bluuebunny/crossref_metadata_2025 huggingface dataset:
EXPERIENCES IN CADASTRAL RESTITUTION AT A TOWN AFFECTED BY A NATURAL DISASTER. THE CASE OF VOLCAN, JUJUY PROVINCE, ARGENTINA
Authors: Gutiérrez Viñuales M. , Puch Sleive E. , Chalabe S.
Published: June 2019

Abstract. The use of UAVs (Unmanned Aerial Vehicles) as a data acquisition tool for cadastral surveying has been used for several years. The processing software and an adequate workflow make this set a powerful tool to obtain quality information quickly in order to take decisions.This work shows the experience of using this tool and the results achieved in an urban area included in the Quebrada de Humahuaca (included in the list of World Heritage of Humanity by UNESCO) that was affected by a landslide and flooding in January of 2017. A precise and fast action was required to begin the reconstruction works of the houses that had to be demolished due to their high degree of affectation. In addition, a general update of the Cadastral Records of the town was required, which were analyzed individually from the information collected for the preparation of the Cadastral Map, the analysis of the previous cadastral information and a set of surveys carried out in each property. It includes an initial assessment of the problem, the work flow adopted, the results achieved, and a discussion of the problems detected in each phase of the work.
DEVELOPMENT OF GPS SATELLITE VISIBILITY SIMULATION METHOD UNDER URBAN CANYON ENVIRONMENT
Authors: Kim D. , Youn J. , Kim T. , Kim G.
Published: June 2019

Abstract. When the UAV is flying in a lower area than the buildings in the urban environment, the GPS positioning accuracy of the aircraft reduced due to the blocked or reflected signals. Therefore, when the UAVs fly in this area, it should be able to provide a criterion that can give warning message to the UAV users such as stop the auto-flight mode or not rely too much on the position of the UAV represented on the monitor of GCS. In this study, the Elevation Mask Profile (EMP) generation algorithm proposed which calculates azimuth and elevation angle ranges blocked by obstacles at certain grid points by using DSM. It is a core algorithm for a GPS simulator developed that predict the blocked satellite signal by matching the EMP calculated for each grid point with the GPS satellites' azimuth and elevation angle information at the grid point. According to the comparison of EMP and observed data, most of the results had correlation with obstacles nearby. We found some signal blocking areas were not determined as EMP due to the skyscrapers located out of the target area where DSM was constructed. To overcome this limitation, larger spatial data than the DSM that used in this study must be applied. Furthermore, additional studies about optimal search radius definition in EMP calculation procedure to acquire more accurate and standardized EMP are necessary.
MASS MOVEMENTS DETECTION FROM UAV IMAGES ANALYSIS
Authors: Morocho V. , España D. A. , Serrano A. C. , Achig R. , Crompvoets J.
Published: June 2019

Abstract. Biophysical conditions, the lack of governance and the weak applications of planning and territorial planning policies are aggravating factors of affections caused by natural disasters in Latin America. In Ecuador, landslides have caused not only material but also human losses, which shows the lack of immediate actions to avoid human settlements in risk areas or more controlled evacuations protocols, in case of early detection of risk areas due to this type of events. This article is part of an applied research project for the City of Cuenca allowing to take advantage of the information provided by citizens with a PPGIS. Furthermore, remote sensor images are analyzed, in order to identify in a semi-automated way, risk areas due to mass movements problems. Two approaches for semi-automated detecting areas where mass movements happen have been explored, i) Temporal images obtained by a LiDAR; and ii) Temporary images obtained by flights of an unmanned aerial vehicle, UAV. This paper present the procedure followed by comparison of image outcomes. The resulting analysis clearly presents the applicability of the two techniques thus allowing the determination of maps and early detection of landslides.
LANDSLIDE DEFORMATION MONITORING BY THREE-CAMERA IMAGING SYSTEM
Authors: Rau J. Y. , Jhan J. P. , Andaru R.
Published: June 2019

Abstract. Landslide deformation is a critical issue for WuWanZai as it will affect the road safety and cause transportation problem. Since the relief of this area is about 400 meters with an area of tens of hectares, we use an unmanned helicopter equipped with a three-camera imaging system to acquire high spatial resolution images in order to measure detail terrain variation. The unmanned helicopter can fly according to terrain slope to obtain 1–3 cm spatial resolution images. The acquired three-camera images are stitched into one perspective image in advance to construct a large format virtual image with a frame size of 34 mm by 78 mm and a FOV of 53° × 97°. Integrating ground control points that were measure by static GNSS continuous observation, we conduct aerial triangulation and dense point cloud generation by PhotoscanPro. We have acquired six dataset of UAV images since April 20, 2018. Then, we have conducted cloud-to-cloud distance calculation, DSM elevation difference calculation, ortho-image change analysis, photogrammetric points and GNSS stations displacement analysis, etc. In the end, from photogrammetric point displacement analysis, we have detected 1.6 meters displacement around the fourth curve of WuWanZai due to a heavy rainfall occurred at June 20. Based on the cloud-to-cloud distance analysis and DSM elevation difference results, we have observed more than 5 meters of height difference at the landslide area due to another heavy rainfall happened at Oct. 23–24. Experimental results demonstrate that by using the proposed UAV and three-camera imaging system can effectively detect landslide deformation in high accuracy.
AUTOMATIC CO-REGISTRATION OF AERIAL IMAGERY AND UNTEXTURED MODEL DATA UTILIZING AVERAGE SHADING GRADIENTS
Authors: Schmitz S. , Weinmann M. , Ruf B.
Published: June 2019

Abstract. The comparison of current image data with existing 3D model data of a scene provides an efficient method to keep models up to date. In order to transfer information between 2D and 3D data, a preliminary co-registration is necessary. In this paper, we present a concept to automatically co-register aerial imagery and untextured 3D model data. To refine a given initial camera pose, our algorithm computes dense correspondence fields using SIFT flow between gradient representations of the model and camera image, from which 2D–3D correspondences are obtained. These correspondences are then used in an iterative optimization scheme to refine the initial camera pose by minimizing the reprojection error. Since it is assumed that the model does not contain texture information, our algorithm is built up on an existing method based on Average Shading Gradients (ASG) to generate gradient images based on raw geometry information only. We apply our algorithm for the co-registering of aerial photographs to an untextured, noisy mesh model. We have investigated different magnitudes of input error and show that the proposed approach can reduce the final reprojection error to a minimum of 1.27 ± 0.54 pixels, which is less than 10% of its initial value. Furthermore, our evaluation shows that our approach outperforms the accuracy of a standard Iterative Closest Point (ICP) implementation.
AUTONOMOUS UAV-BASED 3D-RECONSTRUCTION OF STRUCTURES FOR AERIAL PHYSICAL INTERACTION
Authors: Sirmacek B. , Rashad R. , Radl P.
Published: June 2019

Abstract. We introduce a fully automated only path planning approach especially for drones. This novel method relies on usage of a stereo camera mounted at the bottom of a hexagonal drone for real-time point cloud reconstruction and localization. The real-time point cloud is analyzed in a software loop where the entropy of the point cloud and the surface normals are calculated. The low entropy positions (which indicate the 3D areas with less point density and less information) and the surface normals are used for calculating the next inspection point which can be targeted by the drone in order to enhance the point cloud best. Path planning to these automatically selected target points is done during the flight (quite real-time) and automatically. The initial experiments are performed on Gazebo simulation environment within the ROS system using realistic parameters of our real drone and real stereo camera.
PRECISE AERIAL IMAGE ORIENTATION USING SAR GROUND CONTROL POINTS FOR MAPPING OF URBAN LANDMARKS
Authors: Kurz F. , Krauß T. , Runge H. , Rosenbaum D. , d’Angelo P.
Published: June 2019

Abstract. Highly precise ground control points, which are globally available, can be derived from the SAR satellite TerraSAR-X. This opens up many new applications like for example the precise aerial image orientation. In this paper, we propose a method for precise aerial image orientation using spaceborne geodetic Synthetic Aperture Radar Ground Control Points (SAR-GCPs). The precisely oriented aerial imagery can then be used e.g. for mapping of urban landmarks, which support the ego-positioning of autonomous cars. The method for precise image orientation was validated based on two aerial image data sets. SAR-GCPs were measured in images, then the image orientation has been improved by a bundle-adjustment. Results based on check points show, that the accuracy of the image orientation is better than 5 cm in X and Y coordinates.
QUANTIFYING LODGING PERCENTAGE, LODGING DEVELOPMENT AND LODGING SEVERITY USING A UAV-BASED CANOPY HEIGHT MODEL
Authors: Wilke N. , Siegmann B. , Frimpong F. , Muller O. , Klingbeil L. , Rascher U.
Published: June 2019

Abstract. Unmanned Aerial Vehicles (UAVs) are increasingly used, and open new opportunities, in agriculture and phenotyping because of the flexible data acquisition. In this study the potential of ultra-high spatially resolved UAV image data was investigated to quantify lodging percentage, lodging development and lodging severity of barley using Structure from Motion techniques. The term lodging is defined as the permanent displacement of a plant from the upright position. Traditionally lodging quantification is based on observations that need, and vary with observers in the field. An objective threshold approach was proposed in this study to improve the accuracy in lodging determination. Across breeding trials, manual reference measurements and UAV based lodging percentage showed a very high correlation (R2 = 0.96). In addition, the multi-temporal lodging percentage development was used to estimate the recovery rate and to determine the influence of different lodging events. Based on the parameter lodging percentage an approach was developed that allowed the assessment of lodging severity, an information that is important to estimate the yield impairment. Lodging severity can be used for insurance applications, precision farming and breeder research. This trait, together with differentiated recovery are novel traits next to lodging severity that will aid the selection for genetic lines.
UAVS ENHANCED NAVIGATION IN OUTDOOR GNSS DENIED ENVIRONMENT USING UWB AND MONOCULAR CAMERA SYSTEMS
Authors: Zahran S. , Masiero A. , Mostafa M. M. , Moussa A. M. , Vettore A. , El-Sheimy N.
Published: June 2019

Abstract. The demand for small Unmanned Aerial Vehicles (UAVs) is massively increasing these days, due to the wide variety of applications utilizing such vehicles to perform tasks that may be dangerous or just to save time, effort, or cost. Small UAVs navigation system mainly depends on the integration between Global Navigation Satellite Systems (GNSS) and Inertial Measurement Unit (INS) to estimate the Positions, Velocities, and Attitudes (PVT) of the vehicle. Without GNSS such UAVs cannot navigate for long periods of time depending on INS alone, as the low-cost INS typically exhibits massive accumulation of errors during GNSS absence. Given the importance of ensuring full operability of the UAVs even during GNSS signals unavailability, other sensors must be used to bound the INS errors and enhance the navigation system performance. This paper proposes an enhanced UAV navigation system based on integration between monocular camera, Ultra-Wideband (UWB) system, and INS. In addition to using variable EKF weighting scheme. The paper also investigates this integration in the case of low density of UWB anchors, to reduce the cost required for such UWB system infrastructure. A GoPro Camera and UWB rover were attached to the belly of a quadcopter, an on the shelf commercial drone (3DR Solo), during the experimental flight. The velocity of the vehicle is estimated with Optical Flow (OF) from camera successive images, while the range measurements between the UWB rover and the stationary UWB anchors, which were distributed on the field, were used to estimate UAV position.
FOSS4G DATE FOR DSM GENERATION: SENSITIVITY ANALYSIS OF THE SEMI-GLOBAL BLOCK MATCHING PARAMETERS
Authors: Lastilla L. , Ravanelli R. , Fratarcangeli F. , Di Rita M. , Nascetti A. , Crespi M.
Published: June 2019

Abstract. DATE (Digital Automatic Terrain Extractor) is a Free and Open Source Software for Geospatial (FOSS4G), which combines photogrammetric and computer vision algorithms in order to automatically generate DSMs from multi-view SAR and optical high resolution satellite imagery, following an iterative and pyramidal workflow in order to refine a coarse DSM used as reference. Consequently, DATE is able to face both the issues of DSM generation and epipolar resampling of satellite imagery. The aim of this work is to evaluate DATE performance, by carrying out a sensitivity analysis based on the dense matching parameters. In particular, DATE implements the Semi-Global Block Matching (SGBM) algorithm, a modified version of Semi-Global Matching method: thus, the sensitivity analysis aims at assessing how SGBM parameters – namely, the difference between maximum and minimum disparity (ndisparities), the minimum disparity value (minimumDisp) and the matched block size (SADWindowSize) – affect the efficiency of the disparity map computation and the final DSM accuracy. The analysis focuses on the case study of Trento and of the Adige Valley, which was chosen due to its geomorphological heterogeneity and complexity, allowing to perform an accuracy evaluation on four tiles, characterized by specific roughness frequencies and morphologies (thus having different effects on disparity variations). Several practical indications on the optimal and critical parameter combinations were retrieved; in addition to this, this work highlighted the most influential parameters both in terms of accuracy (minimumDisp) and computation time (ndisparities), paving the way to further principal component analyses. Finally, the obtained results showed no clear relationship between the area morphology and the solution structure.
SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION
Authors: Chen Y. , Ming D.
Published: June 2019

Abstract. In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image. Per-pixel classification method based on CNN (Per-pixel CNN) achieved higher accuracy with the help of high-level features, however, this method still has limitations. Even though per-superpixel classification method based on CNN (Per-superpixel CNN) overcome the limitations of per-pixel CNN, classification accuracy of complex urban is easily influenced by scale effect. To solve this issue, superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) method is proposed. Besides, this paper proposes a novel spatial statistics based method to estimate applicable scale parameter of per-superpixel CNN. Experiments using proposed method were performed on Digital Orthophoto Quarer Quad (DOQQ) images in urban and suburban area. Classification results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent.
A FRAMEWORK FOR ESTIMATING REPRESENTATIVE AREA OF A GROUND SAMPLE USING REMOTE SENSING
Authors: Deshpande P. J. , Sure A. , Dikshit O. , Tripathi S.
Published: June 2019

Abstract. Modelling hydro-meteorological variables over land and atmosphere comprise of ground sampling at selected locations and predicting over the other locations. Remote sensing data can be effectively used to improve predictions by prudently choosing sampling locations of variables co-dependent on the prediction variable. This paper presents a framework for estimating the representative area of a ground sample and thereby determining the number of samples required for prediction with a given level of uncertainty and spatial resolution. Application of the proposed framework for soil moisture as the prediction variable is presented using Google Earth Engine and Scikit-learn libraries implemented in Python 3 programming language.
A COMPARATIVE ANALYSIS OF PLANETSCOPE AND SENTINEL SENTINEL-2 SPACE-BORNE SENSORS IN MAPPING STRIGA WEED USING GUIDED REGULARISED RANDOM FOREST CLASSIFICATION ENSEMBLE
Authors: Mudereri B. T. , Dube T. , Adel-Rahman E. M. , Niassy S. , Kimathi E. , Khan Z. , Landmann T.
Published: June 2019

Abstract. Weeds are one of the major restrictions to sustaining crop productivity. Weeds often outcompete crops for nutrients, soil moisture, solar radiation, space and provide platforms for breeding of pests and diseases. The ever-growing global food insecurity triggers the need for spatially explicit innovative geospatial technologies that can deliver timely detection of weeds within agro-ecological systems. This will help pinpoint maize fields to be prioritized for weed control. Satellite remote sensing offers incomparable opportunities for precision agriculture, ecological applications and vegetation characterisation, with vast socioeconomic benefits. This work compares and evaluates the strength of Sentinel-2 (S2) satellite with the constellation of Dove nanosatellites i.e. PlanetScope (PS) data in detecting and mapping Striga (Striga hermonthica) weed within intercropped maize fields in Rongo sub-county in western Kenya. We applied the S2 and PS derived spectral data and vegetation indices in mapping the Striga occurrence. Data analysis was implemented, using the Guided Regularised Random Forest (GRRF) classifier. Comparatively, Sentinel-2 demonstrated slightly lower Striga detection capacity than PlanetScope, with an overall accuracy of 88% and 92%, respectively. The results further showed that the VNIR (Blue, Green Red and NIR) and the Atmospheric resistance Vegetation Index (ARVI) were the most fundamental variables in detecting and mapping Striga presence in maize fields. Findings from this work demonstrate that Sentinel-2 data has the capability to provide spatial explicit near real-time field level Striga detection – a previously daunting task with broadband multispectral sensors.
STUDY ON SNOWMELT FLOOD DISASTER MODEL BASED ON REMOTE SENSING AND GIS
Authors: Qiao C. , Huang Q. Y. , Chen T. , Chen Y. M.
Published: June 2019

Abstract. In the context of global warming, the snowmelt flood events in the mountainous area of the middle and high latitudes are increasingly frequent and create severe casualties and property damages. Carrying out the prediction and risk assessment of the snowmelt flood is of great importance in the water resources management, the flood warning and prevention. Based on the remote sensing and GIS techniques, the relationships of the variables influencing the snowmelt flood such as the snow area, the snow depth, the air temperature, the precipitation, the land topography and land covers are analyzed and a prediction and damage assessment model for snowmelt floods is developed. This model analyzes and predicts the flood submerging range, flood depth, flood grade, and the damages of different underlying surfaces in the study area in a given time period based on the estimation of snowmelt amount, the snowmelt runoff, the direction and velocity of the flood. Then it was used to predict a snowmelt flood event in the Ertis River Basin in northern Xinjiang, China, during March and June, 2017 and to assess its damages including the damages of roads, transmission lines, settlements caused by the floods and the possible landslides using the hydrological and meteorological data, snow parameter data, DEM data and land use data. A comparison was made between the prediction results from this model and flood measurement and its disaster loss data, which suggests that this model performs well in predicting the strength and impact area of snowmelt flood and its damage assessment.
CA MARKOV MODELING OF LAND USE LAND COVER DYNAMICS AND SENSITIVITY ANALYSIS TO IDENTIFY SENSITIVE PARAMETER(S)
Authors: Surabuddin Mondal M. , Sharma N. , Kappas M. , Garg P. K.
Published: June 2019

Abstract. An attempt has been made to explore, evaluate and identify the sensitive parameter(s) of Cellular Automata Markov chain modeling to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India. For this purpose, land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. Sensitivity analysis has been carried out to identify the land use land cover parameter(s), which have the highest, lowest or intermediate influence on predicted results. The validity of the Cellular Automata Markov process for projecting future land use and cover changes in the study area calculates various Kappa Indices of Agreement (Kstandard) which indicate how well the comparison map agrees and disagrees with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The results shows that the land with or without scrub appeared to be most sensitive parameter as it has highest influences on predicted results of land use land cover of 2007. The second most sensitive parameter was lakes / reservoirs / ponds to predict land use land cover of 2007, followed by river, agricultural crop land, plantation, open land, marshy / swampy, sandy area, aquatic vegetation, built up land, dense forest, degraded forest, waterlogged area and agricultural fallow land. The least sensitive parameter is agricultural fallow land, which has minimum influence on predicted results of land use land cover of 2007. The validation of CA Markov land use land cover prediction results shows Kstandard is 0.7928.
QUANTIFYING THE QUALITY OF INDOOR MAPS
Authors: Assali M. , Pipelidis G. , Podolskiy V. , Iwaszczuk D. , Heinen L. , Gerndt M.
Published: June 2019

Abstract. Indoor maps are required for multiple applications, such as, navigation, building maintenance and robotics. One of common methods for map generation is laser scanning. In such maps, not only geometry of the map is of interest, but also its quality. This study aims at developing methods for real-time generation of indoor maps using features extracted from pointclouds obtained by a robot with their simultaneous quality assessment. We investigate, how this quality can be quantified for feature-based maps. First, we introduce a method for modeling 2D maps into 3D models that enable their usage for localization. Second, we review and evaluate a number of algorithms that can enable us to address features in a map. Hence, we enable the generation of objects from a pointcloud that has been sensed. Finally, we study several aspects of the map quality and we formalize them into metrics that can be applied to quantify their quality.
ENERGY FUNCTION ALGORITHM FOR DETECTION OF OPENINGS IN INDOOR POINT CLOUDS
Authors: Assi R. , Landes T. , Macher H. , Grussenmeyer P.
Published: June 2019

Abstract. As the use of building information model (BIM) for architectural heritage becomes more relevant, this paper explores different solutions to further automatize the modelling process. The scan-to-BIM process still requires manual intervention that is time consuming, subject to errors and user-dependent. In this paper, the main focus is the automated segmentation of windows. In the first part of our paper, we will review and compare several state-of-the-art methods for automatic detection and segmentation of openings in a point cloud. Based on the most pertinent aspects of those methods, a new algorithm focusing on indoor point clouds is proposed. After walls are already detected, they are converted in 2D binary images. Holes in those images correspond to openings. We submit each opening to an energy function with two terms: data and coherence. The data term depends on the shape of the opening. The coherence term considers the position of the opening in the scene. Those function let us determine if an opening in the point cloud is due to a window/door or an object obstructing the acquisition. In the third part we discuss the results obtained by applying the method to different datasets.
A SCENE-ASSISTED POINT-LINE FEATURE BASED VISUAL SLAM METHOD FOR AUTONOMOUS FLIGHT IN UNKNOWN INDOOR ENVIRONMENTS
Authors: Cheng S. , Yang J. , Kang Z. , Akwensi P. H.
Published: June 2019

Abstract. Since Global Navigation Satellite System may be unavailable in complex dynamic environments, visual SLAM systems have gained importance in robotics and its applications in recent years. The SLAM system based on point feature tracking shows strong robustness in many scenarios. Nevertheless, point features over images might be limited in quantity or not well distributed in low-textured scenes, which makes the behaviour of these approaches deteriorate. Compared with point features, line features as higher-dimensional features can provide more environmental information in complex scenes. As a matter of fact, line segments are usually sufficient in any human-made environment, which suggests that scene characteristics remarkably affect the performance of point-line feature based visual SLAM systems. Therefore, this paper develops a scene-assisted point-line feature based visual SLAM method for autonomous flight in unknown indoor environments. First, ORB point features and Line Segment Detector (LSD)-based line features are extracted and matched respectively to build two types of projection models. Second, in order to effectively combine point and line features, a Convolutional Neural Network (CNN)-based model is pre-trained based on the scene characteristics for weighting their associated projection errors. Finally, camera motion is estimated through non-linear minimization of the weighted projection errors between the correspondent observed features and those projected from previous frames. To evaluate the performance of the proposed method, experiments were conducted on the public EuRoc dataset. Experimental results indicate that the proposed method outperforms the conventional point-line feature based visual SLAM method in localization accuracy, especially in low-textured scenes.
INDOOR 3D INTERACTIVE ASSET DETECTION USING A SMARTPHONE
Authors: Kostoeva R. , Upadhyay R. , Sapar Y. , Zakhor A.
Published: June 2019

Abstract. Building floor plans with locations of safety, security and energy assets such as IoT sensors, thermostats, fire sprinklers, EXIT signs, fire alarms, smoke detectors, routers etc. are vital for climate control, emergency security, safety, and maintenance of building infrastructure. Existing approaches to building survey are manual, and usually involve an operator with a clipboard and pen, or a tablet enumerating and localizing assets in each room. In this paper, we propose an interactive method for a human operator to use an app on a smart phone to (a) create the 2D layout of a room, (b) detect assets of interest, and (c) localize them within the layout. We use deep learning methods to train a neural network to recognize assets of interest, and use human in the loop interactive methods to correct erroneous recognitions by the networks. These corrections are then used to improve the accuracy of the system over time as the inspector moves from one room to another in a given building or from one building to the next; this progressive training and testing mechanism makes our system useful in building inspection scenarios where a given class of assets in a building are same instantiation of that object category, thus reducing the problem to instance, rather than category recognition. Experiments show our proposed method to achieve accuracy rate of 76% for testing 102 objects across 10 classes.
USING EDGECONV TO IMPROVE 3D OBJECT DETECTION FROM RGB-D DATA
Authors: Lin W. , Chen Y. , Wang C. , Li J.
Published: June 2019

Abstract. In this paper, we proposed a novel 3D deep learning model for object localization and object bounding boxes estimation. To increase the detection efficiency of small objects in the large scale scenes, the local neighbourhood geometric structure information of objects has been taken into the Edgeconv model, which can operate the original point clouds. We evaluated the 3D bounding box with high resolution in the RGB-D dataset and acquired stable effectiveness even under the sparse points and the strong occlusion. The experimental results indicate that our method achieved the higher mean average precision and better IOU of bounding boxes in SUN RGB-D dataset and KITTI benchmark.
CREATION OF A VIRTUAL REALITY ENVIRONMENT OF A UNIVERSITY MUSEUM USING 3D PHOTOGRAMMETRIC MODELS
Authors: Martinez K. P. , Untalan M. Z. G. , Burgos D. F. M. , Ramos R. V. , Germentil M. J. Q.
Published: June 2019

Abstract. Museums and exhibits are conventionally experienced first-hand through physical interaction with the display and features. Afar from these museums, photographs and videos provide a way to view these museums, but this is an experience far from the actual physical interaction. We study in this research how 3D modelling and photogrammetry can be used to create a virtual reality (VR) environment of a university museum named Bulwagan ng Dangal in the University of the Philippines-Diliman. The main software used were 3DF Zephyr for photogrammetric modelling and AutoDesk Maya for 3D modelling of the VR environment. Models were created based of photographs taken using a DSLR camera and mobile phones. Model validation results show 2.42% to 7.00% errors. The study suggests that photogrammetric methods can be used to create a VR environment of museums or similar indoor exhibits for the purpose of experience and effective visualization.
ADAPTIVE STRATEGY-BASED TIGHTLY-COUPLED INS/GNSS INTEGRATION SYSTEM AIDED BY ODOMETER AND BAROMETER
Authors: Tien Y. C. , Chen Y. L. , Chiang K. W.
Published: June 2019

Abstract. Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integration system have been widely applied in recent years. Unfortunately, it sometimes malfunctions and the performance heavily deteriorates, especially in urban area where signals from satellites may be blocked or reflected by modern buildings. In multipath or Non Light-of-sight (NLOS) environment, incorrect signal results in poor observability of GNSS measurement model in Kalman Filter (KF). For purpose of addressing the issue, we proposed an adaptive strategy-based tightly-coupled INS/GNSS integration system aided by odometer and barometer, targeting to mitigate the error from poor observability. In this method, tightly-coupled (TC) scheme is implemented as the fundamental system in order to increase the reliability and stability. TC is more suitable than Loosely-coupled (LC), the traditional scheme, in urban navigation because it requires less visible GNSS measurement and it overcomes the disadvantage of LC, and further enhances the navigation result. Furthermore, aiding sensors such as odometer and barometer are integrated in this system as well, serving as velocity and height constraints respectively. Since the precision of GNSS positioning depends on the properties of the environment, measurement model of KF must work adaptively. Thus, innovation-based Adaptive Scaled Estimation (IASE) and Residual-based Adaptive Scaled Estimation (RASE), are also implemented to improve navigation performance in this paper. Finally, from the experimental validation, the proposed adaptive sensor-fusion navigation algorithm significantly enhanced the performance. The improvement was approximate 80% compared with the pure TC scheme; the RMSE can reach 6m in 3D and 2.5 m in vertical.
POINT CLOUD CLASSIFICATION BY FUSING SUPERVOXEL SEGMENTATION WITH MULTI-SCALE FEATURES
Authors: Ao W. , Wang L. , Shan J.
Published: June 2019

Abstract. Point cloud classification is quite a challenging task due to the existence of noises, occlusion and various object types and sizes. Currently, the commonly used statistics-based features cannot accurately characterize the geometric information of a point cloud. This limitation often leads to feature confusion and classification mistakes (e.g., points of building corners and vegetation always share similar statistical features in a local neighbourhood, such as curvature, sphericity, etc). This study aims at solving this problem by leveraging the advantage of both the supervoxel segmentation and multi-scale features. For each point, its multi-scale features within different radii are extracted. Simultaneously, the point cloud is partitioned into simple supervoxel segments. After that, the class probability of each point is predicted by the proposed SegMSF approach that combines multi-scale features with the supervoxel segmentation results. At the end, the effect of data noises is supressed by using a global optimization that encourages spatial consistency of class labels. The proposed method is tested on both airborne laser scanning (ALS) and mobile laser scanning (MLS) point clouds. The experimental results demonstrate that the proposed method performs well in terms of classifying objects of different scales and is robust to noise.
QUALITY PREDICTION OF DENSE POINTS GENERATED BY STRUCTURE FROM MOTION FOR HIGH-QUALITY AND EFFICIENT AS-IS MODEL RECONSTRUCTION
Authors: Moritani R. , Kanai S. , Date H. , Niina Y. , Honma R.
Published: June 2019

Abstract. In this paper, we introduce a method for predicting the quality of dense points and selecting low-quality regions on the points generated by the structure from motion (SfM) and multi-view stereo (MVS) pipeline to realize high-quality and efficient as-is model reconstruction, using only results from the former: sparse point clouds and camera poses. The method was shown to estimate the quality of the final dense points as the quality predictor on an approximated model obtained from SfM only, without requiring the time-consuming MVS process. Moreover, the predictors can be used for selection of low-quality regions on the approximated model to estimate the next-best optimum camera poses which could improve quality. Furthermore, the method was applied to the prediction of dense point quality generated from the image sets of a concrete bridge column and construction site, and the prediction was validated in a time much shorter than using MVS. Finally, we discussed the correlation between the predictors and the final dense point quality.
EXTRACTION OF LEAF ANGLE DISTRIBUTION FROM AN INDIVIDUAL BROADLEAF TREE USING TERRESTRIAL LASER SCANNING DATA
Authors: Chen Y. , Liu Z. , Zhang W. , Qiao C. , Gu H.
Published: June 2019

Abstract. The angular distribution of leaves is a key vegetation structural parameter for evaluating the reflection and transmission of solar radiation through vegetation canopies. Accurate extraction of Leaf Angle Distribution (LAD) is of great importance in estimating other vegetation structural parameters such as the canopy clumping and leaf area index. However, field measurement of LAD is time-consuming, labour-intensive and subjective. In most studies, LAD is assumed to follow the spherical distribution assumption within canopy which may lead to considerable errors. To address this issue, we proposed a new approach for leaf segmentation and LAD measurement of individual broadleaf tree based on the TLS point cloud data. Based on the point density, point continuity and the distribution of intensity in the point cloud, this approach provides a fast and accurate leaf segmentation and LAD extraction strategy. Results of this TLS-based LAD method compared well with that extracted by the field measurement and the MDI-based method. This strategy shows its potential and applicability in accurate LAD measurement and LAI estimation.
THE POTENTIAL OF DUAL-WAVELENGTH TERRESTRIAL LASER SCANNING IN 3D CANOPY FUEL MOISTURE CONTENT MAPPING
Authors: Elsherif A. , Gaulton R. , Mills J. P.
Published: June 2019

Abstract. Terrestrial laser scanning (TLS) instruments have been widely utilized in measuring vegetation canopy structural parameters, being capable of providing high density point clouds. However, less attention has been paid to using TLS intensity data in estimating vegetation biochemical attributes, and calculating water status metrics, that can help in early detection of vegetation stress and risk of wildfire. Water status metrics, such as the leaf Equivalent Water Thickness (EWT) and the Fuel Moisture Content (FMC), are being commonly estimated from optical remote sensing data. However, such estimates mainly reflect the water status of canopy top and ignore the vertical heterogeneity of water content distribution within the canopy. The estimates are also affected by canopy structure and understory reflectance. Such limitations can potentially be addressed using TLS intensity data, as observations are performed in three dimensions (3D). This study therefore investigated the potential of using dual-wavelength TLS intensity data to estimate FMC in 3D. The calculated Normalized Difference Index (NDI) of 808 nm near infrared and 1550 nm shortwave infrared wavelengths was found to be correlated to FMC at leaf level for four different tree species. The correlation was moderate, and the relationships were not consistent between species. NDI was subsequently used to estimate FMC at canopy level in seven trees in a small tree plot with an average error < 5 %. The 3D estimates of FMC revealed vertical heterogeneity in all trees measured, which varied between species and also between trees from the same species.
3D-CNN BASED TREE SPECIES CLASSIFICATION USING MOBILE LIDAR DATA
Authors: Guan H. , Yu Y. , Yan W. , Li D. , Li J.
Published: June 2019

Abstract. Our work addresses the problem of classifying tree species from mobile LiDAR data. The work is a two step-wise strategy, including tree segmentation and tree species classification. In the tree segmentation step, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, individual trees are segmented via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. In the tree species classification, a voxel-based 3D convolutional neural network (3D-CNN) model is developed based on intensity information. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree classification method. Qualitative analysis shows that our algorithm achieves a good performance.
Dynamics of ionic species in Svalbard annual snow: the effects of rain event and melting
Authors: Barbaro Elena , Varin Cristiano , Pedeli Xanthi , Christille Jean Marc , Kirchgeorg Torben , Giardi Fabio , Cappelletti David , Turetta Clara , Gambaro Andrea , Bernagozzi Andrea , Gallet Jean Charles , Björkman Mats P. , Spolaor Andrea
Published: June 2019

Abstract. The Arctic and middle latitude (such as the Alps) ice core archives, except for the Greenland summit, are strongly influenced by melting processes, able to modify the original chemical signal of the annual snowfall. In the last decades, the increase of the average Arctic temperature has caused and enhanced surface snow melting in the higher ice cap, especially in the Svalbard Archipelago. The increase of the frequency and altitude of winter “rain on snow” events as well as the increase of the length of the melting season has a direct impact on the chemical composition of the seasonal and permanent snow layers due to different migration processes of water-soluble compounds, such as ionic species. The re-allocation along the snowpack of ionic species could significantly modify the original chemical signal present in the annual snow, making comprehensive interpretation of climate records difficult. The chemical composition of the first 100 cm of the seasonal snow at Austre Brøggerbreen Glacier (Spitsbergen, Svalbard Islands, Norway) was monitored daily from the 27th of March until to the 31st of May 2015. The experiment period covers almost the entire Arctic spring until the melting season. During the experiment, a rain event occurred on the 16th to 17th of April while from the 15th of May the snowpack reached an isothermal profile. The presented dataset is unique and helps to better understand the behaviour of cations (K+, Ca2+, Na+, Mg2+), anions (Br−, I−, SO42−, NO3−, Cl−, MSA) and two carboxylic acids (C2-glycolic and C5-glutaric acids) in the snowpack during this melting period. The results obtained from the experiment give us an overview of how the chemicals are remobilized in the snowpack during a rain event or due to the melting at the end of the spring season. The aim of this paper is to give a picture of the evolution of the seasonal snow strata with the aim to better understand the processes that can influence the chemical distribution in the annual snow. The results of the present work are unique and helpful for future analyses and interpretation of ice core paleoclimatic archives.
Evaluation of genetic diversity of edible honeysuckle monitored by RAPD in relation to bioactive substances
Authors: Cehula Marcela , Jurí­ková Tünde , Žiarovská Jana , Mlček Jiří­ , Kyseľ Matúš
Published: June 2019

The aim of this study was clarifying the relation between genetic diversity of edible honeysuckle (Lonicera kamtschatica) and the major group of biologically active substances as total polyphenols content (TPC) including antioxidant activity (AO). Fruits of edible honeysuckle becomes more and more popular, especially in Europe. The current status of research on polyphenolic compounds in the berries of edible honeysuckle and their biological effects, including recommended utilization, are reviewed.The biological material including 14 cultivars of the edible honeysuckle (´Zoluška´, ´Amfora´, ´Pruhonický 44´, ´Vasilijevsky´, ´Moskovskaja´, ´Vojtek´, ´Sinoglaska´, ´Altaj´, ´Lipnická´, ´Kamčadalka´, ´Sinaja Ptica´, ´Fialka´, ´Modrý Triumf´, and ´Leningradský velikán´) originated from Czech republic (Žabcice near Brno). The content of TPC and AO were determined by location and its soil-climatic conditions and these environmental circumstances determines the RAPD profiles of analysed honeysuckle acessions, too. DPPH method was used to analyze AO and Folin-Ciocalteu method was used to determine TPC. The results of experiment showed that the highest value of AO was determined at the cultivars ´Zoluška´ (81.04 mg.L-1) and the lowest was measured in ´Kamčadalka´ (54.122 mg.L-1).  On the contrary, the highest content of TPC was determined at the cultivar ´Kamčadalka´ (51.09 mg.L-1) and the lowest value was measured at the cultivar ´Pruhonický 44´ (21.65 mg.L-1). Phylogenetic trees were similar in genetic distance. The  content of TPC and AO were  not statistically significant in relation to cultivar. The analyzed cultivars of the edible honeysuckle were separated in 4 clusters according to used primers. In both  gel images, the amplicon size ranged from 100 to 1,500 bp. We found that genetic diversity was partially related to content of total polyphenolic substances and antioxidant activity. Based on phylogenetic trees we have stated that variety ´Lipnická´, ´Sinoglaska´, ´Altaj´, ´Leningradský velikán´, ´Modrý Triumf´, ´Sinaja Ptica´ and ´Kamčadalka´ were grouped in the similar cluster. The highest genetic distance was determined at the variety ´Vasilijevskaja´ and ´Amfora´. In the same way, there were variety ´Vojtek´, ´Fialka´ and ´Zoluška´.
Investment and Growth Amidst Widening Government Debt: “The Ghanaian Story”
Authors: Mawutor John Kwaku Mensah , Boachie Yiadom Eric , Amankwa Richard Fosu
Published: June 2019

The study revisits the debt-growth nexus and broadens the argument to examine the unique effect of government debt on investment in Ghana. Data from World Development Indicators on the Ghanaian economy were sampled from 1990 to 2015. The empirical results from the Multiple Linear Regression (MLR) suggest an inverse relationship between government debt and economic growth in Ghana. In addition, a percentage increase in government debt reduces investment by 0.65%; implying that government debt harms investment due to fungibility of debt and accompanying debt repayment responsibilities. Policy ramifications resulting from the study are that the Ghanaian government should restructure public debt management to eliminate debt fungibility and reduce debt to GDP ratio as well.
Testing a Process-Product Framework for Conceptualizing Motivation
Authors: Palmer David
Published: June 2019

This study explored the idea that motivation for learning can be a process as well as a product: the product being an active state of motivation whereas the process would include the antecedents that created that state. This hypothesis was tested by interviewing 47 university students immediately after normal lectures. Results showed that 89% had experienced an active state of motivation (the product phase) that was influenced by antecedents that included personal characteristics (e.g., self-efficacy, individual interest), temporal personal factors (mood, fatigue), and contextual factors (teacher enthusiasm and “interestingness”). It was concluded that process-product is a potentially useful framework for conceptualizing motivation.
Expediente V8N1
Authors: AtoZ Comitê Editorial
Published: June 2019

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