, special precautions must be taken when estimating camera calibration parameters. In order to better investigate the impact of camera calibration errors, a synthetic, error-free aerial image block is generated to simulate several issues of interest. Firstly, the erroneous focal length in the case of camera pre-calibration is studied. Nadir images are not able to prevent camera poses from drifting to compensate for the erroneous focal length, whereas the inclusion of oblique images brings significant improvement. Secondly, the case where the focal length varies gradually (e.g., when the camera subject to temperature changes) is investigated. The neglect of this phenomenon can substantially degrade the 3D measurement accuracy. Different flight configurations and flight orders are analyzed, the combination of oblique and nadir images shows better performance. At last, the rolling shutter effect is investigated. The influence of camera rotational motion on the final accuracy is negligible compared to that of the translational motion. The acquisition configurations investigated are not able to mitigate the degradation introduced by the rolling shutter effect. Other solutions such as correcting image measurements or including camera motion parameters in the bundle block adjustment should be exploited. [ABSTRACT FROM AUTHOR]

Photogrammetric processing is available in various software solutions and can easily deliver 3D pointclouds as accurate as 1 pixel. Certain applications, e.g., very accurate shape reconstruction in industrial metrology or change detection for deformation studies in geosciences, require results of enhanced accuracy. The tie-point extraction step is the opening in the photogrammetric processing chain and therefore plays a key role in the quality of the subsequent image orientation, camera calibration and 3D reconstruction. Improving its precision will have an impact on the obtained 3D. In this research work we describe a method which aims at enhancing the accuracy of image orientation by adding a second iteration photogrammetric processing. The result from the classical processing is used as a priori information to guide the extraction of refined tie-points of better photogrammetric quality. Evaluated on indoor and UAV acquisitions, the proposed methodology shows a significant improvement on the obtained 3D point accuracy.