bluuebunny/crossref_metadata_2025 huggingface dataset:
UAV BASED MULTI SEASONAL DECIDUOUS TREE SPECIES ANALYSIS IN THE HAINICH NATIONAL PARK USING MULTI TEMPORAL AND POINT CLOUD CURVATURE FEATURES
Authors: Hese S. , Thiel C. , Henkel A.
Published: June 2019

Abstract. Low cost UAV systems are a flexible and mobile platform for very detailed spatial high-resolution point cloud and surface height mapping projects. This study investigates the potential of the DJI Phantom 4 Pro 3D point clouds and derived crown surface height information in combination with RGB spectral information for mapping of deciduous tree species in the Hainich national park area. RGB image data was captured in August, early October and November 2018 to create a multi seasonal spectral dataset for a 100 ha test area. The flight campaigns were controlled from the Hainich flux tower platform in 40 m height owned and operated by University of Göttingen in the central part of the park area. Absolut georeferencing accuracy of the datasets was improved using 7 DGPS measured control points within the stand structure on small forest clearings. Image files and ground control points were processed to a dense point cloud model with 2.6 billion points (approximately 200k points per tree crown object) using the Agisoft Metashape cluster processing environment. Additionally, a digital surface model and a true ortho image mosaic with 3 cm spatial resolution was generated. For the differentiation of deciduous tree species, a reference data set with coordinates for the tree species Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), Carpinus betulus (hornbeam) and dead trees and early defoliated trees was defined. The study site is however dominated by Fagus sylvatica and Fraxinus excelsior. We studied two different groups of features: tree crown surface height variability parameters using point cloud densities, point cloud height variance, local standard deviation of gaussian curvature, standard deviation of local point cloud roughness and multi temporal normalised spectral features using multi seasonal uncalibrated UAV RGB data. Analysis of feature separability showed that very high-resolution point cloud surface curvature properties with small neighbourhood radii can differentiate some tree species types but we also found multitemporal spectral ratios based on RGB data to be very successful in differentiating the main tree species.Results of this work show that super fine very dense point cloud models and derived roughness measures of mixed forest stand surfaces hold valuable information for deciduous species discrimination and will likely also be very useful for morphological analysis of tree crown types.
PREDICTING BIOMASS AND YIELD AT HARVEST OF SALT-STRESSED TOMATO PLANTS USING UAV IMAGERY
Authors: Johansen K. , Morton M. J. L. , Malbeteau Y. , Aragon B. , Al-Mashharawi S. , Ziliani M. , Angel Y. , Fiene G. , Negrao S. , Mousa M. A. A. , Tester M. A. , McCabe M. F.
Published: June 2019

Abstract. Biomass and yield are important variables used for assessing agricultural production. However, these variables are difficult to estimate for individual plants at the farm scale and may be affected by abiotic stressors such as salinity. In this study, the wild tomato species, Solanum pimpinellifolium, was evaluated through field and UAV-based assessment of 600 control and 600 salt-treated plants. The aim of this research was to determine, if UAV-based imagery, collected one, two, four, six, seven and eight weeks before harvest could predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest and if predictions varied for control and salt-treated plants. A Random Forest approach was used to model biomass and yield. The results showed that shape features such as plant area, border length, width and length had the highest importance in the random forest models. A week prior to harvest, the explained variance of fresh shoot mass, number of fruits and yield mass were 86.60%, 59.46% and 61.09%, respectively. The explained variance was reduced as a function of time to harvest. Separate models may be required for predicting yield of salt-stressed plants, whereas the prediction of yield for control plants was less affected if the model included salt-stressed plants. This research demonstrates that it is possible to predict biomass and yield of tomato plants up to four weeks prior to harvest, and potentially earlier in the absence of severe weather events.
ANALYSIS OF ORIENTATION ACCURACY OF AN UAV IMAGE ACCORDING TO CAMERA CALIBRATION
Authors: Lim P. C. , Seo J. , Son J. , Kim T.
Published: June 2019

Abstract. Utilization of an UAV is increasing because of its easy operation and time saving advantages. Compared with other remote sensing platforms, the biggest difference of a small UAV is the unstable flight attitude due to platform stability. UAVs are equipped with a commercial grade camera, unlike expensive cameras mounted on manned aircraft or satellite platforms. The quality of the map is determined by the characteristics of an UAV and camera performance. In this study, the accuracy of orientation parameters according to UAV camera calibration options was analysed. The camera calibration options were no calibration, self-calibration and calibration by a public calibration toolkit with manual corner measurement. We used four different type of UAVs and three type of SWs. Interior and exterior orientation parameters according to the camera calibration options were obtained from each software. The result of processing by each camera calibration option was different from each other. This may indicate that the UAV camera calibration was not performed accurately and still needed further improvement.
ULTRA-HIGH SPATIAL RESOLUTION UAV-BASED IMAGERY TO PREDICT BIOMASS IN TEMPERATE GRASSLANDS
Authors: Lussem U. , Bolten A. , Menne J. , Gnyp M. L. , Bareth G.
Published: June 2019

Abstract. Monitoring biomass yield in grassland is of key importance to support sustainable management decisions. Especially the high spatio-temporal variety in grasslands requires rapid and cost-efficient data acquisition with a high spatial and temporal resolution. Therefore, this study aims to evaluate the comparability of UAV-based simultaneously acquired vegetation indices from a consumer-grade RGB-camera (Sony Alpha 6000) and a well-calibrated narrow-band multispectral camera (MicaSense RedEdge-M) to estimate dry matter biomass yield. The study site is an experimental grassland field in Germany with four nitrogen fertilizer levels. Biomass yield and UAV-based data for the first cut in May 2018 was analysed in this study. From the RGB-data the Plant Pigment Ratio Index (PPR) and the Normalized Green Red Difference Index (NGRDI) and from the multispectral data the Normalized Difference Vegetation Index (NDVI) are calculated as predictors for dry biomass yield. The NGRDI and NDVI perform moderately well with cross-validation R2 of 0.57 and 0.63 respectively, while the PPR performs better with an R2 of 0.70. These results indicate the potential of low-cost UAV-based methods for rapid assessment of grasslands.
VERTICAL ORIENTATION CORRECTION OF UAV IMAGE-BASED POINT CLOUDS USING STATISTICAL MODELING OF GABLE ROOF GEOMETRY
Authors: Polewski P. , Yao W. , Fang L.
Published: June 2019

Abstract. Coregistration of point clouds obtained from various sensors is an important part of workflows for automatic building reconstruction from remote sensing data. Many approaches assume a common Z axis between the coordinate systems, and perform coregistration in 2D. While this assumption is usually valid for laser scanning (LS) data, for photogrammetric point clouds the Z axis is in general different from the world Z axis, and requires correction e.g. by manually measured ground control points (GCP). In this paper, we propose a fully automatic, GCP-free procedure for finding the world Z axis in rural areas, based on the relationships of planar surfaces in building gable roofs. Instead of performing direct gable line detection, we derive these lines as theoretical intersections between adjacent roof planes from 3D shape fitting. Each gable roof then casts a vote for both the Z axis direction and sign based on roof convexity constraints, and the votes are aggregated through a non-parametric kernel density estimator model. Experiments on two real world UAV image-based point clouds show that the Z axis recovered by our method leads to high-accuracy planimetric coregistration, with a median distance over 89 as well as 149 matched linear feature pairs (respectively for dataset 1 and 2) lying below 1 cm. Our results indicate that a high-quality vertical orientation can be achieved without using any GNSS or IMU hardware, which enables the use of low-cost UAV platforms for suburban and rural mapping tasks.
INVESTIGATIONS ON THE GEOMETRIC QUALITY OF CAMERAS FOR UAV APPLICATIONS USING THE HIGH PRECISION UAV TEST FIELD ZOLLERN COLLIERY
Authors: Przybilla H.-J. , Gerke M. , Dikhoff I. , Ghassoun Y.
Published: June 2019

Abstract. The geodetic-photogrammetric test field at the industrial monument Zollern colliery in Dortmund offers a scenario for carrying out geometric and radiometric tests of UAV systems. The foundation for this builds a geodetic precision network (position and height accuracy approx. 2 mm) with a total of 45 ground control points, distributed over an area of approx. 7 hectares. Within the scope of a campaign carried out in autumn 2017, various UAV sensor systems were tested under comparable conditions. Within this paper geometric investigations of two current DJI cameras, Zenmuse X4S (20 Mpix) and X5S (20.8 Mpix), as well as a Phase One IXU 1000 (100 Mpix) are presented. While the Zenmuse cameras reflect the current state of development of the manufacturer DJI, the medium format camera system from Phase One is primarily settled in the classic aerial segment. However, the desire for increased measurement accuracy (e. g. for engineering applications) also makes such a high-performance sensor interesting for UAV applications.In addition to the configuration of the test field, the system comparison requires identical parameters for flight planning, in particular image overlapping, a complete cross flight configuration at different flight altitudes and the definition of a uniform ground resolution (GSD = 14 mm).The investigations show clear differences in the achievable quality of the cameras. Though the high-priced Phase One system shows the best results, the most cost-effective system, the Zenmuse X4S, delivers only slightly worse results. In contrast, the Zenmuse X5S performs significantly worse than the other systems, mainly resulting from the mechanically unstable camera concept with interchangeable lenses. Finally, the comparison of the software products Pix4D Pix4Dmapper, Inpho UASMaster by Trimble and Agisoft PhotoScan partly shows significant differences in the results of image orientation. In particular in settings with sparse GCP usage the results vary considerably, indicating different strategies on how the residuals are distributed and the datum is defined, mostly Pix4Dmapper outperforms the others. In better GCP configurations there is no significant difference between Pix4mapper and Agisoft PhotoScan, while UASMaster does never deliver the best results.
A CALIBRATION WORKFLOW FOR “PROSUMER” UAV CAMERAS
Authors: Radford C. R. , Bevan G.
Published: June 2019

Abstract. High-end consumer quadcopter UAVs or so-called “prosumer devices”, have made inroads into the mapping industry over the past few years, arguably displacing more expensive purpose-built systems. In particular, the DJI Phantom series quadcopters, marketed primarily for videography, have shown considerable promise due to their relatively high-quality cameras. Camera pre-calibration has long been a part of the aerial photogrammetric workflow with calibration certificates being provided by operators for every project flown. Most UAV data, however, is processed today in Structure-from-Motion software where the calibration is generated “on-the-fly” from the same image-set being used for mapping. Often the scenes being mapped and their flight-plans are inappropriate for calibration as they do not have enough variation in altitude to produce a good focal-length solution, and do not have cross-strips to improve the estimation of the principal point. What we propose is a new type of flight-plan that can be run on highly textured scenes of varying height prior to mapping missions that will significantly improve the estimation of the interior orientation parameters and, as a consequence, improve the overall accuracy of projects undertaken with these sorts of UAV systems. We also note that embedded manufacturer camera profiles, which correct for distortion automatically, should be removed prior to all photogrammetric processing, something that is often overlooked as these profiles are not made visible to the end user in most image conversion software, particularly Adobe’s CameraRAW.
DETERMINATION OF SURFACE VELOCITY OF A RIVER USING VIDEOS CAPTURED FROM UNMANNED AERIAL SYSTEM (UAS)
Authors: Shrestha S. , Thapa M. , Feng L. G. Y. , Abdelkader S. , Prinz T. , Lehmann J. , Fritze H.
Published: June 2019

Abstract. Water flow dynamics of a river has significant effect in the ecological functions played by the river. There exists an intricate relationship between discharge in a river and phenomenon such as sedimentation, prevalence of vegetation and river morphology. In order to better understand these phenomena, it is important to determine the velocity of the river. While there exist conventional in-river velocity measurement techniques, UAS based method offers the capability to make large number of measurements at a large number of locations without the need to place measuring instrument at each of these locations. In this paper, we present the results of application of UAS in determining surface velocity of river. In this study, we released floats along the river and captured videos by mounting RGB sensor as well as thermal sensor in the UAS. The videos were processed using OpenCV library as well as Matlab’s Image Processing Toolbox. This paper discusses the learnings of our study which indicates that videos captured from UAS can be used to determine surface velocity of a river.
VALUING FOREST STAND AT A GLANCE WITH UAV BASED LIDAR
Authors: Vepakomma U. , Cormier D.
Published: June 2019

Abstract. Comprehensive knowledge of characteristics and variability of any material is essential for its best utilization; hence forest managers are increasingly recognising the importance of resource quality characterisation earlier along the value-chain. Current methods for cruising timber at the stump lack information on branch characteristics and detailed assessment based on terrestrial lidar systems are restricted to sampled trees for assessment at the block level. Rich and dense information on vertical structure of the canopies captured using lidar system from a closer range like on a UAV platform provides a flexible, quick and a cost-effective alternative for assessing forest stands. In this study, along with detailed tree characterisation and variability, we explore the potential of ultrahigh density lidar data acquired from a UAV platform (ULS) to develop a non-destructive estimation of a suite of timber quality determinants like branchiness, clear stem and stem straightness for standing trees, and further determine possible amount of bucking segments (logs) and their expected quality. Validation of the algorithm is tested on white pine stand in Petawawa Research Forest, Ontario, Canada, holds promise in determining spatially-explicit tree level and hence stand quality.
OPPORTUNITIES OF UAVS IN ORCHARD MANAGEMENT
Authors: Zhang C. , Valente J. , Kooistra L. , Guo L. , Wang W.
Published: June 2019

Abstract. The growth process of fruit trees is accompanied by a large number of monitoring and management activities, such as pruning, irrigation, fertilization, spraying, and harvesting, which are labour intensive and time consuming. In the context of precision agriculture, automation and precision orchard management not only saves labour resources and increases the income of growers, but also has great significance in improving resource utilization. Recent technological developments enable Unmanned Aerial Vehicles (UAVs, also commonly referred to as Unmanned Aerial Systems, or ‘drones’) to become an efficient monitoring tool for improving orchard management, that can provide growers much more detailed and precise information about fruit crops health status, geometric variables, physiological variables etc. This paper reviews the use of UAVs in orchard management, with a focus on recent UAV applications, synthetically describing the existing situation (e.g., general data processing approaches, sensing platform and sensor uploaded). The challenges and prospects of UAVs opportunities in orchard management are also summarized.
GEOMETRIC OBJECT BASED BUILDING RECONSTRUCTION FROM SATELLITE IMAGERY DERIVED POINT CLOUDS
Authors: Li Z. , Xu B. , Shan J.
Published: June 2019

Abstract. 3D building models are needed for urban planning and smart city. These models can be generated from stereo aerial images, satellite images or LiDAR point clouds. In this paper, we propose a geometric object-based building reconstruction method from satellite imagery derived point clouds. The goal is to achieve a geometrically correct, topologically consistent, and non-redundant 3D representation for buildings in urban areas. The paper first introduces our motivation, followed by a comprehensive review on related works. We then introduce the methodology and process developed in this paper. Primary results from the point clouds generated from WorldView high resolution satellite images are used to demonstrate the performance of the approach.
AN ORIGINAL ALGORITHM FOR BIM GENERATION FROM INDOOR SURVEY POINT CLOUDS
Authors: Capocchiano F. , Ravanelli R.
Published: June 2019

Abstract. Nowadays, it is essential to find new strategies, able to perform the first step of the scan-to-BIM process, by retrieving the geometrical information contained in point clouds that are so easily collected through laser scanners and range cameras. This paper presents a new algorithm for the automatic extraction of the layout and the height of a small indoor environment from its point cloud. In particular, the algorithm was tested on a point cloud of 600000 vertices, selected from the dataset of the ISPRS benchmark on indoor modelling. The preliminary results are encouraging: the 3D shape (layout and height) of the investigated room is effectively reconstructed.
MULTISPECTRAL AIRBORNE LASER SCANNING POINT-CLOUDS FOR LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS
Authors: Ma L. , Chen Z. , Li Y. , Zhang D. , Li J. , Chapman M. A.
Published: June 2019

Abstract. This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.
INDOOR POSITIONING BASED-ON IMAGES AIDED BY ARTIFICIAL NEURAL NETWORKS
Authors: Hung M. C. , Liao J. K. , Chiang K. W.
Published: June 2019

Abstract. Indoor positioning has attracted much attention in recent years due to the trend of Internet of Things (IoT), which is capable of providing numerous applications such as personal tracking, vehicle locator, and Location-Based Service (LBS). To put LBS into practice, positioning and navigation are one of the necessary techniques. Using the smartphone to process indoor positioning also become more and more usual. The most common algorithm in the inertial navigation system is Pedestrian Dead Reckoning (PDR), utilizing sensors built-in the smartphones to conquer the strait of GNSS-denied environment. However, for the purpose of eliminating the error accumulated with time, PDR combining with other algorithms, for instance, updating some geospatial information steadily is a better way to solve this problem. Therefore, this research proposes the imaged based aided algorithm. Moreover, in this study, a novel Artificial Neural Networks (ANN) embedded the system is proposed. The self-designed georeferenced markers and the indoor floor plan will be produced by an Indoor Mobile Mapping System (IMMS) in advance. This research proposed using Cascade-Correlation neural Network (CCN) to estimate the distance between the marker and the smartphone camera. The accuracy using this method can achieve to 0.27 meter. As if at least three coordinates and the distance can be obtained simultaneously, the position of the user can be calculated by the trilateration method. From the experiment, the accuracy of the positioning is about 0.5 meter. This way seems to have the high potential to bring into play on the real-time indoor positioning.
INDOOR MAPPING EYEWEAR: GEOMETRIC EVALUATION OF SPATIAL MAPPING CAPABILITY OF HOLOLENS
Authors: Khoshelham K. , Tran H. , Acharya D.
Published: June 2019

Abstract. Existing indoor mapping systems have limitations in terms of time efficiency and flexibility in complex environments. While backpack and handheld systems are more flexible and can be used for mapping multi-storey buildings, in some application scenarios, e.g. emergency response, a light-weight indoor mapping eyewear or head-mounted system has practical advantages. In this paper, we investigate the spatial mapping capability of Microsoft Hololens mixed reality eyewear for 3D mapping of large indoor environments. We provide a geometric evaluation of 3D mesh data captured by the Hololens in terms of local precision, coverage, and global correctness in comparison with terrestrial laser scanner data and a reference 3D model. The results indicate the high efficiency and flexibility of Hololens for rapid mapping of relatively large indoor environments with high completeness and centimetre level accuracy.
CONSTRUCTION OF OBSTACLE ELEMENT MAP BASED ON INDOOR SCENE RECOGNITION
Authors: Li F. , Wang H. , Akwensi P. H. , Kang Z.
Published: June 2019

Abstract. Route planning and navigation in indoor space have become a hot topic recently. To accomplish this task, a map and a real-time detection system are needed. Due to Lidar systems’ high efficiency in data acquisition, Lidar sensors have become an indispensable part of an object detection system. In this paper, we use Lidar points to generate obstacle maps. The obstacle maps can be used as a reference for route planning and navigation. To identify single objects more precisely, a deep network combined PointNet with Markov Random Field (MRF) is designed in our work to classify Lidar points. Then, single objects are segmented by using the Euclidean clustering method. After that, the prior rules and derived criteria we summarized from large amount images are used to determine objects’ kind between Influence Movement Obstacles (IMO) and Non-Influence Movement Obstacles (N-IMO). Finally, objects are projected into a 2D plane to generate obstacle maps. To evaluate the performance of our method, experiments were performed on the S3DIS dataset of Stanford University. The results show that our method greatly improves the overall accuracy compared to the original PointNet model, and can generate high-quality obstacle maps.
VISION-BASED INDOOR LOCALIZATION VIA A VISUAL SLAM APPROACH
Authors: Li M. , Rottensteiner F.
Published: June 2019

Abstract. With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image.
TOWARDS CONTINUOUS CONTROL FOR MOBILE ROBOT NAVIGATION: A REINFORCEMENT LEARNING AND SLAM BASED APPROACH
Authors: Mustafa K. A. A. , Botteghi N. , Sirmacek B. , Poel M. , Stramigioli S.
Published: June 2019

Abstract. We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG). Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid-based SLAM with Rao-Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid-steering mobile robot.
BUILDING CHANGE DETECTION THROUGH COMPARISON OF A LIDAR SCAN WITH A BUILDING INFORMATION MODEL
Authors: Tran H. , Khoshelham K.
Published: June 2019

Abstract. Building Information Models (BIMs) are of paramount importance in lifecycle management of buildings as they enable collaboration among various stakeholders at different phases of a construction project, from planning to maintenance and operation. However, there is usually inconsistency between the as-is condition of the building and its existing BIM, because BIMs are generally not updated to reflect changes in the environment. Monitoring the changes during a building’s lifecycle and keeping the BIM up-to-date is useful for a variety of applications. Yet this process often involves manual surveying inspections, which are very time-consuming, error-prone, and laborious. In this paper, we present an automated approach for building change detection through a comparison between the BIM and a point cloud of the building indoor environment. The approach is based on point classification and surface coverage to identify discrepancies between the BIM and the point cloud. Experiments on a synthetic dataset and an ISPRS Benchmark dataset show the potential of the proposed approach not only for change detection and identifying discrepancies, but also for locating the removed and new structures of the building in comparison with the BIM. The results are useful for updating the BIM to represent the as-is condition of the building and for temporal analysis of changes during a building’s lifecycle.
SEMANTIC LABELING OF ALS POINT CLOUDS FOR TREE SPECIES MAPPING USING THE DEEP NEURAL NETWORK POINTNET++
Authors: Briechle S. , Krzystek P. , Vosselman G.
Published: June 2019

Abstract. Most methods for the mapping of tree species are based on the segmentation of single trees that are subsequently classified using a set of hand-crafted features and an appropriate classifier. The classification accuracy for coniferous and deciduous trees just using airborne laser scanning (ALS) data is only around 90% in case the geometric information of the point cloud is used. As deep neural networks (DNNs) have the ability to adaptively learn features from the underlying data, they have outperformed classic machine learning (ML) approaches on well-known benchmark datasets provided by the robotics, computer vision and remote sensing community. Though, tree species classification using deep learning (DL) procedures has been of minor research interest so far. Some studies have been conducted based on an extensive prior generation of images or voxels from the 3D raw data. Since innovative DNNs directly operate on irregular and unordered 3D point clouds on a large scale, the objective of this study is to exemplarily use PointNet++ for the semantic labeling of ALS point clouds to map deciduous and coniferous trees. The dataset for our experiments consists of ALS data from the Bavarian Forest National Park (366 trees/ha), only including spruces (coniferous) and beeches (deciduous). First, the training data were generated automatically using a classic feature-based Random Forest (RF) approach classifying coniferous trees (precision = 93%, recall = 80%) and deciduous trees (precision = 82%, recall = 92%). Second, PointNet++ was trained and subsequently evaluated using 80 randomly chosen test batches à 400 m2. The achieved per-point classification results after 163 training epochs for coniferous trees (precision = 90%, recall = 79%) and deciduous trees (precision = 81%, recall = 91%) are fairly high considering that only the geometry was included. Nevertheless, the classification results using PointNet++ are slightly lower than those of the baseline method using a RF classifier. Errors in the training data and occurring edge effects limited a better performance. Our first results demonstrate that the architecture of the 3D DNN PointNet++ can successfully be adapted to the semantic labeling of large ALS point clouds to map deciduous and coniferous trees. Future work will focus on the integration of additional features like i.e. the laser intensity, the surface normals and multispectral features into the DNN. Thus, a further improvement of the accuracy of the proposed approach is to be expected. Furthermore, the classification of numerous individual tree species based on pre-segmented single trees should be investigated.
WEIGHTED POINT CLOUD AUGMENTATION FOR NEURAL NETWORK TRAINING DATA CLASS-IMBALANCE
Authors: Griffiths D. , Boehm J.
Published: June 2019

Abstract. Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds. However, many real-world point clouds contain a large class im-balance due to the natural class im-balance observed in nature. For example, a 3D scan of an urban environment will consist mostly of road and façade, whereas other objects such as poles will be under-represented. In this paper we address this issue by employing a weighted augmentation to increase classes that contain fewer points. By mitigating the class im-balance present in the data we demonstrate that a standard PointNet++ deep neural network can achieve higher performance at inference on validation data. This was observed as an increase of F1 score of 19% and 25% on two test benchmark datasets; ScanNet and Semantic3D respectively where no class im-balance pre-processing had been performed. Our networks performed better on both highly-represented and under-represented classes, which indicates that the network is learning more robust and meaningful features when the loss function is not overly exposed to only a few classes.
Modeling the effects of sediment concentration on the propagation
of flash floods in an Andean watershed
Authors: Contreras María Teresa , Escauriaza Cristián
Published: June 2019

Abstract. Rain-induced flash floods are common events in regions near mountain ranges. In peri-urban areas near the Andes the combined effects of the changing climate and ENSO have resulted in an alarming proximity of populated areas to flood-prone streams, increasing the risk for cities and infrastructure. Simulations of rapid floods in these watersheds are particularly challenging, due to the complex morphology, the insufficient hydrometeorological data, and the uncertainty posed by the variability of sediment concentration. High concentrations produced by hillslope erosion and rilling by the overland flow in areas with steep slopes and low vegetational covering can change significantly the dynamics of the flow as the flood propagates in the channel. In this investigation, we develop a two-dimensional finite-volume numerical model of the non-linear shallow water equations coupled with the mass conservation of sediment to study the effects of different densities, which include a modified version of the quadratic stress model to quantify the changes on the flow rheology. We carry out simulations to evaluate the effects of the sediment concentration on the floods in the Quebrada de Ramon watershed, a peri-urban Andean basin in central Chile. We simulate a confluence and a total length of the channel of 10.4 km, with the same water hydrographs and different combinations of sediment concentrations in the tributaries. Our results show that the most important effects on the flood propagation are observed in the range of concentrations from 0 % to 20 %. Simulations also show that variables such as the arrival time of the peak flow, and the shape of the hydrograph at different locations along the channel are not significantly affected by the sediment concentration and depend mostly on the steep channel morphology. Through this work we provide a framework for future studies aimed at improving hazard assessment, urban planning, and early warning systems in urban areas near mountain streams with limited data and affected by rapid flood events.
Realism in FIFA? How social realism enabled platformed racism in a video game
Authors: Srauy Sam , Cheney-Lippold John
Published: June 2019

Platformed racism offers a unique lens through which to investigate technological structures that enable racism. Online video games, such as EA Sports’ FIFA series — which dominates the soccer video game market share through its touted realism — feature these structures. Like many platforms, FIFA enables representations of real bodies (i.e., professional soccer players). But, unlike many games, FIFA enables game players to directly affect the creation/modification of these representation in the form of player character cards. Analyzing a census of six years of player cards, this study found that platformed racism was enabled because the game’s realism invited racism when players tried to maintain that realism. The study concludes that the catalyst for racism to emerge in FIFA was the drive towards realism.
Determination of HMW - GS in wheat using SDS - PAGE and Lab-on-chip methods
Authors: Kuťka Hlozáková Timea , Gregová Edita , Šliková Svetlana , Gálová Zdenka , Chňapek Milan , Drábeková Janka
Published: June 2019

SDS-PAGE is widely used to determine the amounts of the different gluten protein types. However, this method is time-consuming, especially at early stages of wheat breeding, when large number of samples needs to be analyzed. On the other hand, LoC (Lab-on-Chip) technique has the potential for a fast, reliable, and automatable analysis of proteins. Benefits and limitations of Lab-on-Chip method over SDS-PAGE method in gluten proteins evaluation were explored in order to determine in which way LoC method should be improved in order to make its results more compliant with the results of SDS-PAGE. Chip electrophoresis provides a very good reproducibility of HMW-GS patterns. Moreover this approach is much faster than the conventional SDS-PAGE methods requiring several hours for an analysis. Another advantage over traditional gel electrophoresis is lower sample and reagent volume requirements, as well as specialized protein standards for accurate reproducibility and quantification. In the present study, we identified novel complex allele located at the locus Glu-1B.
Determinants of Systemic Risk of Banks in India
Authors: Dash Mihir
Published: June 2019

This study examines the determinants of systemic risk for banks in India. The independent variables considered for the study include the sector, bank size, return on assets, beta, leverage, capital adequacy, non-performing assets, price to book value, deposits, loans & advances, investments, net interest income, and non-interest income. A mixed panel regression model was applied, with bank fixed effects and year random effects.The results of the study indicate that public sector banks have a much higher level of systemic impact than private sector banks. Further, the determinants of systemic impact are different for public sector and private sector banks. The systemic impact of public sector banks was positively related with size and negatively related with price to book value ratio and investments to total assets ratio, while the systemic impact of private sector banks was negatively related with return on assets and positively related with beta and net interest income to total funds ratio.
Historia : perspectiva y cambios
Authors: Viegas Josefa
Published: June 2019

Estamos en un momento en el que Clío, la musa de la historia, parece tener una mayor presencia en los ambientes académicos, y por qué no, culturales de este país.El año recién pasado un grupo de historiadores nos propusimos constituir el Seminario Permanente de Estudios Históricos adscrito al Archivo General de la Nación. Éste se ha conformado como un lugar de encuentro y de discusión histórica único en el país y de esta instancia se han promovido actos públicos para difundir alguna de las investigaciones de sus miembros. Por otra parte, en el ámbito académico hay novedades muy esperanzadoras.En este momento los alumnos de Historia de la Universidad Tecnológica están a punto de acabar su primer ciclo de la licenciatura. También, en la Universidad de El Salvador se están ultimando los detalles necesarios para implantar lo más pronto posible la carrera de Historia.Asimismo, en julio los especialistas de todo el mundo en historia de Centroamérica vinieron a El Salvador a reunirse en el V Congreso Centroamericano de Historia donde expusieron y debatieron los últimos estudios al respecto del pasado del istmo. Y todo esto fue posible gracias a la necesidad por parte de la sociedad salvadoreña por buscar y reflexionar acerca de su pasado, por hacer memoria.
Pasión y fortaleza : fórmula perfecta de una mujer ejemplar
Authors: Ruballo Thirza
Published: June 2019

Altos niveles de sensibilidad, chispazos de pasión y sobre todo una buena porción de fortaleza son las características que permitieron a las ministras Ana Evelyn Jacir de Lovo y María Eugenia Brizuela de Ávila, destacar con los mejores resultados en las encuestas de opinión pública que evaluaron el desempeño del gabinete en su primer año de gobierno.Entorno converso con las dos ministras para conocer su otra cara, su parte humana, sus temores, amores, triunfos, pero sobre todo sus experiencias que podrán servir de luz al final del túnel, ejemplo o motivación a muchas mujeres salvadoreñas.La situación de la mujer en El Salvador evoluciona, en los últimos años los cambios son más evidentes, actualmente las mujeres tienen una función importante en la familia, no sólo en el cuidado de los hijos, sino también en llevar el sustento. Su aporte moral y material es creciente.Las encuestas dicen que el treinta por ciento de las familias tienen por jefe a la mujer, pero solo contabilizan donde hay un miembro de la pareja, si se profundiza en los hogares donde están los dos, no necesariamente el hombre tiene la estabilidad y la función que tenía antes, la mujer también aporta una función social.
El hombre económico: naturaleza humana y cosificación
Authors: Centeno Ángel Alfonso
Published: June 2019

This article proposes to interpret homo oeconomicus from the category of reification of György Lukács. That is an ethos or way of life, perceived as natural but conditioned by mercantile economic structures. The characteristic features of economic behavior –economical reason and self-interest– are perceived as belonging to human nature, but they are in fact historically defined in their legitimizing function of Capitalism. The article also explores some consequences of the underlying Naturalism, especially those related to the place of citizenship and political action in society.
Realidad: Revista de Ciencias Sociales y Humanidades No. 152, 2018: 61-93
Presentación No. 152
Authors: Editor El
Published: June 2019

Realidad: Revista de Ciencias Sociales y Humanidades No. 152, 2018: 1-3
Entrevista con Ricardo Espinoza Lolas
Authors: Brito Marcela
Published: June 2019

No abstract available
Realidad: Revista de Ciencias Sociales y Humanidades No. 152, 2018: 147-172
Retos y oportunidades de la integración centroamericana
Authors: Lazo Miguel A.
Published: June 2019

No abstract available
Realidad: Revista de Ciencias Sociales y Humanidades No. 152, 2018: 183-191
Editorial
Authors: Freitas Maria do Carmo Duarte , Chagas Vera Lucia Bello
Published: June 2019

Este número da Revista AtoZ - novas práticas em informação e conhecimento propõe consolidar a busca de aquisição de competências colaborativas de professores de onze universidades brasileiras associados à Rede de Gestão da Informação e do Conhecimento (Rede GIC). O volume 8 número 1, apresenta um novo horizonte no campo da investigação em gestão da informação como consequência e influenciado pela globalização. Expõe 8 (oito) propostas de pesquisa de mestrado em andamento discutidas pelos autores e professores participantes do II Consórcio Mestral e Doutoral da Rede GIC ocorrido em Curitiba, de 27 a 29 de junho de 2018. Boa leitura!
Gestão das informações sobre treinamentos de agentes de segurança pública
Authors: Coelho Júnior João Daniel , Cândido Ana Clara
Published: June 2019

Os órgãos de segurança pública têm um papel fundamental na sociedade visando o bem-estar e segurança de todos. Os agentes de segurança pública devem ter competências (capacidades técnicas, físicas e psicológicas) em pleno vigor para servir a sociedade com êxito, sendo o treinamento continuado um elemento fundamental para toda sua vida laboral. A falta de treinamento constante destes agentes poderá gerar grandes perdas, incluindo o bem mais precioso: a vida. O objetivo geral deste projeto é estudar as formas de gerenciamento das informações de treinamento continuado num órgão de segurança pública federal específico, bem como propor um sistema de informação que auxilie os gestores no controle e auxilie em correções de eventuais falhas. Trata-se de uma pesquisa exploratória para diagnosticar e identificar a atual situação de um órgão federal de segurança quanto ao registro e gestão das informações de treinamento dos servidores. Na etapa de diagnóstico serão realizadas entrevistas semiestruturada com gestores de órgãos centrais em Brasília responsáveis pelas capacitações e desenvolvimento tecnológico. Além disto, a aplicação de questionário estruturado com perguntas fechadas e abertas, com foco em algumas unidades regionais (superintendências) por amostra. Um estudo de benchmarking estadual é pretendido também na etapa inicial. A revisão teórica do projeto centra-se em descrever fluxos informacionais de Gestão de Pessoas por Competências e a relação com os conceitos de “informação-como-processo” e “informação-como- conhecimento”, e os paradigmas cognitivo e social. Espera-se com a conclusão deste estudo, contribuir para a aplicação da gestão da informação nos treinamentos de órgãos de segurança pública, indicando práticas e instrumentos para a sua implementação.
Modelo teórico de avaliação da gestão da informação nos processos logísticos e de gestão da cadeia de suprimentos nas instituições federais de ensino superior brasileiras
Authors: Belinski Ricardo , Frederico Guilherme Francisco
Published: June 2019

A gestão da informação pode dar suporte às decisões na gestão logística da cadeia de suprimentos de material de consumo nas Instituições de Ensino Superior Federais Brasileiras, a partir da análise dos relatórios de auditoria. A gestão de suprimentos demanda tomada de decisão a partir de informações ao longo das atividades em toda sua cadeia. Na gestão pública, a documentação de processos é prioridade, pois cabe ao gestor fazer apenas aquilo que está previsto na jurisprudência, sobretudo nas regras de licitação. Neste estudo de caso, serão analisados 20 aspectos na gestão de suprimentos das IFES a partir de documentos emitidos pelo Tribunal de Contas da União e pela auditoria interna das IFES. A validação das práticas será feita pela análise de conteúdo de relatórios de auditoria. A amostra será composta pelas IFES brasileiras que foram avaliadas nos últimos anos pelos órgãos de controle externo, incluindo documentos de levantamento das aquisições e apuração de indícios de fraudes na licitação. Entre os resultados esperados está um modelo de gestão da informação aplicada à gestão da cadeia de suprimentos que pode ser aplicada às centenas de Instituições Federais de Ensino Superior brasileiras, no contexto da licitação.
Avaliação por competência em cursos online abertos e massivos por meio de rubrica
Authors: Salerno Byanca Neumann , Freitas Maria do Carmo Duarte
Published: June 2019

O desenvolvimento das tecnologias da informação e seu uso na área da educação contextualiza o surgimento dos Cursos Online Abertos e Massivos caracterizados pela ausência de pré-requisitos para ingresso e quantidade ilimitada de estudantes. Um dos problemas enfrentados nesses cursos é a alta taxa de evasão dos estudantes devido à falta de engajamento. Além disso, há dificuldade para avaliar a aprendizagem e garantir que as competências esperadas com o curso sejam desenvolvidas. Esta pesquisa tem como objetivo formular diretrizes para o desenvolvimento de conteúdos em MOOCs com a utilização de rubricas de avaliação por competências. A metodologia é de revisão sistemática a partir de pesquisa bibliográfica e análise de conteúdo sobre as práticas e estratégias educacionais utilizadas no ensino superior e nos Cursos Online Abertos e Massivos. Os resultados parciais mostram que a avaliação é capaz de motivar o engajamento dos estudantes e que grandes provedores de MOOCs como o Coursera e o edX têm investido em tecnologias automáticas para esse fim. No entanto, essas tecnologias não avaliam os estudantes a partir de competências. Portanto, elas não são suficientes para substituir a avaliação formativa baseada em competências que o professor é capaz de proporcionar por meio de rubricas.