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See detailOn Improving Slant Wet delays for Tracking Severe Weather Events: An evaluation During Two Storms in Europe
Teferle, Felix Norman UL; Hunegnaw, Addisu UL; Duman, Huseyin et al

Scientific Conference (2022, December 14)

Climate change has led to an increase in the frequency and severity of weather events with intense precipitation and, subsequently, a greater susceptibility of communities around the world to flash ... [more ▼]

Climate change has led to an increase in the frequency and severity of weather events with intense precipitation and, subsequently, a greater susceptibility of communities around the world to flash flooding. Networks of ground-based Global Navigation Satellite System (GNSS) stations enable the measurement of integrated water vapor along slant pathways, providing three-dimensional (3D) water vapor distributions at low-cost and in real-time. This makes these data a valuable complementary source of information for tracking storm events and predicting their paths. However, it is well established that residual modelling errors and multipath (MP) effects at GNSS stations do impact incoming signals, especially at low elevations and during storms when the atmospheric conditions change rapidly. Until now, the bulk of GNSS products for meteorology are estimates of the more conventional zenith total delays and horizontal gradients, but these products may not be most appropriate for determining 3D distributions of water vapor during convective storm events. In this study we investigate the impact of residual-phase-corrected and multipath-corrected slant wet delay (SWD) estimates on tracking extreme weather events using two events in Europe that led to flooding, damage to property and loss of life. We employed Precise Point Positioning (PPP) with integer ambiguity resolution to generate station-specific MP correction maps. The spatial stacking was carried out in congruent cells with an optimal resolution in elevation and azimuth at the local horizon but with decreasing azimuth resolution as the elevation angle increases. This permits an approximately equal number of observations allocated to each cell. In our analysis we recovered the one-way SWD by adding GNSS post-fit phase residuals, representing the non-isotropic component of the SWD, i.e., the higher-order inhomogeneity. Using the derived MP maps in a final step, the one-way SWD were improved to employ them for the analysis of the weather event. Moreover, we validated the SWD between ground-based water-vapor radiometry and GNSS-derived SWD for different elevation angles. Furthermore, the spatio-temporal fluctuations in the SWD as measured by GNSS closely mirrored the moisture field from the ERA5 re-analysis associated with this severe weather event [less ▲]

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See detailFirst Results of the GNSS Reprocessing at the University of Luxembourg for an Updated Global Vertical Land Movement Data Set
Erkihune, Eshetu Nega UL; Teferle, Felix Norman UL; Hunegnaw, Addisu UL

Presentation (2022, December 12)

The University of Luxembourg (UL) is currently contributing to the most recent reprocessing effort of the International GNSS Service (IGS) Tide Gauge Benchmark Monitoring Working Group (TIGA-WG) with ... [more ▼]

The University of Luxembourg (UL) is currently contributing to the most recent reprocessing effort of the International GNSS Service (IGS) Tide Gauge Benchmark Monitoring Working Group (TIGA-WG) with multi-constellation GNSS solutions, including GPS, GLONASS and Galileo. As part of this new reprocessing and reanalysis effort of GNSS data including stations at or near tide gauges worldwide, several model enhancements consistent with the IGS’s recent effort have been incorporated. During 1994 to 2022 the network generally contains data from over 700 stations. The IGS has placed high importance on unifying processing standards because homogeneous and consistent reprocessing of all GNSS data over the complete time span is necessary for estimating useful geophysical parameters, such as long-term trends in station positions. In addition to the reprocessing of the data, the time series analysis strategy is crucial for deriving accurate long-term estimates. In order to obtain the best parameter estimates and the most realistic uncertainties, it is anticipated that a number of stochastic and deterministic models will be fitted to the position time series. Additionally, the stochastic properties of the series will be investigated. Existing automated processes will be updated with the most recent developments in geodetic time series analysis due to the vast number of stations. Through the full reprocessing of all GNSS observations and the state-of-the-art analysis of the daily position time series, this study will be able to obtain highly accurate estimates of horizontal and vertical land movements that can be employed for the most challenging applications such as correcting coastal sea level records for a better understanding in their changes and constraining glacial isostatic adjustment models. During this presentation, we will provide details on the current reprocessing, present preliminary results and a first cross-evaluation of the vertical land movement estimates. [less ▲]

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See detailForschung in Geodäsie und Geoinformation an der Uni.lu
Teferle, Felix Norman UL

Presentation (2022, November 10)

This is a summary of selected research carried out by the team GGE of the DoE in 2017-2022.

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See detailRobust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Balado, Jesus et al

in Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds (2022, November)

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS ... [more ▼]

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD. [less ▲]

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See detailkCV-B: Bootstrap with Cross-Validation for Deep Learning Model Development, Assessment and Selection
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Laefer, Debra et al

in kCV-B: Bootstrap with Cross-Validation for Deep Learning Model Development, Assessment and Selection (2022, October)

This study investigates the inability of two popular data splitting techniques: train/test split and k-fold cross-validation that are to create training and validation data sets, and to achieve sufficient ... [more ▼]

This study investigates the inability of two popular data splitting techniques: train/test split and k-fold cross-validation that are to create training and validation data sets, and to achieve sufficient generality for supervised deep learning (DL) methods. This failure is mainly caused by their limited ability of new data creation. In response, the bootstrap is a computer based statistical resampling method that has been used efficiently for estimating the distribution of a sample estimator and to assess a model without having knowledge about the population. This paper couples cross-validation and bootstrap to have their respective advantages in view of data generation strategy and to achieve better generalization of a DL model. This paper contributes by: (i) developing an algorithm for better selection of training and validation data sets, (ii) exploring the potential of bootstrap for drawing statistical inference on the necessary performance metrics (e.g., mean square error), and (iii) introducing a method that can assess and improve the efficiency of a DL model. The proposed method is applied for semantic segmentation and is demonstrated via a DL based classification algorithm, PointNet, through aerial laser scanning point cloud data. [less ▲]

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See detailOn the Application of Multipath Stacking Maps for Improved Severe Weather Tracking and Low-cost Antenna Calibration
Teferle, Felix Norman UL; Hunegnaw, Addisu UL; Duman, Hüseyin et al

Presentation (2022, July 07)

Modern cities all over the world are now more susceptible to flash floods as a result of a rise in the frequency and severity of meteorological events with significant precipitation. For minimizing the ... [more ▼]

Modern cities all over the world are now more susceptible to flash floods as a result of a rise in the frequency and severity of meteorological events with significant precipitation. For minimizing the risks due to these hydro-meteorological hazards, reliable fore- and now-casting of severe precipitation has become essential. Water vapor can be measured along slant paths by network of ground-based GNSS stations, providing real-time, three-dimensional (3D) distributions of vapor concentrations at an affordable cost. Consequently, these data provide an invaluable additional source of knowledge for monitoring and predicting events with flash flood potential. But site-specific multipath (MP) effects at GNSS stations do affect incoming signals, particularly at low elevations, as is widely known. The bulk of GNSS products for meteorology up to now are based on estimates of ordinary zenith total delay and horizontal gradients with little sensitivity to azimuthal variations, thus these products may not be best suited for estimating 3D distributions of water vapor during storm events. Using slant delays directly, can overcome this lack of azimuthal information. However, at low elevations, this approach is more susceptible to multipath errors. A thunderstorm event which occurred over Turkey and adjacent countries on July 27, 2017, resulting in flash floods and severe infrastructure damage, is used as an example to explore the effects of multipath-corrected slant wet delay (SWD) estimations on monitoring extreme weather events. To reconstruct the one-way SWD, we first added phase residuals derived from the GNSS one-way post-fit observations, which represent the anisotropic component of the SWD. This can also be interpreted as a higher order inhomogeneity component, which is not resolved by ordinary zenith or gradient products. For the generation of site-specific MP correction maps, we stacked the post-fit residuals derived from our Precise Point Positioning (PPP) processing strategy because the MP errors in the GNSS phase observables can adversely impact the SWD along the direction of individual satellites. The spatial stacking was performed in congruent cells as a function of elevation and azimuth. This enables each cell to receive roughly the same number of residuals, providing a better stacking result. The stacking of residuals for a single cell over several days allows the detection and reduction of systematic errors; random errors are minimal because the averaging is done over a suitably sufficient chosen time span. Finally, the one-way SWD were enhanced by applying these MP correction maps for the analysis of the meteorological event. Our study revealed that the anisotropic component contributed up to 11% of one-way SWD estimates. Furthermore, the spatio-temporal changes in SWD as derived from GNSS closely matched the moisture field from the ERA5 re-analysis linked to this weather event. As it turns out, the MP correction maps may also provide a “kind of calibration” for uncalibrated or low-cost GNSS antennas, even for those in mobile phones, as these devices are highly susceptible to MP errors. In turn, this would allow the application of low-cost sensors to accurately estimate SWD for severe weather monitoring in urban regions. [less ▲]

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See detailDeep Learning for Ground and Non-ground Surface Separation: A Feature-based Semantic Segmentation Algorithm for Point Cloud Classification
Nurunnabi, Abdul Awal Md UL; Lindenbergh, Roderik; Teferle, Felix Norman UL

E-print/Working paper (2022)

Precise ground surface topography is crucial for 3D city analysis, digital terrain modeling, natural disaster monitoring, high-density map generation, and autonomous navigation to name a few. Deep ... [more ▼]

Precise ground surface topography is crucial for 3D city analysis, digital terrain modeling, natural disaster monitoring, high-density map generation, and autonomous navigation to name a few. Deep learning (DL; LeCun, et al., 2015), a division of machine learning (ML), has been achieving unparalleled success in image processing, and recently demonstrated a huge potential for point cloud analysis. This article presents a feature-based DL algorithm that classifies ground and non-ground points in aerial laser scanning point clouds. Recent advancements of remote sensing technologies make it possible digitizing the real world in a near automated fashion. LiDAR (Light Detection and Ranging) based point clouds that are a type of remotely sensed georeferenced data, providing detailed 3D information on objects and environment have been recognized as one of the most powerful means of digitization. Unlike imagery, point clouds are unstructured, sparse and of irregular data format which creates many challenges, but also provides huge opportunities for capturing geometric details of scanned surfaces with millimeter accuracy. Classifying and separating non-ground points from ground points largely reduce data volumes for consecutive analyses of either ground or non-ground surfaces, which consequently saves cost and labor, and simplifies further analysis. [less ▲]

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See detailRobust Approach for Urban Road Surface Extraction Using Mobile Laser Scanning Data
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Lindenbergh, Roderik et al

in Robust Approach for Urban Road Surface Extraction Using Mobile Laser Scanning Data (2022, June)

Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road ... [more ▼]

Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively. [less ▲]

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See detailResampling methods for a reliable validation set in deep learning based point cloud classification
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL

in Resampling methods for a reliable validation set in deep learning based point cloud classification (2022, June)

A validation data set plays a pivotal role in tweaking a machine learning model trained in a supervised manner. Many existing algorithms select a part of available data by using random sampling to produce ... [more ▼]

A validation data set plays a pivotal role in tweaking a machine learning model trained in a supervised manner. Many existing algorithms select a part of available data by using random sampling to produce a validation set. However, this approach can be prone to overfitting. One should follow careful data splitting to have reliable training and validation sets that can produce a generalized model with a good performance for the unseen (test) data. Data splitting based on resampling techniques involves repeatedly drawing samples from the available data. Hence, resampling methods can give better generalization power to a model, because they can produce and use many training and/or validation sets. These techniques are computationally expensive, but with increasingly available high-performance computing facilities, one can exploit them. Though a multitude of resampling methods exist, investigation of their influence on the generality of deep learning (DL) algorithms is limited due to its non-linear black-box nature. This paper contributes by: (1) investigating the generalization capability of the four most popular resampling methods: k-fold cross-validation (k-CV), repeated k-CV (Rk-CV), Monte Carlo CV (MC-CV) and bootstrap for creating training and validation data sets used for developing, training and validating DL based point cloud classifiers (e.g., PointNet; Qi et al., 2017a), (2) justifying Mean Square Error (MSE) as a statistically consistent estimator, and (3) exploring the use of MSE as a reliable performance metric for supervised DL. Experiments in this paper are performed on both synthetic and real-world aerial laser scanning (ALS) point clouds. [less ▲]

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See detailAnalysis of GNSS sensed slant wet delay during severe weather events in central Europe
Hunegnaw, Addisu UL; Duman, Hüseyin; Elgered, Gunnar et al

Scientific Conference (2022, May 26)

Over the last few decades, anthropogenic greenhouse gas emissions have increased the frequency of climatological anomalies such as temperature, precipitation, and evapotranspiration. It is noticed that ... [more ▼]

Over the last few decades, anthropogenic greenhouse gas emissions have increased the frequency of climatological anomalies such as temperature, precipitation, and evapotranspiration. It is noticed that the frequency and severity of the intense precipitation signify a greater susceptibility to flash flooding. Flash flooding continues to be a major threat to European cities, with devastating mortality and considerable damage to urban infrastructure. As a result, accurate forecasting of future extreme precipitation events is critical for natural hazard mitigation. A network of ground-based GNSS receivers enables the measurement of integrated water vapour along slant pathways providing three-dimensional water vapour distributions. This study aims to demonstrate how GNSS sensing of the troposphere can be used to monitor the rapid and extreme weather events that occurred in central Europe in June 2013 and resulted in flash floods and property damage. We recovered one-way slant wet delay (SWD) by adding GNSS post-fit phase residuals, representing the troposphere's higher-order inhomogeneity. Nonetheless, noise in the GNSS phase observable caused by site-specific multipath can significantly affect the SWD from individual satellites. To overcome the problem, we employ a suitable averaging strategy for stacking post-fit phase residuals obtained from the PPP processing strategy to generate site-specific multipath corrections maps (MPS). The spatial stacking is carried out in congruent cells with an optimal resolution in elevation and azimuth at the local horizon but with decreasing azimuth resolution as the elevation angle increases. This permits an approximately equal number of observations allocated to each cell. The spatio-temporal fluctuations in the SWD as measured by GNSS closely mirrored the moisture field associated with severe weather events in central Europe, i.e., a brief rise prior to the main rain events, followed by a rapid decline once the storms passed. Furthermore, we validated the one-way SWD between ground-based water-vapour radiometry (WVR) and GNSS-derived SWD for different elevation angles. [less ▲]

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See detailScanning the Past: A 3D Model of Trausch's Library
Teferle, Felix Norman UL; Wieneke, Lars UL; Parvaz, Shahoriar UL et al

Scientific Conference (2022, May 05)

Today, modern geospatial technologies and methods are widely used in combination with the documentation and preservation of objects of importance to cultural heritage. In this setting, archeologists and ... [more ▼]

Today, modern geospatial technologies and methods are widely used in combination with the documentation and preservation of objects of importance to cultural heritage. In this setting, archeologists and historians alike benefit from the rapid technological developments over the past decades, which have resulted in instrumentation that allows the capture of real objects and the generation of accurate and precise three-dimensional (3D) digital representations, i.e. models, from these sensed data. Here, the object of interest is the villa of the late historian Professor Gilbert Trausch with its library, for which a virtual library should be created for the general public. The building is an 19th century townhouse located on Limpertsberg in the City of Luxembourg and contains a cellar, three floors as well as a loft. Of particular interest were the cellar, stair cases, the first (the location of Professor Trausch’s office) and second floors as most of the bookshelves are situated there. In line with state-of-the-art approaches for 3D building modelling, a broad spectrum of modern geospatial technologies including classical surveying, Global Navigation Satellite System (GNSS), digital close-range photogrammetry and terrestrial laser scanning were employed to capture the Villa Trausch and its 33 bookshelves, i.e. Trausch’s Library, in all its details, while providing all data in one homogeneous coordinate system. Models and more photo-realistic visualizations of the exterior and interior have been obtained using, e.g., indoor images captured during the scanning. These allowed us to explore different virtual reality (VR) pathways employed by the gaming industry, for the generation of a first VR experience of the building in the sense of a digital museum. Currently solutions for the development of a public virtual library using commercial providers are investigated. [less ▲]

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See detailEvaluation of the Multipath Environment Using Electromagnetic-Absorbing Materials at Continuous GNSS Stations
Hunegnaw, Addisu UL; Teferle, Felix Norman UL

in Sensors (2022), 22(9), 1-23

o date, no universal modelling technique is available to mitigate the effect of site-specific multipaths in high-precision global navigation satellite system (GNSS) data processing. Multipaths affect both ... [more ▼]

o date, no universal modelling technique is available to mitigate the effect of site-specific multipaths in high-precision global navigation satellite system (GNSS) data processing. Multipaths affect both carrier-phase and code/pseudorange measurements, and the errors can propagate and cause position biases. This paper presents the use of an Eccosorb AN-W-79 microwave-absorbing material mounted around a GNSS antenna that reflects less than −17 dB of normal incident energy above a frequency of 600 MHz. To verify the feasibility and effectiveness of the Eccosorb, we installed two close stations by continuously operating multi-GNSS (BeiDou, GLONASS, Galileo and GPS) in a challenging location. One station is equipped with the Eccosorb AN-W-79, covering a square area of 3.35 m2 around the antenna, and the second station operates without it. The standard deviation reductions from single point positioning estimates are significant for all the individual GNSS solutions for the station equipped with microwave-absorbing material. The reductions are as follows: for GPS, between 15% and 23%; for Galileo, between 22% and 45%; for GLONASS, 22%; and for BeiDou, 4%. Furthermore, we assess the influence of multipaths by analysing the linear combinations of code and carrier phase measurements for various GNSS frequencies. The Galileo code multipath shows a reduction of more than 60% for the station with microwave-absorbing material. For GLONASS, particularly for the GLOM3X and GLOM1P code multipath combinations, the reduction reaches 50%, depending on the observation code types. For BeiDou, the reduction is more than 30%, and for GPS, it reaches between 20% and 40%. The Eccosorb AN-W-79 microwave-absorbing material shows convincing results in reducing the code multipath noise level. Again, using microwave-absorbing material leads to an improvement between 15% and 60% in carrier phase cycle slips. The carrier-phase multipath contents on the post-fit residuals from the processed GNSS solutions show a relative RMS reduction of 13% for Galileo and 9% for GLONASS and GPS when using the microwave-absorbing material. This study also presents power spectral contents from residual signal-to-noise ratio time series using Morlet wavelet transformation. The power spectra from the antenna with the Eccosorb AN-W-79 have the smallest magnitude, demonstrating the capacity of microwave-absorbing materials to lessen the multipath influence while not eliminating it. [less ▲]

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See detailA TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Laefer, Debra et al

in A TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION (2022, March)

Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to ... [more ▼]

Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to overfitting. Furthermore, reliance on empirically-based feature dimensionality reduction may lead to misclassification. In contrast, efficient feature management can reduce storage and computational complexities, builds better classifiers, and improves overall performance. Principal Component Analysis (PCA) is a well-known dimension reduction technique that has been used for feature extraction. This paper presents a two-step PCA based feature extraction algorithm that employs a variant of feature-based PointNet (Qi et al., 2017a) for point cloud classification. This paper extends the PointNet framework for use on large-scale aerial LiDAR data, and contributes by (i) developing a new feature extraction algorithm, (ii) exploring the impact of dimensionality reduction in feature extraction, and (iii) introducing a non-end-to-end PointNet variant for per point classification in point clouds. This is demonstrated on aerial laser scanning (ALS) point clouds. The algorithm successfully reduces the dimension of the feature space without sacrificing performance, as benchmarked against the original PointNet algorithm. When tested on the well-known Vaihingen data set, the proposed algorithm achieves an Overall Accuracy (OA) of 74.64% by using 9 input vectors and 14 shape features, whereas with the same 9 input vectors and only 5PCs (principal components built by the 14 shape features) it actually achieves a higher OA of 75.36% which demonstrates the effect of efficient dimensionality reduction. [less ▲]

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See detailINVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Li, J. et al

Scientific journal (2021)

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL ... [more ▼]

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL algorithms degrades, because point clouds are often sparse and have irregular data format. As a result, point clouds are regularly first transformed into voxel grids or image collections. PointNet was the first promising algorithm that feeds point clouds directly into the DL architecture. Although PointNet achieved remarkable performance on indoor point clouds, its performance has not been extensively studied in large-scale outdoor point clouds. So far, we know, no study on large-scale aerial point clouds investigates the sensitivity of the hyper-parameters used in the PointNet. This paper evaluates PointNet’s performance for semantic segmentation through three large-scale Airborne Laser Scanning (ALS) point clouds of urban environments. Reported results show that PointNet has potential in large-scale outdoor scene semantic segmentation. A remarkable limitation of PointNet is that it does not consider local structure induced by the metric space made by its local neighbors. Experiments exhibit PointNet is expressively sensitive to the hyper-parameters like batch-size, block partition and the number of points in a block. For an ALS dataset, we get significant difference between overall accuracies of 67.5% and 72.8%, for the block sizes of 5m×5m and 10m×10m, respectively. Results also discover that the performance of PointNet depends on the selection of input vectors. [less ▲]

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See detailPost-Urban Mining Automation and Digitalisation for a Closed- Loop Circular Construction
Akbarieh, Arghavan UL; Schäfer, Markus UL; Waldmann, Daniele UL et al

Poster (2021, October 13)

The large volume of in- and out-flow of raw materials to construction projects has a huge potential to be optimised for resource efficiency and waste reduction. With the recent awareness of the importance ... [more ▼]

The large volume of in- and out-flow of raw materials to construction projects has a huge potential to be optimised for resource efficiency and waste reduction. With the recent awareness of the importance of the circular economy, construction actors are aligning their practices to be more circular and sustainable. The concept of material banks is born out of this awareness in order to document the lifecycle information of materials and facilitate re-using them. The introduction of new cycles before individual materials reach their final lifecycle stages results in reduced negative environmental impacts. This paper presents a workflow by positioning different digital technologies to automate the procedures for reuse assessment: from the deconstructed building to M/C bank to new construction projects. This automation supports a practical material and component reuse, while it provides the necessary infrastructure to digitise and digitalise the post-deconstruction materials to be visualised, selected and used by future designers in Building Information Modelling (BIM)-based design and management environments. To this aim, the coupling of BIM, reality capturing technologies, additive manufacturing techniques, IoT and RFID sensors is also anticipated. [less ▲]

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See detailFeasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance
Yuan, Peng; Hunegnaw, Addisu UL; Alshawaf, Fadwa et al

in Remote Sensing of Environment (2021), 260(112416),

Although the statistical significances for the trends of integrated water vapor (IWV) are essential for a correct interpretation of climate change signals, obtaining accurate IWV trend estimates with ... [more ▼]

Although the statistical significances for the trends of integrated water vapor (IWV) are essential for a correct interpretation of climate change signals, obtaining accurate IWV trend estimates with realistic uncertainties remains a challenge. This study evaluates the feasibility of the IWV trends derived from the newly released fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) for climate change analysis in continental Europe. This is achieved by comparing the trends derived from in-situ ground-based Global Positioning System (GPS)’s daily IWV series from 1994 to 2019 at 109 stations. The realistic uncertainties and statistical significances of the IWV trends are evaluated with the time series analysis on their noise characteristics and proper noise models. Results show that autoregressive moving average ARMA(1,1) noise model is preferred rather than the commonly assumed white noise (WN) or first-order autoregressive AR(1) noise for about 68% of the ERA5 and GPS IWV series. An improper noise model would misevaluate the trend uncertainty of an IWV time series, compared with its specific preferred noise model. For example, ARMA(1,1) may misevaluate the standard deviations of their trend estimates (0.1–0.3 kg m−2 decade−1) by 10%. Nevertheless, ARMA(1,1) is recommended as the default noise model for the ERA5 and GPS IWV series. However, the preferred noise model for each ERA5 minus GPS (E-G) IWV series should be specifically determined, because the AR(1)-related models can result in an underestimation on its trend uncertainty by 90%. In contrast, power-law (PL) model can lead to an overestimation by up to nine times. The E-G IWV trends are within −0.2–0.4 kg m−2 decade−1, indicating that the ERA5 is a potential data source of IWV trends for climate change analysis in continental Europe. The ERA5 and GPS IWV trends are consistent in their magnitudes and geographical patterns, lower in Northwest Europe (0–0.4 kg m−2 decade−1) but higher around the Mediterranean Sea (0.6–1.4 kg m−2 decade−1). [less ▲]

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See detailMulti-GNSS Slant Wet Delay Retrieval Using Multipath Mitigation Maps
Hunegnaw, Addisu UL; Ejigu, Yohannes Getachew; Teferle, Felix Norman UL et al

Scientific Conference (2021, April 24)

The conventional Global Navigation Satellite System (GNSS) processing is typically contaminated with errors due to atmospheric variabilities, such as those associated with the mesoscale phenomena. These ... [more ▼]

The conventional Global Navigation Satellite System (GNSS) processing is typically contaminated with errors due to atmospheric variabilities, such as those associated with the mesoscale phenomena. These errors are manifested in the parameter estimates, including station coordinates and atmospheric products. To enhance the accuracy of these GNSS products further, a better understanding of the local-scale atmospheric variability is necessary. As part of multi-GNSS processing, station coordinates, carrier phase ambiguities, orbits, zenith total delay (ZTD) and horizontal gradients are the main parameters of interest. Here, ZTD is estimated as the average zenith delay along the line-of-sight to every observed GNSS satellite mapped to the vertical while the horizontal gradients are estimated in NS and EW directions and provide a means to partly account for the azimuthally inhomogeneous atmosphere. However, a better atmospheric description is possible by evaluating the slant path delay (SPD) or slant wet delay (SWD) along GNSS ray paths, which are not resolved by ordinary ZTD and gradient analysis. SWD is expected to provide better information about the inhomogeneous distribution of water vapour that is disregarded when retrieving ZTD and horizontal gradients. Usually, SWD cannot be estimated directly from GNSS processing as the number of unknown parameters exceeds the number of observations. Thus, SWD is generally calculated from ZTD for each satellite and may be dominated by un-modelled atmospheric delays, clock errors, unresolved carrier-phase ambiguities and near-surface multipath scattering. In this work, we have computed multipath maps by stacking individual post-fit carrier residuals incorporating the signals from four GNSS constellations, i.e. BeiDou, Galileo, Glonass and GPS. We have selected a subset of global International GNSS Service (IGS) stations capable of multi-GNSS observables located in different climatic zones. The multipath effects are reduced by subtracting the stacked multipath maps from the raw post-fit carrier phase residuals. We demonstrate that the multipath stacking technique results in significantly reduced variations in the one-way post-fit carrier phase residuals. This is particularly evident for lower elevation angles, thus, producing a retrieval method for SWD that is less affected by site-specific multipath effects. We show a positive impact on SWD estimation using our multipath maps during increased atmospheric inhomogeneity as induced by severe weather events. [less ▲]

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See detailMonitoring and prediction of hurricane tracks using GPS tropospheric products
Ejigu, Yohannes Getachew; Teferle, Felix Norman UL; Klos, Anna UL et al

in GPS Solutions (2021), (76),

We have reconstructed integrated water vapor (IWV) using the zenith wet delays to track the properties of hurricanes and explore their spatial and temporal distributions estimated from 922 GPS stations ... [more ▼]

We have reconstructed integrated water vapor (IWV) using the zenith wet delays to track the properties of hurricanes and explore their spatial and temporal distributions estimated from 922 GPS stations. Our results show that a surge in GPS-derived IWV occurred at least six hours prior to the landfall of two major hurricanes (Harvey and Irma) that struck the Gulf and East Coasts of the USA in 2017. We observed enhanced IWV, in particular, for the two hurricanes landfall locations. The observed variations exhibit a correlation with the precipitation value constructed from GPM/IMERG satellite mission coinciding with hurricane storm front passage. We used GPS-IWV data as inputs for spaghetti line plots for our path predictions, helping us predict the paths of Hurricanes Harvey and Irma. Hence, a directly estimable zenith wet delay sourced from GPS that has not been previously reported can serve as an additional resource for improving the monitoring of hurricane paths. [less ▲]

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See detailMultipath Mitigation Maps feasibility and applicability as an International GNSS Service product
Hunegnaw, Addisu UL; Teferle, Felix Norman UL; Elgered, Gunnar et al

Scientific Conference (2020, December 17)

There have been many advances in the modeling of Global Navigation Satellite System (GNSS) observables when estimating position and other parameters of interest. Some of these bias models are related to ... [more ▼]

There have been many advances in the modeling of Global Navigation Satellite System (GNSS) observables when estimating position and other parameters of interest. Some of these bias models are related to improvements of reference frames, phase center offsets and variations of transmitter and receiver antennas, satellite orbits and clocks, and troposphere. Nonetheless, multipath remains for the most part an unmodelled source of error which causes range errors in the GNSS observations. The associated effects show highly localized features and have a different impact for each receiver and antenna. Multipath errors can propagate, can cause in-situ position biases and are also contributing to the prevalent draconitic harmonic signals. In order to mitigate the problem we generate site-specific corrections by employing a suitable averaging scheme for the stacking of carrier phase residuals. Our processing is based on globally distributed static multi-GNSS observations using several scientific GNSS software packages (Bernese GNSS Software, NAPEOS, GAMIT-GLOBK, and CSRS-PPP). Our multipath stacking maps (MPS) use the stacking of carrier phase residuals generated by variable azimuth cell size (congruent cells) and by allocating carrier phase residuals in each cell to generate the correction maps, unlike the standard fixed azimuth cell resolution approaches. This reduces the binning of fewer residuals at higher elevation angles. Before stacking, we also apply rigorous statistical outlier screening tests for each one-way post-fit carrier phase residual assigned to each of the congruent cells. We thus correct the multipath effects by subtracting the stacked multipath map from the post-fit carrier phase residual. Using this technique we produce a model available in the form of the Antenna Exchange (ANTEX) file format, that can potentially be implemented in routine GNSS analysis with no or little additional overhead for individual analysis centers (ACs). In this study, we assess the feasibility and applicability of the MPS maps as an International GNSS Service (IGS) product for routine GNSS analysis. We demonstrate the multipath stacking technique to result in a significant reduction of the variation in the one-way post-fit carrier phase residuals from multi-GNSS observations. [less ▲]

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