bluuebunny/crossref_metadata_2025 huggingface dataset:
TREE SPECIES CLASSIFICATION BASED ON 3D SPECTRAL POINT CLOUDS AND ORTHOMOSAICS ACQUIRED BY SNAPSHOT HYPERSPECTRAL UAS SENSOR
Authors: Iseli C. , Lucieer A.
Published: June 2019

Abstract. In recent years, there has been a growing number of small hyperspectral sensors suitable for deployment on unmanned aerial systems (UAS. The introduction of the hyperspectral snapshot sensor provides interesting opportunities for acquisition of three-dimensional (3D) hyperspectral point clouds based on the structure-from-motion (SfM) workflow. In this study, we describe the integration of a 25-band hyperspectral snapshot sensor (PhotonFocus camera with IMEC 600 – 875 nm 5x5 mosaic chip) on a multi-rotor UAS. The sensor was integrated with a dual frequency GNSS receiver for accurate time synchronisation and geolocation. We describe the sensor calibration workflow, including dark current and flat field characterisation. An SfM workflow was implemented to derive hyperspectral 3D point clouds and orthomosaics from overlapping frames. On-board GNSS coordinates for each hyperspectral frame assisted in the SfM process and allowed for accurate direct georeferencing (< 10 cm absolute accuracy). We present the processing workflow to generate seamless hyperspectral orthomosaics from hundreds of raw images. Spectral reference panels and in-field spectral measurements were used to calibrate and validate the spectral signatures. This process provides a novel data type which contains both 3D, geometric structure and detailed spectral information in a single format. First, to determine the potential improvements that such a format could provide, the core aim of this study was to compare the use of 3D hyperspectral point clouds to conventional hyperspectral imagery in the classification of two Eucalyptus tree species found in Tasmania, Australia. The IMEC SM5x5 hyperspectral snapshot sensor was flown over a small native plantation plot, consisting of a mix of the Eucalyptus pauciflora and E. tenuiramis species. High overlap hyperspectral imagery was captured and then processed using SfM algorithms to generate both a hyperspectral orthomosaic and a dense hyperspectral point cloud. Additionally, to ensure the optimum spectral quality of the data, the characteristics of the hyperspectral snapshot imaging sensor were analysed utilising measurements captured in a laboratory environment. To coincide with the generated hyperspectral point cloud data, both a file format and additional processing and visualisation software were developed to provide the necessary tools for a complete classification workflow. Results based on the classification of the E. pauciflora and E. tenuiramis species revealed that the hyperspectral point cloud produced an increased classification accuracy over conventional hyperspectral imagery based on random forest classification. This was represented by an increase in classification accuracy from 67.2% to 73.8%. It was found that even when applied separately, the geometric and spectral feature sets from the point cloud both provided increased classification accuracy over the hyperspectral imagery.
A NORMALIZED SURF FOR MULTISPECTRAL IMAGE MATCHING AND BAND CO-REGISTRATION
Authors: Jhan J. P. , Rau J. Y.
Published: June 2019

Abstract. Due to the raw images of multi-lens multispectral (MS) camera has significant misregistration errors, performing image registration for band co-registration is necessary. Image matching is an essential step for image registration, which obtains conjugate features on the overlapped areas, and use them to estimate the coefficients of a transformation model for correcting the geometrical errors. However, due to the none-linear intensity of spectral response, performing feature-based image matching (such as SURF) can only obtain only a few conjugate features on cross-band MS images. Different to SURF that extracts local extremum in a multi-scale space and utilizes a threshold to determine a feature, we proposed a normalized SURF (N-SURF) that extracts features on single scale, calculates the cumulative distribution function (CDF) of features, and obtains consistent features from the CDF. In this study, two datasets acquired from Tetracam MiniMCA-12 and Micasense RedEdge Altum are used for evaluating the matching performance of N-SURF. Results show that N-SURF can extract approximately 2–3 times number of features, match more points, and have more efficient than original SURF. On the other hand, with the successful of MS image matching, we can therefor use the conjugates to compute the coefficients of a geometric transformation model. In this study, three transformation models are used to compare the difference on MS band co-registration, i.e. affine, projective, and extended projective. Results show that extended projective model is better than the others as it can compensate the difference of lens distortion and viewpoint, and has co-registration accuracy of 0.3–0.6 pixels.
LOCAL VERSUS GLOBAL VARIATIONAL APPROACHES TO ENHANCE WATERSHED TRANSFORMATION BASED INDIVIDUAL TREE CROWN SEGMENTATION OF DIGITAL SURFACE MODELS FROM 3K OPTICAL IMAGERY
Authors: Kempf C. , Tian J. , Kurz F. , d’Angelo P. , Reinartz P.
Published: June 2019

Abstract. Detection and delineation of forest trees in airborne observational data has been under study for decades, starting with images. With the advent of 3D point cloud generation techniques, much research has been spent for point cloud segmentation. From a cost perspective, aerial images are still advantageous. In this paper, two individual tree crown segmentation approaches for digital surface models are compared. Both methods attempt to enhance the drawbacks of watershed segmentation in unmanaged forests by applying a variational technique, locally to a watershed segment or globally to the image, respectively. The preprocessing by means of local histogram equalization that is necessary to harness the globally applied technique simultaneously improves the performance of the feature detection, while resulting boundaries are distorted. In contrast, the approach that uses the locally applied technique does not perform local histogram equalization prior to feature detection. It produces better localized boundaries in cases where detection is correct, but has a significantly lower rate of detection.
UAV AND SATELLITE IMAGERY APPLIED TO ALIEN SPECIES MAPPING IN NW SPAIN
Authors: Martínez-Sánchez J. , González-de Santos L. M. , Novo A. , González-Jorge H.
Published: June 2019

Abstract. Image classification stands as an essential tool for automated mapping, that is demanded by agencies and stakeholders dealing with geospatial information. Decreasing costs or UAV-based surveying and open access to high resolution satellite images such as that provided by European Union’s Copernicus programme are the basis for multi-temporal landscape analysis and monitoring. Besides that, invasive alien species are considered a risk for biodiversity and their inventory is needed for further control and eradication. In this work, a methodology for semi-automatic detection of invasive alien species through UAV surveying and Sentinel 2 satellite monitoring is presented and particularized for Acacia dealbata Link species in the province of Pontevedra, in NW Spain. We selected a scenario with notable invasion of Acaciae and performed a UAS surveying to outline feasible training areas. Such areas were used as bounds for obtaining a spectral response of the cover from Sentinel 2 images with a level of processing 2A, that was used for invasive area detection. Sparse detected areas were treated as a seed for a region growing step to obtain the final map of alien species.
MAPPING ARTIFICIAL TERRACES FROM IMAGE MATCHING POINT CLOUD IN LOESS PLATEAU OF CHINA
Authors: Na J. , Yang X. , Fang X. , Tang G. , Pfeifer N.
Published: June 2019

Abstract. The Loess Plateau of China, as one of the most affected areas in the world, suffers from serious gully erosion due to its fragmented terrains and erosional materials. The farmland is terraced, i.e. artificial terraces are widely constructed in this region from 1960s to improve the food productivity. While from late 1990s, a project “Grain for Green” start to change those built artificial terraces from the agricultural use into ecologic areas, helping to conserve water. Mapping the artificial terraces, both their distribution and boundaries, is the basis of monitoring their extent and understanding their ecological effects. The drone-based image matching technology provides a possible solution. In this study, an automatic extraction method for artificial terraces was proposed based on the image-matching point cloud. Firstly, an image-matching point cloud was generated using the Pix4d software. Then the vegetation index and height difference were applied on the original point cloud for positive (non-gully) – negative (gully area) terrain segmentation. After that, edge detection on normal vector difference was performed in the non-gully area to define the ridges of artificial terraces. The case study was performed in a small catchment Wucheng in Shanxi province. A comparison between the manual delineation result and the automatic extraction result indicates our method has a total classification accuracy of 85.8%. The proposed method considers comprehensively of topography and landcover. We conclude that it has a an optimistic potential in loess hilly-gully region with similarly complex terrains and diverse vegetation covers.
EFFICIENT FLIGHT PLANNING FOR BUILDING FAÇADE 3D RECONSTRUCTION
Authors: Palanirajan H. K. , Alsadik B. , Nex F. , Oude Elberink S.
Published: June 2019

Abstract. Three-dimensional (3D) building model is gaining more scientific attention in recent times due to its application in various fields such as vehicle autonomous navigation, urban planning, heritage building documentation, gaming visualisation and tourism. The quality of the Level of Detail (LoD) of building models relies on the high-resolution data sets obtained for the building. As an alternative to laser scanners, Unmanned Aerial Vehicles (UAV) are efficient in collecting good quality images and generate reliable LoD3 of buildings (i.e. to model both roof and facades of a building) at comparatively lower cost and time. However, the complete collection of images on building facades is usually performed by manual flights along the different façade to assure a homogenous image coverage with the same resolution on each element: no offline autonomous procedure to define the main façade planes and acquire complete image sets independent of UAV platform have been developed yet. This paper proposes a novel methodology to generate the flight plan in correspondence of building facades. The Digital Surface Model (DSM) obtained from an initial nadir flight is used as an input to identify the target building and plan the image acquisition around it. The optimised flight plan ensures complete coverage of the building with a minimum number of images. The coordinates as well as the attitude of each planned image can be finally loaded on a UAV to perform the flight. In order to validate the proposed methodology, some tests performed on synthetic buildings of growing complexity and very different shapes are presented.
LONG LINE CLIFF TOPOGRAPHY MEASUREMENT BY USING STRUCTURE FROM MOTION ALGORITHM IN PHOTOGRAMMETRY
Authors: Altuntas C.
Published: June 2019

Abstract. The topography of cliffs and steep slopes must be measured to acquire additional information for landscaping, visualizing changes and taking precautions against natural hazards. The Earth topography has been measured predominantly with photogrammetry, terrestrial/aerial laser scanning or other traditional measurement techniques. The stereo photogrammetry necessitates greater effort to obtain a three-dimensional (3D) model of the imaged surface. Meanwhile, terrestrial or aerial laser scanning can collect high-density measurements of spatial data in a short time. However, the costs of implementing laser scanning instruments are very high. Furthermore, conventional measurement techniques that use total stations require immense effort to collect complete 3D measurements of cliffs. On the other hand, dense image based point cloud using multi-view photogrammetry based on structure from motion (SfM) algorithm is much more effective than the others for measuring the Earth topography. In this study, the cliff topography of an old quarry located in the state of Selcuklu of Konya Province in Turkey was measured by multi-view photogrammetry. The cliff has a continuous length of approximately 600 metres and a height of 25 metres in some places. The 3D model of the cliff was generated with the image based dense point cloud of multi-view photogrammetry. Then 3D dense point cloud model was registered into a local georeference system by using control points (CPs). Because of the long line measurement area, number and localization of the CPs is very important for achieving a high-accuracy to registration into georeferenced system. The registration accuracies were evaluated for different number and distribution of the CPs with the residuals on the check points (ChPs). The high accuracy registration was acquired with uniform distributed 3 and 8 CPs as the residuals of 24.08 cm and 23.03 cm on the ChPs respectively. The results indicated that 3D measurement of long line cliffs can be performed using multi-view photogrammetry, and the registration should be made with the uniform distributed CPs. In addition, a texture-mapped 3D model and orthophoto images of the cliff surfaces were created for detailed visualization.
MARKED POINT PROCESSES FOR THE AUTOMATIC DETECTION OF BOMB CRATERS IN AERIAL WARTIME IMAGES
Authors: Kruse C. , Rottensteiner F. , Heipke C.
Published: June 2019

Abstract. Many countries were the target of air strikes during the Second World War. The aftermath of such attacks is felt until today, as numerous unexploded bombs or duds still exist in the ground. Typically, such areas are documented in so-called impact maps, which are based on detected bomb craters. This paper proposes a stochastic approach to automatically detect bomb craters in aerial wartime images that were taken during World War II. In this work, one aspect we investigate is the type of object model for the crater: we compare circles with ellipses. The respective models are embedded in the probabilistic framework of marked point processes. By means of stochastic sampling the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function which describes the conformity with a predefined model. High gradient magnitudes along the border of the object are favoured and overlapping objects are penalized. In addition, a term that requires the grey values inside the object to be homogeneous is investigated. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global optimum of the energy function. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results, based on 22 aerial wartime images, show the general potential of the method for the automated detection of bomb craters and the subsequent automatic generation of an impact map.
REMOTE SENSING UAV/DRONE TECHNOLOGY AS A TOOL FOR URBAN DEVELOPMENT MEASURES IN APCRDA
Authors: Preethi Latha T. , Naga Sundari K. , Cherukuri S. , Prasad M. V. V. S. V.
Published: June 2019

Abstract. Now-a-days, collecting accurate and meaningful information about the urban localities/environment with the maximum efficiency in terms of cost and time has become more relevant for urban, rural and city level development planning and administration. This work presents a technical procedure for automatic extraction of building information and characterization of different urban building types within the Andhra Pradesh Capital Region Development Authority (APCRDA) jurisdiction areas using UAVs. The methodology consists of a number of sequential processes of acquisition and generation of high resolution Orthomosaic images, creation of 3D point cloud data, and image classification algorithm for feature extraction using exclusively the geometric coordinates. The main parameters of the urban structures/buildings assessed in this work are site area of the building, built-up area, and building dimensions, building setbacks and building height. Different geometric and appropriate metrics were automatically extracted for each of the elements, defining the urban typology. In this study, residential and commercial buildings were considered for the analysis and the measurements from Drone were validated with respective approved plans and manual inspections and showed positive results with threshold parameters like setbacks and height as per building bye-laws of Andhra Pradesh Government Order (G.O) 119. Based on the results, measurements from Drone are used for the buildings occupancy permissions following the State government building rules. This automated system would replace physical inspections and manual reports and significantly reduce costs and improve efficiency. As an important component in this pilot study, visualisation of the building information were represented / displayed on a web application in an interactive mode. This added value of UAV technology with an automated system in comparison with traditional ways provides geospatial information and can also be considered as an essential Earth Observation indicator which has the potential to lead to next generation Urban Information Services and in the Smart cities development. The considerable potential use of these indicators in urban planning and development offers an opportunity in appropriate decision making in day to day urban planning measures.
REAL-TIME POWERLINE CORRIDOR INSPECTION BY EDGE COMPUTING OF UAV LIDAR DATA
Authors: Pu S. , Xie L. , Ji M. , Zhao Y. , Liu W. , Wang L. , Zhao Y. , Yang F. , Qiu D.
Published: June 2019

Abstract. This paper presents an innovative power line corridor inspection approach using UAV LiDAR edge computing and 4G real real-time transmission. First, sample point clouds of power towers are manually classified and decomposed into components according to five mainstream tower types: T type, V type, n type, I type and owl head type. A deep learning AI agent, named “Tovos Age Agent” internally, is trained by supervised deep learning the sample data sets under a 3D CNN framework. Second, laser points of power line corridors are simultaneously classified into Ground, Vegetation, Tower, Cable, and Building types using semantic feature constraints during the UAV-borne LiDAR acquisition process, and then tower types are further recognized by Tovos Agent for strain span separation. Spatial and topological relations between Cable points and other types are analyzed according to industry standards to identify potential risks at the same time. Finally, all potential risks are organized as industry standard reports and transmitted onto central server via 4G data link, so that maintenance personal can be notified the risks as soon as possible. Tests on LiDAR data of 1000 KV power line show the promising results of the proposed method.
RPAS IN THE SUPPORT FOR PHOTOGRAMMETRY EDUCATION: CASES IN TOPOGRAPHIC MAPPING AND DOCUMENTATION OF HISTORICAL MONUMENTS
Authors: Reiss M. L. L. , Mendes T. S. G. , Andrade M. R. M. , Amory A. M. , de Lara R. , Souza S. F.
Published: June 2019

Abstract. Currently on the world market there are several models and manufacturers of RPAS-UAV. This equipment made it possible to complete the entire flow of the photogrammetric mapping process in teaching for the students of the Cartographic Engineering course. In the Institute of Geosciences, this course already counted on the photogrammetric stations, equipped with hardware for 3D visualization (graphic card, high-speed monitors and stereoscopic vision glasses), photogrammetric software for data processing, and equipment for geodetic measurement control points. Before the acquisition of the RPAS, for the accomplishment of the academic works, it was necessary to buy or to look for in donations from companies of aerial photographs, that did not always correspond to an area of scientific interest. In addition, the scenes in the photos were not current, and it was impossible to have control points that were pre-signalized. From the availability of the RPAS in the University, the students began to have more interest by Photogrammetry in their professional end of coursework and throughout the disciplines. It became possible to carry out in practice all the processes involved in the mapping of nearby areas of interest, from the planning to the generation of photogrammetric products, such as the generation of DTM (Digital Terrain Model), DEM (Digital Elevation Model), orthophotomaps and extraction of features. In addition to products related to the 3D mapping of terrains, mainly through the RPAS of rotating wings, there was also the possibility of mapping and reconstructing 3D structures, such as buildings of historical monuments.
UAV/SATELLITE MULTISCALE DATA FUSION FOR CROP MONITORING AND EARLY STRESS DETECTION
Authors: Sagan V. , Maimaitijiang M. , Sidike P. , Maimaitiyiming M. , Erkbol H. , Hartling S. , Peterson K. T. , Peterson J. , Burken J. , Fritschi F.
Published: June 2019

Abstract. Early stress detection is critical for proactive field management and terminal yield prediction, and can aid policy making for improved food security in the context of climate change and population growth. Field surveys for crop monitoring are destructive, labor-intensive, time-consuming and not ideal for large-scale spatial and temporal monitoring. Recent technological advances in Unmanned Aerial Vehicle (UAV) and high-resolution satellite imaging with frequent revisit time have proliferated the applications of this emerging new technology in precision agriculture to address food security challenges from regional to global scales. In this paper, we present a concept of UAV and satellite virtual constellation to demonstrate the power of multi-scale imaging for crop monitoring. Low-cost sensors integrated on a UAV were used to collect RGB, multispectral, and thermal images during the growing season in a test site established near Columbia, Missouri, USA. WorldView-3 multispectral data were pan-sharpened, atmospherically corrected to reflectance and combined with UAV data for temporal monitoring of early stress. UAV thermal and multispectral data were calibrated to canopy temperature and reflectance following a rigorous georeferencing and ortho-correction. The results show that early stress can be effectively detected using multi-temporal and multi-scale UAV and satellite observation; the limitations of satellite remote sensing data in field-level crop monitoring can be overcome by using low altitude UAV observations addressing not just mixed pixel issues but also filling the temporal gap in satellite data availability enabling capture of early stress. The concept developed in this paper also provides a framework for accurate and robust estimation of plant traits and grain yield and delivers valuable insight for high spatial precision in high-throughput phenotyping and farm field management.
A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY
Authors: Xu Y. , Hu X. , Wei Y. , Yang Y. , Wang D.
Published: June 2019

Abstract. The demand for timely information about earth’s surface such as land cover and land use (LC/LU), is consistently increasing. Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample. For satellite remote sensing image, however, sample datasets covering large scope are still limited. Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area. For large scope (national level) view, choosing a sufficient unbiased sampling method is crucial for constructing balanced training sample dataset. Dependable spatial sample locations considering spatial heterogeneity of land cover are needed for choosing sample images. This paper introduces an ongoing work on establishing a national scope sample dataset for high spatial-resolution satellite remote sensing image processing. Sample sites been chosen sufficiently using spatial sampling method, and divided sample patches been grouped using clustering method for further uses. The neural network model for road detection trained our dataset subset shows an increased performance on both completeness and accuracy, comparing to two widely used public dataset.
A GRAPH-MATCHING APPROACH TO INDOOR LOCALIZATION USING A MOBILE DEVICE AND A REFERENCE BIM
Authors: Bot F. J. , Nourian P. , Verbree E.
Published: June 2019

Abstract. The presented method provides for a possibility to perform graph-based indoor localization, by comparing the topological structure embedded in a mesh model to the topological structure of a semantically rich reference model, specifically a BIM. However different in nature and structure, both input sources can be converted to a graph of similar calibre, such that they can be tested for a match. After a match between both graphs is found, the current position of the actor within the mesh model can be translated to the room found in the graph. This room is now connected to a room within the reference graph, for which the semantics are stored in the BIM. Returning these to the actor, a location description can be formed.Many indoor positioning methods have been developed, which can provide an actor with a relative geometric place. Most preferred are positioning systems not relying on a contingent system, which can be performed using a fusion of sensors embedded into a mobile device. Such a system found to perform sufficiently is VI-SLAM, simultaneously building a geometric place and tracking each pose and heading relatively. Its output is a mesh model, in which a viewshed of the indoor environment is built. Indoor localization however, entails finding a humanly interpretable qualitative description of a place, rather than a quantitative position in Euclidean space. In order to transcend such a position to a meaningful location, the context of an indoor environment has to be understood. As the interpretation of indoor space is different across the board, misalignment in data sources representing the indoor environment occurs.
CONFIGURATION AND SIMULATION TOOL FOR 360-DEGREE STEREO CAMERA RIG
Authors: Hasler O. , Loesch B. , Blaser S. , Nebiker S.
Published: June 2019

Abstract. The demand for capturing outdoor and indoor scenes is rising with the digitalization trend in the construction industry. An efficient solution for capturing these environments is mobile mapping. Image-based systems with 360° panoramic coverage allow a rapid data acquisition and can be made user-friendly accessible when hosted in a cloud-based 3D geoinformation service. The design of such a 360° stereo camera system is challenging since multiple parameters like focal length, stereo base length and environmental restrictions such as narrow corridors are influencing each other. Therefore, this paper presents a toolset, which helps configuring and evaluating such a panorama stereo camera rig. The first tool is used to determine, from which distance on 360° stereo coverage depending on the parametrization of the rig is achieved. The second tool can be used to capture images with the parametrized camera rig in different virtual indoor and outdoor scenes. The last tool supports stitching the captured images together in respect of the intrinsic and extrinsic parameters from the configuration tool. This toolset radically simplifies the evaluation process of a 360° stereo camera configuration and decreases the number of physical MMS prototypes.
A FASTER R-CNN APPROACH FOR EXTRACTING INDOOR NAVIGATION GRAPH FROM BUILDING DESIGNS
Authors: Niu L. , Song Y. Q.
Published: June 2019

Abstract. The indoor navigation graph is crucial for emergency evacuation and route guidance. However, most of existing solutions are limited to the tedious manual solutions and inefficient automatic solutions of the indoor building designs. In this paper, we strive to combine the cutting-edge faster R-CNN deep learning models with spatial connection rules to provide fine quality indoor navigation graphs. The extraction experiment result is convincing for general navigation purpose. But there exist several shortages for faster R-CNN models to overcome, such as optimizations of the complex object detections and ability of handling irregular shape regions for indoor navigation graph extractions.
A BOOK RETRIEVAL AND LOCATION SYSTEM BASED ON REAL-SCENE 3D
Authors: Wei S. , Li B. , Guo Z. , Guo S. , Cheng L.
Published: June 2019

Abstract. With the development of urbanization, the building structures are more and more complex, and various moving objects, such as human beings, robots and unmanned aerial vehicles, often travel through indoor and outdoor 3D space, which puts forward higher requirements for the accurate search and location in indoor and outdoor space. At present, most of the spatial location methods for indoor entities are carried out through 2D maps. However, the indoor environment is a complex 3D space, which increases the difficulty of the search process. In addition, 2D map cannot accurately display the 3D spatial position of the entity. Therefore, it is difficult for 2D maps to search and locate in complex environment. Therefore, how to quickly and effectively carry out spatial location query in complex indoor environment has become an urgent problem to be solved. Taking the library of Beijing University of Civil Engineering and Architecture as an example, this paper obtains the indoor 3D information of the library based on SLAM, processes and publishes the acquired 3D information on IndoorViewer, and uses its API in the book retrieval system. Finally, a book retrieval and location system based on real-scene 3D is finished.
POTENTIAL AND LIMITATIONS OF TERRESTRIAL LASER SCANNING FOR DISCONTINUITY ROUGHNESS ESTIMATION
Authors: Bitenc M. , Kieffer D. S. , Khoshelham K.
Published: June 2019

Abstract. Terrestrial Laser Scanning (TLS) greatly facilitates the acquisition of detailed and accurate 3D measurements of remote rock outcrops, at an operational range from several meters to a few kilometres. Reliable, quantitative measures of rock discontinuity roughness are necessary to characterize and evaluate the mechanical and hydraulic behavior of the rock mass. The aim of this research is to investigate the TLS potential and limitations for a reliable estimation of small scale roughness. TLS data noise and resolution define the level of extractable morphological detail, and therefore need to be known and associated with roughness value. The stationary variant of Discrete Wavelet Transform (SWT) was applied to estimate TLS noise level and perform wavelet denoising in direction of range measurements. Denoised TLS data were compared to reference surfaces of decreasing resolution (reference grids) in order to define the size of extractable surface detail. Noise and resolution effect on rock surface roughness, wavelet denoising success and extractable roughness scale were investigated with comparative analyses of TLS and reference surfaces. The developed methodology enabled reasonable TLS noise estimation, improved capabilities of TLS for modelling fine features of an irregular rock surface, and indicated the surface scale that can be reliably extracted from the TLS data.
TLS POINT CLOUD REGISTRATION BASED ON ICP ALGORITHM USING POINT QUALITY
Authors: Date H. , Wakisaka E. , Moribe Y. , Kanai S.
Published: June 2019

Abstract. A simple extension method of the ICP algorithm using point quality based on the distance and incident angle is presented for improving the registration accuracy of TLS point clouds. First, using a structured point cloud representation, the distance and incident angle of each point are calculated. Then, quality measures for the distance and incident angle are defined, considering the relationship between them and the scanning errors. Finally, an extension method of the ICP algorithm by the weighting using the estimated point qualities is described. A method for efficient matching using point properties is also introduced. The proposed method is applied to two datasets of TLS point clouds of a small plant and an outdoor environment, and the effectiveness is evaluated by comparing registration errors with the ground truths obtained using the scan target and a total station.
BUILDING DETECTION FROM LIDAR DATA USING ENTROPY AND THE K-MEANS CONCEPT
Authors: dos Santos R. C. , Pessoa G. G. , Carrilho A. C. , Galo M.
Published: June 2019

Abstract. Information obtained from LiDAR data processing is considered in a variety of applications, among them urban planning. In this context, buildings play a substantial role, since a high percentage of the urban landscape is occupied by them. In the literature, many methodologies have been developed aiming at the detection of building using remote sensing data. The approaches can be developed by applying different ideas: regularity of cluster boundary, plane fitting, radiometric data and also in geometric attribute derived from LiDAR. This paper proposes a method of building detection based on the use of the entropy concept and the K-means algorithm in which the training step is dispensed with. The experiments were performed considering two LiDAR datasets with different densities (12.5 pts/m2 and 4 pts/m2). Visual and qualitative analysis enabled verification of the potential of the proposed method, which presented satisfactory results for both datasets.
ORTHOGRAPHIC REFLECTANCE IMAGE FOR PLANAR TARGET LOCALIZATION IN LOW DENSITY TLS POINT CLOUDS
Authors: Guo D. , Yu D. , Liang Y. , Feng C.
Published: June 2019

Abstract. Point cloud registration is important and essential task for terrestrial laser scanning applications. Point clouds acquired at different positions exhibit significant variation in point density. Most registration methods implicitly assume dense and uniform distributed point clouds, which is hardly the case in large-scale surveying. The accuracy and robustness of feature extraction are greatly influenced by the point density, which undermines the feature-based registration methods. We show that the accuracy and robustness of target localization dramatically decline with decreasing point density. A methodology for localization of artificial planar targets in low density point clouds is presented. An orthographic image of the target is firstly generated and the potential position of the target center is interactively selected. Then the 3D position of the target center is estimated by a non-linear least squares adjustment. The presented methodology enables millimeter level accuracy of target localization in point clouds with 30mm sample interval. The robustness and effectiveness of the methodology is demonstrated by the experimental results.
Statistical post-processing of ensemble forecasts of the height of new snow
Authors: Nousu Jari-Pekka , Lafaysse Matthieu , Vernay Matthieu , Bellier Joseph , Evin Guillaume , Joly Bruno
Published: June 2019

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, roads viability, ski resorts management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool is refining the elevation resolution, and the Crocus snowpack model is representing the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on Nonhomogeneous Regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24-hour HN in French Alps and Pyrenees. The calibration is tested at the station-scale and the massif-scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.
The long-term variability of extreme sea levels in the German Bight
Authors: Lang Andreas , Mikolajewicz Uwe
Published: June 2019

Abstract. Extreme high sea levels (ESLs) caused by storm floods constitute a major hazard for coastal regions. We here quantify their long-term variability in the southern German Bight using simulations covering the last 1000 years. To this end, global earth system model simulations from the PMIP3 past1000 project are dynamically scaled down with a regionally coupled climate system model focusing on the North Sea. This approach provides an unprecedented long high-resolution data record that can extend the knowledge of ESL variability based on observations, and allows for the identification of associated large-scale forcing mechanisms in the climate system. While the statistics of simulated ESLs compare well with observations from the tide gauge record at Cuxhaven, we find that simulated ESLs show large variations on interannual to centennial timescales without preferred oscillation periods. As a result of this high internal variability, ESL variations appear to a large extent decoupled from those of the background sea level, and mask any potential signals from solar or volcanic forcing.
Comparison with large-scale climate variability shows that periods of high ESL are associated with a sea level pressure dipole between northeastern Scandinavia and the Gulf of Biscay.
While this large-scale circulation regime applies to enhanced ESL in the wider region, it differs from the North Atlantic Oscillation pattern that has often been linked to periods of elevated background sea level. The high internal variability with large multidecadal to centennial variations emphasizes the inherent uncertainties related to traditional extreme value estimates based on short data subsets, which fail to account for such long-term variations. We conclude that ESL variations as well as existing estimates of future changes are likely to be dominated by internal variability rather than climate change signals. Thus, larger ensemble simulations will be required to assess future flood risks.
Can wave coupling improve operational regional ocean forecasts for the north-west European Shelf?
Authors: Lewis Huw W. , Castillo Sanchez Juan Manuel , Siddorn John , King Robert R. , Tonani Marina , Saulter Andrew , Sykes Peter , Pequignet Anne-Christine , Weedon Graham P. , Palmer Tamzin , Staneva Joanna , Bricheno Lucy
Published: June 2019

Abstract. Operational ocean forecasts are typically produced
by modelling systems run using a forced mode approach. The evolution of the
ocean state is not directly influenced by surface waves, and the ocean
dynamics are driven by an external source of meteorological data which are
independent of the ocean state. Model coupling provides one approach to
increase the extent to which ocean forecast systems can represent the
interactions and feedbacks between ocean, waves, and the atmosphere seen in
nature. This paper demonstrates the impact of improving how the effect of
waves on the momentum exchange across the ocean–atmosphere interface is
represented through ocean–wave coupling on the performance of an operational
regional ocean prediction system. This study focuses on the eddy-resolving
(1.5 km resolution) Atlantic Margin Model (AMM15) ocean model configuration
for the north-west European Shelf (NWS) region. A series of 2-year duration forecast trials of the Copernicus Marine
Environment Monitoring Service (CMEMS) north-west European Shelf regional ocean
prediction system are analysed. The impact of including ocean–wave feedbacks
via dynamic coupling on the simulated ocean is discussed. The main
interactions included are the modification of surface stress by wave growth
and dissipation, Stokes–Coriolis forcing, and wave-height-dependent ocean
surface roughness. Given the relevance to operational forecasting, trials
with and without ocean data assimilation are considered. Summary forecast metrics demonstrate that the ocean–wave coupled system is a
viable evolution for future operational implementation. When results are
considered in more depth, wave coupling was found to result in an annual
cycle of relatively warmer winter and cooler summer sea surface temperatures
for seasonally stratified regions of the NWS. This is driven by enhanced
mixing due to waves, and a deepening of the ocean mixed layer during summer.
The impact of wave coupling is shown to be reduced within the mixed layer
with assimilation of ocean observations. Evaluation of salinity and ocean
currents against profile measurements in the German Bight demonstrates
improved simulation with wave coupling relative to control simulations.
Further, evidence is provided of improvement to simulation of extremes of
sea surface height anomalies relative to coastal tide gauges.
Subglacial hydrological control on flow of an Antarctic Peninsula palaeo-ice stream
Authors: Larter Robert D. , Hogan Kelly A. , Hillenbrand Claus-Dieter , Smith James A. , Batchelor Christine L. , Cartigny Matthieu , Tate Alex J. , Kirkham James D. , Roseby Zoë A. , Kuhn Gerhard , Graham Alastair G. C. , Dowdeswell Julian A.
Published: June 2019

Abstract. Basal hydrological systems play an important role in controlling
the dynamic behaviour of ice streams. Data showing their morphology and
relationship to geological substrates beneath modern ice streams are,
however, sparse and difficult to collect. We present new multibeam
bathymetry data that make the Anvers-Hugo Trough west of the Antarctic
Peninsula the most completely surveyed palaeo-ice stream pathway in
Antarctica. The data reveal a diverse range of landforms, including
streamlined features where there was fast flow in the palaeo-ice stream,
channels eroded by flow of subglacial water, and compelling evidence of
palaeo-ice stream shear margin locations. We interpret landforms as
indicating that subglacial water availability played an important role in
facilitating ice stream flow and controlling shear margin positions. Water
was likely supplied to the ice stream bed episodically as a result of
outbursts from a subglacial lake located in the Palmer Deep basin on the
inner continental shelf. These interpretations have implications for
controls on the onset of fast ice flow, the dynamic behaviour of palaeo-ice
streams on the Antarctic continental shelf, and potentially also for
behaviour of modern ice streams.
Alt-right pipeline: Individual journeys to extremism online
Authors: Munn Luke
Published: June 2019

The rise of the alt-right as a potent and sometimes violent political force has been well documented. Yet the journey of an individual towards upholding these ideologies is less well understood. Alt-righters are not instantly converted, but rather incrementally nudged along a particular medial pathway. Drawing on video testimonies, chat logs, and other studies, this paper explores the interaction between this alt-right “pipeline” and the psyche of a user. It suggests three overlapping cognitive phases that occur within this journey: normalization, acclimation, and dehumanization. Finally, the article examines the individual who has reached the end of this journey, an extremist who nevertheless remains largely unregistered within traditional terrorist classifications.
Testing popular news discourse on the “echo chamber” effect: Does political polarisation occur among those relying on social media as their primary politics news source?
Authors: Nguyen An , Vu Hong Tien
Published: June 2019

Since 2016, online social networks (OSNs), especially their “big data” algorithms, have been intensively blamed in popular news discourse for acting as echo chambers. These chambers entrap like-minded voters in closed ideological circles that cause serious damage to democratic processes. This study examines this “echo chamber” argument through the rather divisive case of EU politics among EU citizens. Based on an exploratory secondary analysis of the Eurobarometer 86.2 survey dataset, we investigate whether the reliance on OSNs as a primary EU political news source can lead people to more polarisation in EU-related political beliefs and attitudes than a reliance on traditional media. We found little evidence for this polarisation, lending credence to a rejection of social media’s “echo chamber” effect.
The monitoring of biogenic amines in the raw food
Authors: Pleva Pavel , Berčí­ková Lucie , Čechová Erika , Bartošek Petr , Buňková Leona
Published: June 2019

The aim of this work was to evaluate microbial quality and the presence of biogenic amines in raw bars. This study was focused on microbiological research in order to determine the presence of selected indicator groups of microorganisms depending on the composition of raw food. Identification of microorganisms was carried out by MALDI-TOF MS. In the second part of the experiment, biogenic amines and polyamines were analyzed using high performance liquid chromatography with UV/VIS detection. An increased incidence of mold has been reported in the samples, which is associated with a risk of mycotoxin production. After identifying microorganisms, it was found out that genera Micrococcus, Bacillus and Staphylococcus were the most represented. The highest concentration of biogenic amines (tyramine 42.2 ±4.8 mg.kg-1; putrescine 54.0 ±2.9 mg.kg-1) was found in a sample containing the vegetable component. The average concentration of biogenic amines in the tested raw bars was <30 mg.kg-1 and therefore they do not pose a serious health hazard to a consumer.
Is edible insect as a novel food digestible?
Authors: Adámek Martin , Mlček Jiří­ , Adámková Anna , Borkovcová Marie , Bednářová Martina , Jurí­ková Tünde , Musilová Zuzana , Faměra Oldřich
Published: June 2019

This work deals with the digestibility of a selected species of edible insect - mealworm (larvae) as novel food in dependency on its culinary treatment. The aim of this work was to find suitable thermic culinary treatment of mealworm larvae considering its optimum digestibility by human. The digestibility of materials from whole insect and extracted nitrogenous substances was determined using three different culinary treatments - without culinary treatment (freshly killed), dried insect and roasted insect. The digestibility was determined by gravimetric in vitro method using pepsin and pancreatin enzymes and their combination. The total nitrogen content of the insect samples was determined by the Kjeldahl method. The digestibility of the whole homogenized larvae using the combination of pepsin and pancreatin enzymes, thus simulating human digestion in-vitro, ranged from 81% for roasted specimens to 91.5% for culinary unprocessed insect. Similarly, the digestibility of nitrogenous substances of homogenized insect samples using this combination of enzymes ranged from 24.2% for roasted specimens to 80.2% for culinary unprocessed samples. The work showed the dependence of the digestibility of the mealworm larvae on the culinary treatment - the increasing heat load of the sample reduced the digestibility. Furthermore, it proved the effect of the digestive enzyme on the digestibility of the insect sample.