bluuebunny/crossref_metadata_2025 huggingface dataset:
A comparative analysis of waste heat recovery systems in vehicles and their viability in real-world applications
Authors: Op de Veigh Jaiden , Glynatsis Nick , Gurung Pasang , Wang Chengmin
Published: June 2019

In motor vehicles, an average of 60-70% of the overall fuel energy is dissipated primarily through the heated exhaust gases and engine coolant, which accounts for 90% of an engine’s thermal output. The fuel efficiency and environment impact of vehicles can therefore be improved by implementation of systems designed to recover this wasted energy. This meta- study explores the current methods for waste heat recovery (WHR) currently in production and research phases. A comparison is also made between the thermodynamic viability of each proposed system, from which future strategies to maximise the efficiency of WHR systems can be obtained. These include the use of the organic Rankine cycle (ORC), thermoelectric generators (TEG), and regenerative braking. The purpose of this paper is to analyse the current state of research for waste heat recovery in vehicles and therefore provide a basis for further research and investigation. The results indicate a promising future for further study of ORCs in the field of WHR for internal combustion engines (ICE) in vehicles. This is due to the various design opportunities that ORCs offer, including multiple loop configurations, different working fluids and integration of thermal energy storage devices. Current research for TEGs indicate a high cost to efficiency ratio for the materials required for production, meaning that TEGs are not as viable of a solution for WHR in vehicles relative to ORCs. This paper concludes that a fuel savings of 8-19% can be achieved through the integration of multiple energy recovery systems.

Keywords: Waste Heat Recovery; Vehicles; Thermodynamics; Organic Rankine Cycle; Thermoelectric Generators; Regenerative Braking; Exergy
Genetic diversity of different breeds of Kazakh sheep using microsatellite analysis
Authors: Dossybayev Kairat , Orazymbetova Zarina , Mussayeva Aizhan , Saitou Naruya , Zhapbasov Rakhymbek , Makhatov Bolathan , Bekmanov Bakytzhan
Published: June 2019

Abstract. A total of 75 individuals from five sheep populations in Kazakhstan were investigated based on 12 STR (short tandem repeat, also known as microsatellite) markers in order to study their genetic structure and phylogenetic relationship based on genetic distances. These sheep had a high level of genetic diversity. In total, 163 alleles were found in all the populations using 12 microsatellite loci. The mean number of alleles, effective number of alleles, and polymorphism information content (PIC) values per loci were 13.4, 5.9, and 0.78, respectively. Comparing the allelic diversity between the populations, the highest genetic diversity was observed in the Edilbay-1 sheep breed (8.333±0.644), and the lowest parameter was for Kazakh Arkhar-Merino (7.083±0.633). In all populations, there is a deficiency of heterozygosity. The largest genetic diversity was found in loci INRA023 and CSRD247 with 16 alleles, and the smallest polymorphism was noted for the locus D5S2 with 8 alleles. The level of observed heterozygosity was in the range 0.678±0.051 for Kazakh Arkhar-Merino and 0.767±0.047 for Kazakh fat-tailed coarse wool. The expected heterozygosity level range was from 0.702±0.033 for Kazakh Arkhar-Merino to 0.777±0.023 for Edilbay-1. When 12 microsatellite loci are compared, the OarFCB20 locus showed the highest level of genetic variability. Excess of heterozygosity was observed at three loci; MAF065, McM042, and OarFCB20. The highest genetic distance was observed between Kazakh Arkhar-Merino and Edilbay-1, whereas the genetic distance between Edilbay-1 and Edilbay-2 is the smallest using Nei's standard genetic distance. The Edilbay-1 sheep breed possesses the largest genetic diversity among these five populations.
NH3 emissions from large point sources derived from CrIS and IASI satellite observations
Authors: Dammers Enrico , McLinden Chris A. , Griffin Debora , Shephard Mark W. , Van Der Graaf Shelley , Lutsch Erik , Schaap Martijn , Gainairu-Matz Yonatan , Fioletov Vitali , Van Damme Martin , Whitburn Simon , Clarisse Lieven , Cady-Pereira Karen , Clerbaux Cathy , Coheur Pierre Francois , Erisman Jan Willem
Published: June 2019

Abstract. Ammonia (NH3) is an essential reactive nitrogen species in the biosphere and through its use in agriculture in the form of fertilizer important for sustaining human kind. The current emission levels however, are up to four times higher than in the previous century and continue to grow with uncertain consequences to human health and the environment. While NH3 at its current levels is a hazard to the environmental and human health the atmospheric budget is still highly uncertain, which is a product of an overall lack of measurements. The capability to measure NH3 with satellites has opened up new ways to study the atmospheric NH3 budget. In this study we present the first estimates of NH3 emissions, lifetimes, and plume widths from large (> ~ 5 kt/yr) agricultural and industrial point sources from CrIS satellite observations across the globe with a consistent methodology. The same methodology is also applied to the IASI (A and B) satellite observations and we show that the satellites typically provide comparable results that are within the uncertainty of the estimates. The computed NH3 lifetime for large point sources is on average 2.35 ± 1.16 hours. For the 249 sources with emission levels detectable by the CrIS satellite, there are currently 55 locations missing (or underestimated by more than an order of magnitude) from the current HTAPv2 emission inventory, and only 72 locations with emissions within a factor 2 compared to the inventories. We find a total of 5622 kt/yr, for the sources analyzed in this study, which is equivalent to a factor ~ 2.5 between the CrIS estimated and HTAPv2 emissions. Furthermore, the study shows that it is possible to accurately detect short and long-term changes in emissions, demonstrating the possibility of using satellite observed NH3 to constrain emission inventories.
Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
Authors: Western Luke M. , Sha Zhe , Rigby Matthew , Ganesan Anita L. , Manning Alistair J. , Stanley Kieran M. , O'Doherty Simon J. , Young Dickon , Rougier Jonathan
Published: June 2019

Abstract. We present a method to infer spatially and spatiotemporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial differential equation approach. The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. Combining an autoregressive temporal correlation and the Matérn field provides a full spatiotemporal correlation structure. We first demonstrate the method on a synthetic data example and follow this using a well-studied test case of inferring UK methane emissions from tall tower measurements of atmospheric mole fraction. Results from these two test cases show that this method can accurately estimate regional greenhouse gas emissions, accounting for spatiotemporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.
PREFACE – ISPRS GEOSPATIAL WEEK 2019
Authors: Vosselman G. , Oude Elberink S. J. , Yang M. Y.
Published: June 2019

Abstract. The ISPRS Geospatial Week 2019 is a combination of 13 workshops organised by 30 ISPRS Working Groups active in areas of interest of ISPRS. The Geospatial Week 2019 is held from 10–14 June 2019, and is convened by the University of Twente acting as local organiser. The Geospatial Week 2019 is the fourth edition, after Antalya Turkey in 2013, La Grande Motte France in 2015 and Wuhan China in 2017.The following 13 workshops provide excellent opportunities to discuss the latest developments in the fields of sensors, photogrammetry, remote sensing, and spatial information sciences: C3M&GBD – Collaborative Crowdsourced Cloud Mapping and Geospatial Big Data CHGCS – Cryosphere and Hydrosphere for Global Change Studies EuroCow-M3DMaN – Joint European Calibration and Orientation Workshop and Workshop onMulti-sensor systems for 3D Mapping and Navigation HyperMLPA – Hyperspectral Sensing meets Machine Learning and Pattern Analysis Indoor3D ISSDQ – International Symposium on Spatial Data Quality IWIDF – International Workshop on Image and Data Fusion Laser Scanning PRSM – Planetary Remote Sensing and Mapping SarCon – Advances in SAR: Constellations, Signal processing, and Applications Semantics3D – Semantic Scene Analysis and 3D Reconstruction from Images and ImageSequences SmartGeoApps – Advanced Geospatial Applications for Smart Cities and Regions UAV-g – Unmanned Aerial Vehicles in Geomatics Many of the workshops are part of well-established series of workshops convened in the past. They cover topics like UAV photogrammetry, laser scanning, spatial data quality, scene understanding, hyperspectral imaging, and crowd sourcing and collaborative mapping with applications ranging from indoor mapping and smart cities to global cryosphere and hydrosphere studies and planetary mapping.In total 143 full papers and 357 extended abstracts were submitted by authors from 63 countries. 1250 reviews have been delivered by 295 reviewers. A total of 81 full papers have been accepted for the volume IV-2/W5 of the International Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Another 289 papers are published in volume XLII-2/W13 of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.The editors would like to thank all contributing authors, reviewers and all workshop organizers for their role in preparing and organizing the Geospatial Week 2019. Thanks to their contributions, we can offer an excessive and varying collection in the Annals and the Archives.We hope you enjoy reading the proceedings.George Vosselman, Geospatial Week Director 2019, General Chair Sander Oude Elberink, Programme Chair Michael Ying Yang, Programme Chair
MLS POINT CLOUD SEGMENTATION BASED ON FEATURE POINTS OF SCANLINES
Authors: Honma R. , Date H. , Kanai S.
Published: June 2019

Abstract. Point clouds acquired using Mobile Laser Scanning (MLS) are applied to extract road information such as curb stones, road markings, and road side objects. In this paper, we present a scanline-based MLS point cloud segmentation method for various road and road side objects. First, end points of the scanline, jump edge points, and corner points are extracted as feature points. The feature points are then interpolated to accurately extract irregular parts consisting of irregularly distributed points such as vegetation. Next, using a point reduction method, additional feature points on a smooth surface are extracted for segmentation at the edges of the curb cut. Finally, points between the feature points are extracted as flat segments on the scanline, and continuing feature points are extracted as irregular segments on the scanline. Furthermore, these segments on the scanline are integrated as flat or irregular regions. In the extraction of the feature points, neighboring points based on the spatial distance are used to avoid being influenced by the difference in the point density. Based on experiments, the effectiveness of the proposed method was indicated based on an application to an MLS point cloud.
ACTIVE LEARNING TO EXTEND TRAINING DATA FOR LARGE AREA AIRBORNE LIDAR CLASSIFICATION
Authors: Li N. , Pfeifer N.
Published: June 2019

Abstract. Training dataset generation is a difficult and expensive task for LiDAR point classification, especially in the case of large area classification. We present a method to automatically extent a small set of training data by label propagation processing. The class labels could be correctly extended to their optimal neighbourhood, and the most informative points are selected and added into the training set. With the final extended training dataset, the overall (OA) classification could be increased by about 2%. We also show that this approach is stable regardless of the number of initial training points, and achieve better improvements especially stating with an extremely small initial training set.
AUTOMATIC CLASSIFICATION OF BRIDGES AND CONTINENTAL WATER BODIES FROM 3D POINT CLOUDS (AERIAL LIDAR)
Authors: Lorite Martínez S. , Moreno Jabato J. , Garrido Sáenz de Tejada J. M. , Rodríguez-Cuenca B.
Published: June 2019

Abstract. The use of algorithms for automatic classification of aerial laser scanner 3D Point Clouds is the main process that improves its thematic quality. The main objectives of using 3D Point Clouds are the description of the surface and the detection of objects. The aim of this proposal for bridge and water detection algorithms is to increase the range and accuracy of the classification parameters of these products obtained with LiDAR technologies. With this methodology, the Digital Elevation Models (DEM) quality is improved and they are obtained by automated models of bridges and hydrography.This paper describes a methodology to detect and classify bridges and continental water bodies in points using the properties of LiDAR technology such as radiometric and geometric variables implementing indexes like NDVI, NDWI or NFC. In addition, the Network of Roads and Hydrographic models in Spain are used to reduce the area of interest and errors. Part of the province of Teruel (Spain) has been used as study area.
DETECTION AND EXTRACTION OF WATER BOTTOM TOPOGRAPHY FROM LASERBATHYMETRY DATA BY USING FULL-WAVEFORM-STACKING TECHNIQUES
Authors: Mader D. , Richter K. , Westfeld P. , Weiß R. , Maas H.-G.
Published: June 2019

Abstract. Airborne LiDAR bathymetry allows an efficient and area-wide acquisition of water bottom points in shallow water areas. However, the measurement method is severely limited by water turbidity, impending a reliable detection of water bottom points at higher turbidity or in deeper water bodies. This leads to an incomplete acquisition of the water bottom topography. In this contribution, advanced processing methods are presented, which increase the penetration depth compared to the original processed data and enable a reliable extraction and detection of bottom points in deeper water bodies. The methodology is based on the analysis of correlated neighborhood information assuming a steady water bottom. The results confirm a significantly higher penetration depth with a high reliability of the additionally extracted water bottom points along with a larger coverage of the water bottom topography.
INDIVIDUAL TREE SPECIES CLASSIFICATION BASED ON TERRESTRIAL LASER SCANNING USING CURVATURE ESTIMATION AND CONVOLUTIONAL NEURAL NETWORK
Authors: Mizoguchi T. , Ishii A. , Nakamura H.
Published: June 2019

Abstract. In this paper, we propose a new method for specifying individual tree species based on depth and curvature image creation from point cloud captured by terrestrial laser scanner and Convolutional Neural Network (CNN). Given a point cloud of an individual tree, the proposed method first extracts the subset of points corresponding to a trunk at breast-height. Then branches and leaves are removed from the extracted points by RANSAC -based circle fitting, and the depth image is created by globally fitting a cubic polynomial surface to the remaining trunk points. Furthermore, principal curvatures are estimated at each scanned point by locally fitting a quadratic surface to its neighbouring points. Depth images clearly capture the bark texture involved by its split and tear-off, but its computation is unstable and may fail to acquire bark shape in the resulting images. In contrast, curvature estimation enables stable computation of surface concavity and convexity, and thus it can well represent local geometry of bark texture in the curvature images. In comparison to the depth image, the curvature image enables accurate classification for slanted trees with many branches and leaves. We also evaluated the effectiveness of a multi-modal approach for species classification in which depth and curvature images are analysed together using CNN and support vector machine. We verified the superior performance of our proposed method for point cloud of Japanese cedar and cypress trees.
AUTOMATIC DETECTION OF FOREST-ROAD DISTANCES TO IMPROVE CLEARING OPERATIONS IN ROAD MANAGEMENT
Authors: Novo A. , González-Jorge H. , Martínez-Sánchez J. , González-de Santos L. M. , Lorenzo H.
Published: June 2019

Abstract. There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation.The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30 m. The minimum distance calculation is 2 m, for the vegetation of 5 m height and a maximum of 60 m for vegetation 30 m height. The height of vegetation has a directly relation with the distance separation with the road.
AN INSTRUMENTAL BASIS FOR MULTISPECTRAL LIDAR WITH SPECTRALLY-RESOLVED DISTANCE MEASUREMENTS
Authors: Salido-Monzú D. , Wieser A.
Published: June 2019

Abstract. Hyperspectral solutions augment laser scanning technology with material probing capabilities by measuring target reflectance along with topography. We propose a novel instrumental basis that enables also spectrally-resolved distance measurement with sufficient sensitivity as to access dispersive phenomena on the reflecting target and along the propagation medium, further enhancing the material analysis capabilities of hyperspectral approaches. To this end we have extended distance metrology using intermode beat notes of a mode-locked femtosecond laser to cover flexibly selected spectral regions. The approach is based on an ultra-broadband source derived from a femtosecond laser via coherent supercontinuum generation. Herein we provide a first demonstration of the successful application of this technique to reflectorless measurements and thus its feasibility for multispectral LiDAR. We use a table-top experimental set-up to assess the approach by measuring distance, spectrally-resolved relative distance and reflectance to 5 different material samples on 5 multiplexed contiguous spectral bands of 50 nm in the range of 600 nm to 850 nm. We have achieved a distance measurement precision and accuracy better than 100 μm using integration times of about 30 ms, with promising perspectives regarding scalability to practical distances. The spectrally-resolved distance measurements also show repeatable material-dependent profiles with differences between materials up to several tenths of mm in some spectral bands. Combined with simultaneously acquired reflectance estimations, these profiles enable collecting additional target information, indicating the potential of the approach to enhance the material probing capabilities of prospective multispectral laser scanners.
AUTOMATIC DETECTION AND CHARACTERISATION OF POWER LINES AND THEIR SURROUNDINGS USING LIDAR DATA
Authors: Yermo M. , Martínez J. , Lorenzo O. G. , Vilariño D. L. , Cabaleiro J. C. , Pena T. F. , Rivera F. F.
Published: June 2019

Abstract. Light Detection and Ranging (LiDAR) is nowadays one of the most used tools to obtain geospatial data. In this paper, a method to detect and characterise power lines of both high and low voltage and their surroundings from 3D LiDAR point clouds exclusively is proposed. First, to identify points of the power lines a global search of candidate points is carried out based on the height of each point compared to its neighbours. Then, the Hough Transform (HT) is applied on the set of candidate points to extract the catenaries that belong to each power line, allowing the identification of each conductor individually. Finally, conductors located on the same power line are grouped, their geometric characteristics analysed, and the quantitative features of the surroundings are computed. A very high accuracy of power line classification is reached with these methods, while the computational time is optimised by efficient memory usage and parallel implementation of the code.
AN IMPROVED COHERENT POINT DRIFT METHOD FOR TLS POINT CLOUD REGISTRATION OF COMPLEX SCENES
Authors: Zang Y. , Lindenbergh R. C.
Published: June 2019

Abstract. Processing unorganized 3D point clouds is highly desirable, especially for the applications in complex scenes (such as: mountainous or vegetation areas). Registration is the precondition to obtain complete surface information of complex scenes. However, for complex environment, the automatic registration of TLS point clouds is still a challenging problem. In this research, we propose an automatic registration for TLS point clouds of complex scenes based on coherent point drift (CPD) algorithm combined with a robust covariance descriptor. Out method consists of three steps: the construction of the covariance descriptor, uniform sampling of point clouds, and CPD optimization procedures based on Expectation-Maximization (EM algorithm). In the first step, we calculate a feature vector to construct a covariance matrix for each point based on the estimated normal vectors. In the subsequent step, to ensure efficiency, we use uniform sampling to obtain a small point set from the original TLS data. Finally, we form an objective function combining the geometric information described by the proposed descriptor, and optimize the transformation iteratively by maximizing the likelihood function. The experimental results on the TLS datasets of various scenes demonstrate the reliability and efficiency of the proposed method. Especially for complex environments with disordered vegetation or point density variations, this method can be much more efficient than original CPD algorithm.
AUTOMATED VISIBILITY FIELD EVALUATION OF TRAFFIC SIGN BASED ON 3D LIDAR POINT CLOUDS
Authors: Zhang S. , Wang C. , Cheng M. , Li J.
Published: June 2019

Abstract. Maintaining high visibility of traffic signs is very important for traffic safety. Manual inspection and removal of occlusion in front of traffic signs is one of the daily tasks of the traffic management department. This paper presents a method that can automatically detect the occlusion and continuously quantitative estimate the visibility of traffic sign cover all the road surface based on Mobile Laser Scanning (MLS) systems. The concept of traffic sign’s visibility field is proposed in this paper. One of important innovation of this paper is that we use retinal imaging area to evaluate the visibility of a traffic sign. And this makes our method is in line with human vision. To validate the reasonable and accuracy of our method, we use the 2D and 3D registration technology to observe the consistence of the occlusion ratio in point clouds with it in photo. Experiment of implementation on large scale traffic environments show that our method is feasible and efficient.
HUMAN DETECTION BASED ON A SEQUENCE OF THERMAL IMAGES USING DEEP LEARNING
Authors: Wang X. , Hosseinyalamdary S.
Published: June 2019

Abstract. Human detection has been playing an increasingly important role in many fields in recent years. Human detection is a still challenging task because, for the group of people, each individual has his unique appearance and, body shape.Compared with the traditional method, the deep learning neural network has the advantages of shorter computing time, higher accuracy and easier operation. Therefore, deep learning method has been widely used in object detection. The current state of art in human detection is RetinaNet. Among all the deep learning approaches, RetinaNet gives the highest accuracy of human detection (Lin, Goyal, Girshick, He, & Piotr Dollar, 2018).The temporal component of video provides additional and significant clues as compared to the static image. In this paper, the temporal relationship of the images is utilized to improve the accuracy of human detection. Compared to using only an image, the accuracy of human detection is 21.4% higher when a sequence of images is applied.
FULLY CONVOLUTIONAL NETWORKS FOR STREET FURNITURE IDENTIFICATION IN PANORAMA IMAGES
Authors: Ao Y. , Wang J. , Zhou M. , Lindenbergh R. C. , Yang M. Y.
Published: June 2019

Abstract. Panoramic images are widely used in many scenes, especially in virtual reality and street view capture. However, they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images. This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks (FCN). FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction. In this study, we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data. The results show that in both pre-trained model and fine-tuning, transformed images have better prediction results than panoramic images.
A MODIFIED THREE-DIMENSIONAL GRAY-LEVEL CO-OCCURRENCE MATRIX FOR IMAGE CLASSIFICATION WITH DIGITAL SURFACE MODEL
Authors: Yan L. , Xia W.
Published: June 2019

Abstract. 2D texture cannot reflect the 3D object’s texture because it only considers the intensity distribution in the 2D image region but int real world the intensities of objects are distributed in 3D surface. This paper proposes a modified three-dimensional gray-level co-occurrence matrix (3D-GLCM) which is first introduced to process volumetric data but cannot be used directly to spectral images with digital surface model because of the data sparsity of the direction perpendicular to the image plane. Spectral and geometric features combined with no texture, 2D-GLCM and 3D-GLCM were put into random forest for comparing using ISPRS 2D semantic labelling challenge dataset, and the overall accuracy of the combination containing 3D GLCM improved by 2.4% and 1.3% compared to the combinations without textures or with 2D-GLCM correspondingly.
BUILDING SEGMENTATION FROM AIRBORNE VHR IMAGES USING MASK R-CNN
Authors: Zhou K. , Chen Y. , Smal I. , Lindenbergh R.
Published: June 2019

Abstract. Up-to-date 3D building models are important for many applications. Airborne very high resolution (VHR) images often acquired annually give an opportunity to create an up-to-date 3D model. Building segmentation is often the first and utmost step. Convolutional neural networks (CNNs) draw lots of attention in interpreting VHR images as they can learn very effective features for very complex scenes. This paper employs Mask R-CNN to address two problems in building segmentation: detecting different scales of building and segmenting buildings to have accurately segmented edges. Mask R-CNN starts from feature pyramid network (FPN) to create different scales of semantically rich features. FPN is integrated with region proposal network (RPN) to generate objects with various scales with the corresponding optimal scale of features. The features with high and low levels of information are further used for better object classification of small objects and for mask prediction of edges. The method is tested on ISPRS benchmark dataset by comparing results with the fully convolutional networks (FCN), which merge high and low level features by a skip-layer to create a single feature for semantic segmentation. The results show that Mask R-CNN outperforms FCN with around 15% in detecting objects, especially in detecting small objects. Moreover, Mask R-CNN has much better results in edge region than FCN. The results also show that choosing the range of anchor scales in Mask R-CNN is a critical factor in segmenting different scale of objects. This paper provides an insight into how a good anchor scale for different dataset should be chosen.
TOWARDS A HIGH-RESOLUTION DRONE-BASED 3D MAPPING DATASET TO OPTIMISE FLOOD HAZARD MODELLING
Authors: Backes D. , Schumann G. , Teferle F. N. , Boehm J.
Published: June 2019

Abstract. The occurrence of urban flooding following strong rainfall events may increase as a result of climate change. Urban expansion, aging infrastructure and an increasing number of impervious surfaces are further exacerbating flooding. To increase resilience and support flood mitigation, bespoke accurate flood modelling and reliable prediction is required. However, flooding in urban areas is most challenging. State-of-the-art flood inundation modelling is still often based on relatively low-resolution 2.5 D bare earth models with 2–5 m GSD. Current systems suffer from a lack of precise input data and numerical instabilities and lack of other important data, such as drainage networks. Especially, the quality and resolution of the topographic input data represents a major source of uncertainty in urban flood modelling. A benchmark study is needed that defines the accuracy requirements for highly detailed urban flood modelling and to improve our understanding of important threshold processes and limitations of current methods and 3D mapping data alike.This paper presents the first steps in establishing a new, innovative multiscale data set suitable to benchmark urban flood modelling. The final data set will consist of high-resolution 3D mapping data acquired from different airborne platforms, focusing on the use of drones (optical and LiDAR). The case study includes residential as well as rural areas in Dudelange/Luxembourg, which have been prone to localized flash flooding following strong rainfall events in recent years. The project also represents a cross disciplinary collaboration between the geospatial and flood modelling community. In this paper, we introduce the first steps to build up a new benchmark data set together with some initial flood modelling results. More detailed investigations will follow in the next phases of this project.
SURFACE FLOW VELOCITY MEASUREMENTS FROM UAV-BASED VIDEOS
Authors: Brauneck J. , Gattung T. , Jüpner R.
Published: June 2019

Abstract. Most measuring methods for determining the volumetric flow rate or surface flow velocity have in common that they cannot be safely used under extreme outflow conditions. Especially in catastrophic situations, it is of particular interest to determine the amount of water that flows into the hinterland as precisely as possible in order to improve hydrodynamic models. Faulty assumptions lead to misleading calculations and may result in preventable casualties. As technical improvements throughout the last decade facilitated the widespread utilization of unmanned aerial vehicles (UAV) or remotely piloted aircraft systems (RPAS), these systems are now capable to collect and transmit precise information from remote areas to task forces immediately. The usage of a UAV is possible with minimal preparation at almost every place and is suitable for improving the database for a quick assessment of the status during a catastrophic event.In this work, the determination of surface flow velocity using unmanned aerial vehicles (UAV) and floating optical tracers is evaluated. It is also discussed, to what extent numerical methods are able to efficiently undistort and correct this data. Precision analysis of video data from field investigations was performed with R using three different approaches that calculated the true velocity of the floating objects. The results indicate similar degrees of precision for both advanced methods but calculating an ortho-corrected video is a timeconsuming process not suitable for nearly real-time applications.
APPLICATION OF SELF-ORGANIZING MAP ON FLIGHT DATA ANALYSIS FOR QUADCOPTER HEALTH DIAGNOSIS SYSTEM
Authors: Cheng D.-L. , Lai W.-H.
Published: June 2019

Abstract. The UAS fault problem has led to many potential risk factors behind its rapid development in recent years. Therefore, the diagnosis of UAS health status is still an important issue. This study adopted the SOM machine learning method which is an unsupervised clustering method to establish a model for diagnosing the health status of quadcopter. Take the vibration features of three flight states (undamaged, motor mount loose, unbalanced broken propeller). Through those training data the model can cluster different vibration pattern of fault situation. It not only can classify the failure status with 99% accuracy but also can provide pre-failure indicators.
ASSESSMENT OF INFLUENCE OF IMAGE PROCESSING ON FULLY AUTOMATIC UAV PHOTOGRAMMETRY
Authors: Feng C. , Yu D. , Liang Y. , Guo D. , Wang Q. , Cui X.
Published: June 2019

Abstract. Nowadays UAVs have been widely used for large scale surveying and mapping. Compared with traditional surveying techniques, UAV photogrammetry is more convenient, cost-effective, and responsive. Aerial images, Position and Orientation System (POS) observations and coordinates of ground control points are usually acquired during a surveying campaign. Aerial images are the data source of feature point extraction, dense matching and ortho-rectification procedures. The quality of the images is one of the most important factors that influence the accuracy and efficiency of UAV photogrammetry. Image processing techniques including image enhancement, image downsampling and image compression are usually used to improve the image quality as well as the efficiency and effectiveness of the photogrammetric data processing. However, all of these image processing techniques bring in uncertainties to the UAV photogrammetry. In this work, the influences of the aforementioned image processing techniques on the accuracy of the automatic UAV photogrammetry are investigated. The automatic photogrammetric data processing mainly consists of image matching, relative orientation, absolute orientation, dense matching, DSM interpolation and orthomosaicing. The results of the experiments show that the influences of the image processing techniques on the accuracy of automatic UAV photogrammetry are insignificant. The image orientation and surface reconstruction accuracies of the original and the enhanced images are comparable. The feature points extraction and image matching procedures are greatly influenced by image downsampling. The accuracies of the image orientations are not influenced by image downsampling and image compression at all.
DETERMINING MORPHOMETRIC PROPERTIES OF RADIATA PINE USING LONG WAVE INFRARED SENSING AND BIOLOGICALLY-INSPIRED VISION
Authors: Finn A. , Brinkworth R. , Griffiths D. , Peters S.
Published: June 2019

Abstract. A miniaturised, high resolution visible and long wave infrared (LWIR) sensor was carried onboard an unmanned aerial vehicle to observe sections of radiata pine forests. The raw irradiance measurements were temporally pre-processed using a biologically-inspired vision (BIV) model to allow information within and across the images to be normalised. This permitted a larger, denser, and more tailored set of key points within the 2D image stacks to be corresponded, thereby improving 3D reconstructions of individual trees derived using structure from motion (SfM). The BIV model comprises multiple layers of processing derived from measured or assumed responses of the photoreceptor cells in the hoverfly. Its pre-processing expands the range of input signal obtained from the LWIR sensor and enhances foreground-background contrast. Morphological image processing techniques were also applied to enhance key image features before structure from motion is applied. The result allows structural properties of individual trees to be characterised in terms of their potential volume and quality; and contrasted with the point clouds obtained from the visible imagery that only depicts the tree canopies.
U.PH.O AND MAGO: TWO USEFUL INSTRUMENTS IN SUPPORT OF PHOTOGRAMMETRIC UAV SURVEY
Authors: Gagliolo S. , Passoni D. , Federici B. , Ferrando I. , Sguerso D.
Published: June 2019

Abstract. In emergency and critical scenarios, the UAV could play a key role in accessing unreachable sites in a safe and rapid way, guaranteeing at the same time the necessary accuracy and precision of the survey. In this context, UAV survey campaigns have been performed by the authors in Norcia (Italy), hit by tragic seismic events in August and October 2016. The surveys were motivated by the artistic and historical value of monuments, the need to plan and design the restoring and retrofitting of buildings, and also to quantify and manage the ruins. Goal of such surveys was the description of the structures geometry with a centimetric precision and a high level of reliability. Recently, the authors have conceived two tools, U.Ph.O (Unmanned Photogrammetric Office) and MAGO (Adaptive Mesh for Orthophoto Generation), dedicated to the planning and restitution phases of the survey, respectively. U.Ph.O. and MAGO are here applied to two different buildings in Norcia, i.e. the Civic Tower of Norcia and San Salvatore Church of Campi di Norcia. The former is a standing-out structure, surrounded by the complex of the historical centre, while the latter is located in an isolated site in the countryside. These features make the survey planning and the orthophoto reconstruction completely different, mainly due to the different optimal shooting geometry and the presence or absence of obstructions.
MOSAICING FIDELITY GEOMETRICAL ASSESSMENT BASED ON SURF POINT CLASSIFICATION
Authors: Giudici R. , Courtrai L. , Lefevre S.
Published: June 2019

Abstract. The recent development of automated UAV imaging applications for geomatics is leading to an unprecedented rapid growth in mosaic and DEM images. Newer, more advanced algorithms are being extensively studied to fulfill constantly increasing requirements. Sequences of georeferenced images that cannot be shot at once are merged with ideally no geometrical distortion to allow for 3D reconstruction and orthophotography generation. If newer robust mosaicing algorithms are being developed to withstand UAV tighter real-time constraints, very little attention has been given to objectively assess their capacity to coherently combine different image geometries. Changes in FOV and illumination, oblique scenes and dynamic sequences featuring movement and object occlusion are not yet fully handled by state-of-the-art algorithms. Despite the large panel of existing applications featuring different quality constraints, our work focuses on the geomatics context, where the main requirement is the respect of geometrical proportions among images. Geometrically coherent mosaics and 3D reconstructions can only result from fully static, high-altitude orthogonal views. GCPs or well known geographical landmarks are employed with RTK-GPS for scene georeferencing. The overall fidelity is computed as the global variance of the distances between SGPs. However, very few ways seem to exist to measure the content coherence of large geographical scenes, interiors or unreferenced scenes. Distances are mostly expressed in a local coordinate system and GCPs are often unavailable. The main contribution of this paper consists in the definition of a formal metric for measuring local coordinate geometrical fidelity of mosaics issued of UAV image sequences, in real operating conditions. A Mosaicing Fidelity Assessment (MSF) index is therefore computed for every couple of consecutive images of a sequence. The metric generates an index based on the distances of SURF feature points extracted in the images and compares them to estimate geometrical changes transferred to the mosaic. The solution can detect perspective inaccuracies caused by residual registration errors. It can fulfill the requirements of most of imaging and geomatics applications and can be executed as nearly real-time due to its low computational complexity.
ORIENTATION OF UAV IMAGE BLOCKS BY SURFACE MATCHING
Authors: Gonçalves J. A. , Jordão N. , Pinhal A.
Published: June 2019

Abstract. Topographic maps are produced in Portugal by the Army Geospatial Data Centre, based on a geographical database collected from aerial photography in resolutions from 0.30 to 0.50 m. Each map sheet is revised with an update interval of 10 years or more. Many changes, such as new roads or power lines, would be possible to update with much higher frequency using UAV imagery. Although the nominal scale of the published paper map sheets is 1 : 25,000, the actual positional accuracy requirement is of 1 meter, which is compatible with larger scales. Exterior orientation parameters obtained by UAV navigation equipment does not have enough accuracy for that. This paper deals with a method to automatically improve the UAV exterior orientation, intended to be compatible with the positional accuracy standards of the geographic database. The method is based on matching a point cloud derived from UAV imagery, without GCPs, with a reference DSM obtained from conventional aerial photographs, which were oriented by standard aerial triangulation. Results allowed to improve the UAV data to a positional accuracy of 1 meter, making it compatible with the requirements established for the geographic database.
CROP ROW DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM
Authors: Hassanein M. , Khedr M. , El-Sheimy N.
Published: June 2019

Abstract. Precision Agriculture (PA) management systems are considered among the top ten revolutions in the agriculture industry during the last couple decades. Generally, the PA is a management system that aims to integrate different technologies as navigation and imagery systems to control the use of the agriculture industry inputs aiming to enhance the quality and quantity of its output, while preserving the surrounding environment from any harm that might be caused due to the use of these inputs. On the other hand, during the last decade, Unmanned Aerial Vehicles (UAVs) showed great potential to enhance the use of remote sensing and imagery sensors for different PA applications such as weed management, crop health monitoring, and crop row detection. UAV imagery systems are capable to fill the gap between aerial and terrestrial imagery systems and enhance the use of imagery systems and remote sensing for PA applications. One of the important PA applications that uses UAV imagery systems, and which drew lots of interest is the crop row detection, especially that such application is important for other applications such as weed detection and crop yield predication. This paper introduces a new crop row detection methodology using low-cost UAV RGB imagery system. The methodology has three main steps. First, the RGB images are converted into HSV color space and the Hue image are extracted. Then, different sections are generated with different orientation angles in the Hue images. For each section, using the PCA of the Hue values in the section, an analysis can be performed to evaluate the variances of the Hue values in the section. The crop row orientation angle is detected as the same orientation angle of the section that provides the minimum variances of Hue values. Finally, a scan line is generated over the Hue image with the same orientation angle of the crop rows. The scan line computes the average of the Hue values for each line in the Hue image similar to the detected crop row orientation. The generated values provide a graph full of peaks and valleys which represent the crop and soil rows. The proposed methodology was evaluated using different RGB images acquired by low-cost UAV for a Canola field. The images were taken at different flight heights and different dates. The achieved results proved the ability of the proposed methodology to detect the crop rows at different cases.
HIGH THROUGHPUT PHENOTYPING OF PHYSIOLOGICAL GROWTH DYNAMICS FROM UAS-BASED 3D MODELING IN SOYBEAN
Authors: Herrero-Huerta M. , Rainey K. M.
Published: June 2019

Abstract. Nowadays, an essential tool to improve the efficiency of crop genetics is automated, precise and cost-effective phenotyping of the plants. The aim of this study is to generate a methodology for high throughput phenotyping the physiological growth dynamics of soybeans by UAS-based 3D modelling. During the 2018 growing season, a soybean experiment was performed at the Agronomy Center for Research and Education (ACRE) in West-Lafayette (Indiana, USA). Periodic images were acquired by G9X Canon compact digital camera on board senseFly eBee. The study area is reconstructed in 3D by Image-based modelling. Algorithms and techniques were combined to analyse growth dynamics of the crop via height variations and to quantify biomass. Results provide practical information for the selection of phenotypes for breeding.