bluuebunny/crossref_metadata_2025 huggingface dataset:
2005–2017 ozone trends and potential benefits of local measures as deduced from air quality measurements in the north of the Barcelona metropolitan area
Authors: Massagué Jordi , Carnerero Cristina , Escudero Miguel , Baldasano José María , Alastuey Andrés , Querol Xavier
Published: June 2019

Abstract. We analyzed 2005–2017 data sets on ozone (O3)
concentrations in an area (the Vic Plain) frequently affected by the
atmospheric plume northward transport of the Barcelona metropolitan area
(BMA), the atmospheric basin of Spain recording the highest number of
exceedances of the hourly O3 information threshold
(180 µg m−3). We aimed at evaluating the potential benefits
of implementing local-BMA short-term measures to abate emissions of
precursors. To this end, we analyzed in detail spatial and time variations of
concentration of O3 and nitrogen oxides (NO and NO2,
including OMI remote sensing data for the latter). Subsequently, a
sensitivity analysis is done with the air quality (AQ) data to evaluate
potential O3 reductions in the north of the BMA on Sundays compared
with weekdays as a consequence of the reduction in regional emissions of
precursors. The results showed a generalized decreasing trend for regional background
O3 as well as the well-known increase in urban O3 and higher
urban NO decreasing slopes compared with those of NO2. The most
intensive O3 episodes in the Vic Plain are caused by (i) a relatively
high regional background O3 (due to a mix of continental,
hemispheric–tropospheric and stratospheric contributions); by (ii) intensive
surface fumigation from mid-troposphere high O3 upper layers arising
from the concatenation of the vertical recirculation of air masses; but also
by (iii) an important O3 contribution from the northward
transport/channeling of the pollution plume from the BMA. The high relevance
of the local-daily O3 contribution during the most intense pollution
episodes is clearly supported by the O3 (surface concentration) and
NO2 (OMI data) data analysis. A maximum decrease potential (by applying short-term measures to abate
emissions of O3 precursors) of 49 µg O3 m−3
(32 %) of the average diurnal concentrations was determined. Structurally
implemented measures, instead of episodically, could result in important
additional O3 decreases because not only the local O3 coming
from the BMA plume would be reduced, but also the recirculated O3 and
thus the intensity of O3 fumigation in the plain. Therefore, it is
highly probable that both structural and episodic measures to abate
NOx and volatile organic compound (VOC) emissions in the BMA
would result in evident reductions of O3 in the Vic Plain.
Skin temperature from the Thermal Infrared Sounder IASI
Authors: Safieddine Sarah , Parracho Ana Claudia , George Maya , Aires Filipe , Pellet Victor , Clarisse Lieven , Whitburn Simon , Lezeaux Olivier , Thepaut Jean-Noel , Hersbach Hans , Radnoti Gabor , Goettsche Frank , Martin Maria , Doutriaux Boucher Marie , Coppens Dorothee , August Thomas , Clerbaux Cathy
Published: June 2019

Abstract. Skin temperature (Tskin) derived from infrared sensors on board satellites provides a continuous view of Earth’s surface day and night and allows for the monitoring of global temperature changes relevant for climate trends. Tskin from the Infrared Atmospheric Sounding Interferometer (IASI) has not been properly exploited to date to assess its long-term spatio-temporal variability and no current homogenous Tskin record from IASI exists. In this study, we present a fast retrieval method of Tskin based on an artificial neural network from a set of IASI channels selected using the information theory/entropy reduction technique. We compare and validate our IASI Tskin product with that from EUMETSAT Level 2, ECMWF Reanalysis ERA5, SEVIRI land-surface temperature products, as well as ground measurements. Our results show good correlation between the IASI neural network product and the datasets used for validation, with a standard deviation between 1 and 4 °C. This method can be applied to other infrared measurements, and allows for the construction of a robust Tskin dataset, making it suitable for trend analysis.
Gas exchange estimates in the Peruvian upwelling regime biased by multi-day near-surface stratification
Authors: Fischer Tim , Kock Annette , Arévalo-Martínez Damian L. , Dengler Marcus , Brandt Peter , Bange Hermann W.
Published: June 2019

Abstract. The coastal upwelling regime off Peru in December 2012 showed considerable
vertical concentration gradients of dissolved nitrous oxide (N2O)
across the top few meters of the ocean. The gradients were predominantly
downward, i.e., concentrations decreased toward the surface. Ignoring these
gradients causes a systematic error in regionally integrated gas exchange
estimates, when using observed concentrations at several meters below the
surface as input for bulk flux parameterizations – as is routinely practiced.
Here we propose that multi-day near-surface stratification events are
responsible for the observed near-surface N2O gradients, and that the
gradients induce the strongest bias in gas exchange estimates at winds of
about 3 to 6 m s−1. Glider hydrographic time series reveal that
events of multi-day near-surface stratification are a common feature in the
study region. In the same way as shorter events of near-surface
stratification (e.g., the diurnal warm layer cycle), they preferentially exist
under calm to moderate wind conditions, suppress turbulent mixing, and thus
lead to isolation of the top layer from the waters below (surface trapping).
Our observational data in combination with a simple gas-transfer model of the
surface trapping mechanism show that multi-day near-surface stratification
can produce near-surface N2O gradients comparable to observations.
They further indicate that N2O gradients created by diurnal or
shorter stratification cycles are weaker and do not substantially impact bulk
emission estimates. Quantitatively, we estimate that the integrated bias for
the entire Peruvian upwelling region in December 2012 represents an
overestimation of the total N2O emission by about a third, if
concentrations at 5 or 10 m depth are used as surrogate for bulk water
N2O concentration. Locally, gradients exist which would lead to
emission rates overestimated by a factor of two or more. As the Peruvian
upwelling region is an N2O source of global importance, and other
strong N2O source regions could tend to develop multi-day
near-surface stratification as well, the bias resulting from multi-day
near-surface stratification may also impact global oceanic N2O
emission estimates.
How does the downstream boundary affect avulsion dynamics in a laboratory bifurcation?
Authors: Salter Gerard , Voller Vaughan R. , Paola Chris
Published: June 2019

Abstract. Bifurcations play a major role in the evolution of landscapes by controlling how fluxes such as water and sediment are partitioned in distributary and multi-thread channel networks. In this paper, we present the first experimental investigation on the effect of the downstream boundary on bifurcations. Our experiments in a fixed-wall Y-shaped flume consist of three phases: progradation, transitional, and bypass; the first two phases are depositional, whereas during the third, the sediment flux exiting the downstream boundary matches the input on average. We find that deposition qualitatively changes bifurcation dynamics; we observe frequent switching in the discharge partitioning under depositional conditions, whereas bypass results in long periods of time where one branch captures most of the flow. We compare our results with a previously developed model for the effect of deposition on bifurcation dynamics. The switching dynamics we observe are more irregular and complex than those predicted by the model. Furthermore, while we observe long periods of time where one branch dominates under bypass conditions, these are not permanent, unlike in the model. We propose that the range of switching timescales we observe arises from a complex interplay of downstream-controlled avulsion, and the effect of bars in the upstream-channel, including previously unrecognized long-timescale bar dynamics. Finally, we describe bifurcation experiments conducted with sand but no water. These experiments share the essential feedbacks of our fluvial bifurcation experiments, but do not include bars. In these experiments, we find that the sandpile grows symmetrically while it progrades, but bypass leads to one branch permanently capturing all avalanches. We conclude that the downstream control of deposition vs. bypass is likely a major influence on bifurcation dynamics across a range of physical systems, from river deltas to talus slopes.
FAST PAIRWISE COARSE REGISTRATION BETWEEN POINT CLOUDS OF CONSTRUCTION SITES USING 2D PROJECTION BASED PHASE CORRELATION
Authors: Huang R. , Ye Z. , Boerner R. , Yao W. , Xu Y. , Stilla U.
Published: June 2019

Abstract. For conducting change detection using 3D scans of a construction site, the registration between point clouds at different acquisition times is normally necessary. However, due to the complexity of constructing areas, the automatic registration of temporal scans is a challenging problem. In this work, we propose a fast and maker-free method for coarse registration between point clouds by converting the 3D matching problem into a 2D correlation problem, taking the special properties of building structures into consideration. Our proposed method consists of two major steps: the conversion from 3D points to 2D image data and the estimation of transformation parameters between 2D images in the frequency domain. In the conversion step, the point cloud of each scan is projected into a 2D grey image, by which the ground footprint of the point cloud is obtained. In the following step, we represent the 2D image in frequency-domain and estimate the horizontal transformation parameters by using Fourier-Mellin transformation. A real application is performed to validate the feasibility and effectiveness of our workflow using photogrammetric point clouds of a construction site in two different acquisition time. Regarding the real application of coarse registration of point clouds, our proposed method can achieve a registration error of less than 1 degree and more efficient than the classical baseline methods for the fast orientation between scans.
A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION
Authors: Hui Z. , Cheng P. , Wang L. , Xia Y. , Hu H. , Li X.
Published: June 2019

Abstract. Denoising is a key pre-processing step for many airborne LiDAR point cloud applications. However, the previous algorithms have a number of problems, which affect the quality of point cloud post-processing, such as DTM generation. In this paper, a novel automated denoising algorithm is proposed based on empirical mode decomposition to remove outliers from airborne LiDAR point cloud. Comparing with traditional point cloud denoising algorithms, the proposed method can detect outliers from a signal processing perspective. Firstly, airborne LiDAR point clouds are decomposed into a series of intrinsic mode functions with the help of morphological operations, which would significantly decrease the computational complexity. By applying OTSU algorithm to these intrinsic mode functions, noise-dominant components can be detected and filtered. Finally, outliers are detected automatically by comparing observed elevations and reconstructed elevations. Three datasets located at three different cities in China were used to verify the validity and robustness of the proposed method. The experimental results demonstrate that the proposed method removes both high and low outliers effectively with various terrain features while preserving useful ground details.
JOINT CLASSIFICATION OF ALS AND DIM POINT CLOUDS
Authors: Politz F. , Sester M.
Published: June 2019

Abstract. National mapping agencies (NMAs) have to acquire nation-wide Digital Terrain Models on a regular basis as part of their obligations to provide up-to-date data. Point clouds from Airborne Laser Scanning (ALS) are an important data source for this task; recently, NMAs also started deriving Dense Image Matching (DIM) point clouds from aerial images. As a result, NMAs have both point cloud data sources available, which they can exploit for their purposes. In this study, we investigate the potential of transfer learning from ALS to DIM data, so the time consuming step of data labelling can be reduced. Due to their specific individual measurement techniques, both point clouds have various distinct properties such as RGB or intensity values, which are often exploited for classification of either ALS or DIM point clouds. However, those features also hinder transfer learning between these two point cloud types, since they do not exist in the other point cloud type. As the mere 3D point is available in both point cloud types, we focus on transfer learning from an ALS to a DIM point cloud using exclusively the point coordinates. We are tackling the issue of different point densities by rasterizing the point cloud into a 2D grid and take important height features as input for classification. We train an encoder-decoder convolutional neural network with labelled ALS data as a baseline and then fine-tune this baseline with an increasing amount of labelled DIM data. We also train the same network exclusively on all available DIM data as reference to compare our results. We show that only 10% of labelled DIM data increase the classification results notably, which is especially relevant for practical applications.
IMAGE-BASED VEHICLE TRACKING FROM ROADSIDE LIDAR DATA
Authors: Zhang J. , Xiao W. , Coifman B. , Mills J. P.
Published: June 2019

Abstract. Vehicle tracking is of great importance in urban traffic systems, and the adoption of lidar technologies – including on-board and roadside systems – has significant potential for such applications. This research therefore proposes and develops an image-based vehicle-tracking framework from roadside lidar data to track the precise location and speed of a vehicle. Prior to tracking, vehicles are detected in point clouds through a three-step procedure. Cluster tracking then provides initial tracking results. The second tracking stage aims to provide more precise results, in which two strategies are developed and tested: frame-by-frame and model-matching strategies. For each strategy, tracking is implemented through two threads by converting the 3D point cloud clusters into 2D images relating to the plan and side views along the tracked vehicle’s trajectory. During this process, image registration is exploited in order to retrieve the transformation parameters between every image pair. Based on these transformations, vehicle speeds are determined directly based on (a) the locations of the chosen tracking point in the first strategy; (b) a vehicle model is built and tracking point locations can be calculated after matching every frame with the model in the second strategy. In contrast with other existing methods, the proposed method provides improved vehicle tracking via points instead of clusters. Moreover, tracking in a decomposed manner provides an opportunity to cross-validate the results from different views. The effectiveness of this method has been evaluated using roadside lidar data obtained by a Robosense 32-line laser scanner.
THE JOINT RESEARCH PROJECT ANKOMMEN – EXPLORATION USING AUTOMATED UAV AND UGV
Authors: Alamouri A. , Gerke M. , Batzdorfer S. , Becker M. , Bestmann U. , Bobbe M. , Khedar Y. , Blume T. , Schattenberg J. , Schmiemann J.
Published: June 2019

Abstract. Disasters such as floods, large fires, landslides, avalanches, or forest fires are often inevitable and cannot be fully prevented, but their impact can be minimized with sound disaster management strategies aided by the latest technological advancements. A key factor affecting these strategies is the time, where any delay can result in dramatic consequences and potentially human losses. Therefore, a quick situation report of the disaster is highly demanded, but still not an easy task because - in most cases - a priori known spatial information like map data or geo-databases, are outdated. In addition, visual and geometric information on the current situation is needed to help rescue teams and first responders. From this point of view, we came up to the main idea of the joint research project ANKommEn and its extension ANKommEn 2 (german acronym for Automated Navigation and Communication for Exploration). The project idea embodies an exploratory investigation to be smart in providing correct and timely geodata that can help in emergency cases; especially in support decision making in emergency risk management. For this purpose, automated unmanned systems, both ground (UGV) and airborne (UAV), are being developed to provide up-to-date information of rescue scenarios.
ESTABLISHING NEW FOUNDATIONS FOR THE USE OF REMOTELY-PILOTED AIRCRAFT SYSTEMS FOR CIVILIAN APPLICATIONS
Authors: Balsi M. , Prem S. , Williame K. , Teboul D. , Délétraz L. , Hebrard Capdeville P. I.
Published: June 2019

Abstract. Skyopener is a project funded by the EU through the European GNSS Agency (GSA) in the framework of the Horizon 2020 program. Skyopener’s goal is contributing to the roadmap for the integration of civil Remotely Piloted Aircraft Systems (RPAS) into nonsegregated airspace, by providing and testing enabling technologies, in particular with reference to European initiative U-Space, aimed at establishing regulations and infrastructure for integration of unmanned aviation into shared airspace. The main outcomes of the project include: implementing and testing a reliable and secure redundant air-ground communication link, based on satellite and 3G/4G networks; integrating the mission management system and ground station with a UTM (Unmanned aerial system Traffic Management) client, and experimenting UTM services being deployed by one of the partners; demonstrating technical and economic feasibility of long- range missions beyond visual line of sight (BVLOS) by executing corridor mapping on a high-voltage powerline, and airport area surveys (e-TOD: electronic-Terrain Obstacle Database).
TOWARDS POST-DISASTER DEBRIS IDENTIFICATION FOR PRECISE DAMAGE AND RECOVERY ASSESSMENTS FROM UAV AND SATELLITE IMAGES
Authors: Ghaffarian S. , Kerle N.
Published: June 2019

Abstract. Often disasters cause structural damages and produce rubble and debris, depending on their magnitude and type. The initial disaster response activity is evaluation of the damages, i.e. creation of a detailed damage estimation for different object types throughout the affected area. First responders and government stakeholders require the damage information to plan rescue operations and later on to guide the recovery process. Remote sensing, due to its agile data acquisition capability, synoptic coverage and low cost, has long been used as a vital tool to collect information after a disaster and conduct damage assessment. To detect damages from remote sensing imagery (both UAV and satellite images) structural rubble/debris has been employed as a proxy to detect damaged buildings/areas. However, disaster debris often includes vegetation, sediments and relocated personal property in addition to structural rubble, i.e. items that are wind- or waterborne and not necessarily associated with the closest building. Traditionally, land cover classification-based damage detection has been categorizing debris as damaged areas. However, in particular in waterborne disaster such as tsunamis or storm surges, vast areas end up being debris covered, effectively hindering actual building damage to be detected, and leading to an overestimation of damaged area. Therefore, to perform a precise damage assessment, and consequently recovery assessment that relies on a clear damage benchmark, it is crucial to separate actual structural rubble from ephemeral debris. In this study two approaches were investigated for two types of data (i.e., UAV images, and multi-temporal satellite images). To do so, three textural analysis, i.e., Gabor filters, Local Binary Pattern (LBP), and Histogram of the Oriented Gradients (HOG), were implemented on mosaic UAV images, and the relation between debris type and their time of removal was investigated using very high-resolution satellite images. The results showed that the HOG features, among other texture features, have the potential to be used for debris identification. In addition, multi-temporal satellite image analysis showed that debris removal time needs to be investigated using daily images, because the removal time of debris may change based on the type of disaster and its location.
AUTOMATIC GENERATION OF GEOMETRIC PARAMETERS OF INDIVIDUAL CAULIFLOWER PLANTS FOR RAPID PHENOTYPING USING DRONE IMAGES
Authors: Grenzdörffer G. J.
Published: June 2019

Abstract. Multitemporal drone surveys are a perfect tool to determine various geometric and spectral crop parameters for rapid phenotyping in field trials. Depending on the geometric resolution and the size of the crop, information at leaf level or canopy level can be obtained. The focus of this paper is to demonstrate which geometric properties can be automatically derived from high resolution drone imagery during the vegetation period. For this research approx. 1920 cauliflower with a large genetic variety were planted and monitored by five different drone surveys at an altitude of 20 m, using a high resolution 36 Mpix. RGB-camera. In order to minimize intensive radiometric calibration, BRDF effects and eliminate shade, flights were carried out at overcast skies. After photogrammetric image processing, detailed crop height models (CHM) were computed. 10 distinct crop parameters were derived from a combination of the orthophotos, the CHM and additional information. According to the phenological phase a specific set of parameters was developed for every flight. For instance, the position of the individual plants is computed right after the first flight. For the flight prior to harvesting, an algorithm for the head diameter and the curvature of the cauliflower heads was developed. Geometric parameters are generally better suited for automation, because they require less specific ground truth or reference information, than spectrally derived biophysical parameters.
A REAL-TIME DRONE MAPPING PLATFORM FOR MARINE SURVEILLANCE
Authors: Jeon I. , Ham S. , Cheon J. , Klimkowska A. M. , Kim H. , Choi K. , Lee I.
Published: June 2019

Abstract. Marine incidents have caused serious casualties and damaged on property, and situational awareness and actions are needed to reduce further extensive damage for marine surveillance. The importance of an attempt for maritime monitoring using UAV has been raised, and a platform should be prepared to respond immediately to urgent situations. In this research, a real-time drone image mapping platform is proposed for marine surveillance that receives marine images acquired and transmitted by drones and processes them in real time. The platform proposed in this study is divided into 1) UAV System, 2) Real-time image processing, 3) Visualization. UAV system transfers data from a drone to the ground stations. Real-time image processing module generates individual orthophotos followed by directly georeferencing in real time and detecting ships on the orthophotos. Visualization module enables to visualize the orthophotos. The overall mapping time of 3.26 seconds on average was verified for processing image mapping, and ship detection time for a single image was estimated to be within about 1 second, which corresponds to an environment in which an emergency must be handled. In conclusion, a real-time drone mapping platform that is introduced in this study can be evaluated as being available for maritime monitoring that requires swift responses.
UAV-BASED OBLIQUE PHOTOGRAMMETRY FOR 3D RECONSTRUCTION OF TRANSMISSION LINE: PRACTICES AND APPLICATIONS
Authors: Jiang S. , Jiang W.
Published: June 2019

Abstract. Regular inspection of transmission line is an essential work to ensure reliable supply and distribution of electric power. Considering that the conventional time consuming and economic inefficient methods can not satisfy new coming demands, an inspection strategy is proposed in this study, which aims to achieve off-site visual inspection instead of in-site inspection. The most challenging task is 3D model reconstruction of transmission line. Therefore, based on oblique imaging technique and the flexibility of UAV platforms, an UAV-Based oblique photogrammetric system is designed and manufactured for data acquisition of transmission line. The system integrates a dual-camera imaging system, an onboard GNSS (Global Navigation Satellite System) receiver and a ground fixed GNSS station. Beside, using open-source and commercial software packages, a data processing pipeline is also proposed for 3D model reconstruction. The experimental results demonstrate that the UAV-based oblique imaging system could obtain images of power pylons in four different directions and provides enough information for the successful model reconstruction. Without GCPs, the dual-strip flight mode is recommended for flight configuration to achieve high geo-referencing accuracy. Compared with the traditional methods, the proposed solution can achieve off-site inspection of transmission line.
IMPROVING 3D MESH QUALITY USING MULTI-DIRECTIONAL UAV IMAGES
Authors: Kang J. , Lee S. , Yeon S. , Park S.
Published: June 2019

Abstract. With the increasing use of 3D spatial information, updated 3D spatial information is required and unmanned aerial vehicles (UAVs) are being used to accomplish this task. In general, 3D spatial information is constructed by generating meshes through point clouds extracted from many images. However, generating a mesh that is comparable to a real building is difficult because UAV images use relatively cheap sensors compared to aerial images. In this study, Nadir images with various overlapping degrees were obtained and point clouds generated to compare the reproducibility of actual buildings. This led to the confirmation that the use of Nadir images alone does not fully complement the occlusion even when the images are taken with high overlapping degrees. To complement such limitations of unidirectional images, oblique images were used in addition to create point clouds, and the occlusion was found to have been complemented. Furthermore, through its conversion, high quality 3D spatial information data could be obtained.
COMPUTATIONAL TIME MODELLING FOR TREE CROWN EXTRACTION FROM HIGH RESOLUTION IMAGERY USING GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS
Authors: Okojie J. A. , Effiom A. E. , Tawiah E. N. , Akpejiori I. J.
Published: June 2019

Abstract. Image segmentation, unlike most conventional approaches, assesses image pixel information in relation to image contextual information to extract congruent image objects. A comprehensive review of literature during the research conceptualization phase revealed that while a lot of work has been done into the optimization of the segmentation computational time, there was a conspicuous research gap in the area of computational time modelling and pre-analytical projections.The focus of this research was, therefore, to model the computational time requirements of segmentation as a function of variability in image spatial resolutions and pixel counts. The employed methodology integrated structure from motion (SFM), segmentation, and regression modelling into a multiphase workflow to process UAV-acquired imagery into an orthomosaic, which underwent segmentation at multiple resolutions. The segmentation computational time was regressed against the image pixel count and spatial resolution.The results showed that the image pixel count was the main determinant of the computational time, expressing a statistically strong linear relationship. However, when the regressors were tested individually, the spatial resolution showed an exponential relationship with computational time which, however, exhibited saturation. It can be inferred that image spatial resolution is not the key determinant of segmentation computational time but the pixel count of the image.This research recommends the adoption of statistically viable models to thoroughly assess computational time requirements for image segmentation and a pre-analytics estimation of the time requirements such that image quality – image processing time adjustments can be made before rather than after analyses have commenced.
PHOTOGRAMMETRIC ASSESSMENT AND COMPARISON OF DJI PHANTOM 4 PRO AND PHANTOM 4 RTK SMALL UNMANNED AIRCRAFT SYSTEMS
Authors: Peppa M. V. , Hall J. , Goodyear J. , Mills J. P.
Published: June 2019

Abstract. Consumer-grade Unmanned Aircraft Systems (UAS), and particularly Small Unmanned Aircraft (SUA) weighing less than 20 kg, have recently become very attractive for photogrammetric data acquisition across a wide range of applications. Compared to other more expensive remote-sensing technology, DJI Phantom series SUA provide a trade-off between cost, sensor quality, functionality and portability. Because of the significant interest in such systems, rigorous accuracy assessment of metric performance is crucial. This research investigates the capabilities of the Phantom 4 Pro (P4P) and the recently launched Phantom 4 RTK (P4RTK) SUA through both laboratory and in-situ assessments with multi-scale photogrammetric blocks. The study adopts self-calibrating bundle adjustments from conventional photogrammetry and from a Structure-from-Motion (SfM)-photogrammetric approach. Both systems deliver planimetric and vertical absolute accuracies of better than one and two pixels ground sampling distance, respectively, against independent check points. This can be achieved if the imaging network configuration includes a mixed range of nadir and oblique imagery and several ground control points are established as reference information. Ongoing analysis is investigating the strength of all bundle adjustment solutions. It is also evaluating the GNSS capabilities of the P4RTK SUA after post-processing raw observations of its trajectory. Findings from a comprehensive accuracy assessment can support non-experts in designing the pre-flight photogrammetric data acquisition plan and aid understanding of the performance of such popular off-the-shelf SUA.
URBAN SCENE CLASSIFICATION USING FEATURES EXTRACTED FROM PHOTOGRAMMETRIC POINT CLOUDS ACQUIRED BY UAV
Authors: Pessoa G. G. , Santos R. C. , Carrilho A. C. , Galo M. , Amorim A.
Published: June 2019

Abstract. Images and LiDAR point clouds are the two major data sources used by the photogrammetry and remote sensing community. Although different, the synergy between these two data sources has motivated exploration of the potential for combining data in various applications, especially for classification and extraction of information in urban environments. Despite the efforts of the scientific community, integrating LiDAR data and images remains a challenging task. For this reason, the development of Unmanned Aerial Vehicles (UAVs) along with the integration and synchronization of positioning receivers, inertial systems and off-the-shelf imaging sensors has enabled the exploitation of the high-density photogrammetric point cloud (PPC) as an alternative, obviating the need to integrate LiDAR and optical images. This study therefore aims to compare the results of PPC classification in urban scenes considering radiometric-only, geometric-only and combined radiometric and geometric data applied to the Random Forest algorithm. For this study the following classes were considered: buildings, asphalt, trees, grass, bare soil, sidewalks and power lines, which encompass the most common objects in urban scenes. The classification procedure was performed considering radiometric features (Green band, Red band, NIR band, NDVI and Saturation) and geometric features (Height – nDSM, Linearity, Planarity, Scatter, Anisotropy, Omnivariance and Eigenentropy). The quantitative analyses were performed by means of the classification error matrix using the following metrics: overall accuracy, recall and precision. The quantitative analyses present overall accuracy of 0.80, 0.74 and 0.98 for classification considering radiometric, geometric and both data combined, respectively.
TRANSLATING AERIAL IMAGES INTO STREET-MAP-LIKE REPRESENTATIONS FOR VISUAL SELF-LOCALIZATION OF UAVS
Authors: Schleiss M.
Published: June 2019

Abstract. Unmanned aerial vehicles (UAVs) rely on global navigation satellite systems (GNSS) like the Global Positioning System (GPS) for navigation but GNSS signals can be easily jammed. Therefore, we propose a visual localization method that uses a camera and data from Open Street Maps in order to replace GNSS. First, the aerial imagery from the onboard camera is translated into a map-like representation. Then we match it with a reference map to infer the vehicle’s position. An experiment over a typical sized mission area shows localization accuracy close to commercial GPS. Compared to previous methods ours is applicable to a broader range of scenarios. It can incorporate multiple types of landmarks like roads and buildings and it outputs absolute positions with higher frequency and confidence and can be used at altitudes typical for commercial UAVs. Our results show that the proposed method can serve as a backup to GNSS systems where suitable landmarks are available.
UAV-BASED CADASTRAL MAPPING: AN ASSESSMENT OF THE IMPACT OF FLIGHT PARAMETERS AND GROUND TRUTH MEASUREMENTS ON THE ABSOLUTE ACCURACY OF DERIVED ORTHOIMAGES
Authors: Stöcker C. , Nex F. , Koeva M. , Gerke M.
Published: June 2019

Abstract. In the domain of land administration, UAV-based orthophotos are gaining in importance as base data to support the extraction of cadastral boundaries and further visual interpretation, manual digitization, or automated feature detection procedures. However, the fact that UAV-derived geographical information can support decision-making processes that involve people’s land rights ultimately raises questions about the quality of the respective data. Especially geometric accuracy and radiometry can be negatively influenced by poor flight planning, densely populated areas, and adverse meteorological conditions. Thus, this paper takes a closer look at optimal workflows to minimize the need for ground truthing by presenting an experimental assessment of flight parameters and ground truth methods. More than 40 datasets entail the representative basis to investigate the impact on the absolute geometric accuracy of derived UAV-based orthomosaics. Results suggest that UAV data acquisition workflows can cover a wide range of data quality depending on UAV equipment, flight planning parameters and ground truthing strategies. Ultimately, this paper can help to determine the best approach to provide a high-quality data product that satisfies end user and supports the provision of reliable base data for automated or manual extraction of cadastral boundaries.
ESTIMATING CROP DENSITY FROM MULTI-SPECTRAL UAV IMAGERY IN MAIZE CROP
Authors: Stroppiana D. , Pepe M. , Boschetti M. , Crema A. , Candiani G. , Giordan D. , Baldo M. , Allasia P. , Monopoli L.
Published: June 2019

Abstract. In this study we exploit UAV data for estimating Fractional Vegetation Cover (FVC) of maize crop at the early stages of the growing season. UAV survey with a MicaSense RedEdge multispectral sensor was carried out on July 13th, 2017 over a maize field in Italy; simultaneous RGB in situ pictures were collected to build a reference dataset of FVC over 15 ESU (Elementary Sampling Units) distributed over the field under investigation. The approach proposed for classification of UAV data is based on local contrast enhancement techniques applied to a vegetation index (NDVI-Normalized Difference Vegetation Index) to capture signal from small plants at the early development stage. The output fc map is obtained over grid cells over 70 × 70 cm size. The approach proposed here, based on contextual analysis, reduced artefacts due to illumination conditions by better enhancing signal from vegetation compared to, for example, simple band combination such as vegetation index alone (e.g. NDVI). Validation accomplished by a point comparison between estimated (from UAV) and in situ measured FVC values provided R2 = 0.73 and RMSE = 6%.
INDIVIDUAL TREE DETECTION FROM UAV LIDAR DATA IN A MIXED SPECIES WOODLAND
Authors: Zaforemska A. , Xiao W. , Gaulton R.
Published: June 2019

Abstract. The study evaluates five existing segmentation algorithms to determine the method most suitable for individual tree detection across a species-diverse forest: raster-based region growing, local maxima centroidal Voronoi tessellation, point-cloud level region growing, marker controlled watershed and continuously adaptive mean shift. Each of the methods has been tested twice over one mixed and five single species plots: with their parameters set as constant and with the parameters calibrated for every plot. Overall, continuous adaptive mean shift performs best across all the plots with average F-score of 0.9 with fine-tuned parameters and 0.802 with parameters held at constant. Raster-based algorithms tend to achieve higher scores in coniferous plots, due to the clearly discernible tops, which significantly aid the detection of local maxima. Their performance is also highly dependent on the moving size window used to detect the local maxima, which ideally should be readjusted for every plot. Crown overlap, suppressed and leaning trees are the most likely sources of error for all the algorithms tested.
IMPROVING AUTOMATIC RECONSTRUCTION OF INTERIOR WALLS FROM POINT CLOUD DATA
Authors: Maset E. , Magri L. , Fusiello A.
Published: June 2019

Abstract. In this paper we deal with the automatic reconstruction of interior walls from point clouds, an active research topic with several practical applications. We propose an improved version of the method presented in (Magri and Fusiello, 2018), where the overall structure of the environment is extracted by fitting lines to the main building features, using Min-hashed J-Linkage as a multi-model fitting technique. Our variation has the merit of producing more accurate results, both in terms of wall reconstruction and room segmentation, and greatly reducing the need for user-defined thresholds.
AUGMENTED ANNOTATIONS: INDOOR DATASET GENERATION WITH AUGMENTED REALITY
Authors: Saran V. , Lin J. , Zakhor A.
Published: June 2019

Abstract. The proliferation of machine learning applied to 3D computer vision tasks such as object detection has heightened the need for large, high-quality datasets of labeled 3D scans for training and testing purposes. Current methods of producing these datasets require first scanning the environment, then transferring the resulting point cloud or mesh to a separate tool for it to be annotated with semantic information, both of which are time consuming processes. In this paper, we introduce Augmented Annotations, a novel approach to bounding box data annotation that solves the scanning and annotation processes of an environment in parallel. Leveraging knowledge of the user’s position in 3D space during scanning, we use augmented reality (AR) to place persistent digital annotations directly on top of indoor real world objects. We test our system with seven human subjects, and demonstrate that this approach can produce annotated 3D data faster than the state-of-the-art. Additionally, we show that Augmented Annotations can also be adapted to automatically produce 2D labeled image data from many viewpoints, a much needed augmentation technique for 2D object detection and recognition. Finally, we release our work to the public as an open-source iPad application designed for efficient 3D data collection.
THE AUTOMATIC GENERATION OF AN ADAPTIVE NAVIGATION MODEL FOR INDOOR MAP MATCHING
Authors: Wang P. , Shang J. , Zhou Z. , Wu Y. , Sun W.
Published: June 2019

Abstract. Indoor map matching has been an important technique to improve the indoor localization accuracy because it takes the advantage of available indoor building data and effectively decreases the localization cost. One of the spatial model involved in the indoor map matching, adaptive navigation model, could balance the accuracy and complexity of the model representation required by map matching by combining the medial axes and fine-grained grids according to the movement characteristics of pedestrians in open and narrow areas. In order to reduce the manual effort of producting a large number of models and update them, we propose an algorithm to automatically generate this model. Futermore, we use this algorithm to generate this model for three lab architectures, and the evaluation of the results of the ANM generated by the algorithm proves that the algorithm meets the requirements.
Open enough? Eight factors to consider when transitioning from closed to open resources and courses: A conceptual framework
Authors: McNally Michael B. , Christiansen Erik G.
Published: June 2019

Transitioning from closed courses and educational resources to open educational resources (OER) and open courseware (OCW) requires considerations of many factors beyond simply the use of an open licence. This paper examines the pedagogical choices and trade-offs involved in creating OER and OCW. Eight factors are identified that influence openness (open licensing, accessibility and usability standards, language, cultural considerations, support costs, digital distribution, and file formats). These factors are examined under closed, mixed and most open scenarios to relatively compare the amount of effort, willingness, skill and knowledge required. The paper concludes by suggesting that maximizing openness is not practical and argues that open educators should strive for ‘open enough’ rather than maximal openness.
La oferta y demanda de las universidades privadas en El Salvador
Authors: Fajardo Rodolfo Cornejo
Published: June 2019

La masificación y politización en el manejo de la enseñanza universitaria se han señalado como los factores del desprestigio, así como el bajo nivel académico en ciertas universidades privadas.La razón principal se desconoce, pero es evidente que un número de ellas ofrezcan más de lo que pueden dar.Por su magnitud este estudio representa la primera parte de una investigación permanente.Para el año dos mil uno se pretenden los siguientes planteamientos: Índice de satisfacción global en las universidades, Índice de satisfacción por servicio de los colegios particulares y nacionales, índice de satisfacción por carrera.
¿Existe un progreso moral en la historia?
Authors: Klappenbach Augusto
Published: June 2019

El mero hecho de plantear esta pregunta implica una serie de supuestos, cada uno de los cuales daría lugar a una larga discusión que, como suele suceder con este tipo de discusiones, terminaría sin acuerdos.Sin ánimo de entrar a fondo en ese debate, empecemos por recorrer algunos, si no para resolver los problemas, al menos para explicitar los inevitables prejuicios desde los cuales abordamos el tema.Y aclarando, desde el principio, que lo abordamos desde un punto de vista centrado en la cultura europea ­ occidental y, por tanto, adoptando -críticamente- los supuestos de la modernidad ilustrada y el concepto de razón que hemos heredado de ella. Lo cual no impide -más bien exige- un punto de vista universal.
Anime: sueños de narcisismo y omnipotencia
Authors: Molina Velásquez Carlos
Published: June 2019

No abstract available
Realidad: Revista de Ciencias Sociales y Humanidades No. 152, 2018: 173-182
Sobrecarga de informação na tomada de decisão: legitimação de escalas e sua relação com a necessidade de cognição do decisor
Authors: Vieira Icaro De Oliveira , Razzolini Filho Edelvino
Published: June 2019

O desafio que se vislumbra para o processo decisório é a percepção da quantidade de informação como uma variável a ser controlada. A tomada de decisão do gestor, por sua vez, se vincula a sua base cognitiva. O presente trabalho tem como objetivo a busca de evidências de validade psicométrica, através das técnicas critério e construto, de dois instrumentos psicométricos para a mensuração da sobrecarga de informação na tomada de decisão e eventuais relações com a necessidade de cognição do decisor. Para a análise, se pretende apreciar uma amostra formada por estudantes de pós-graduação. Para a legitimação dos instrumentos se utilizará a técnica estatística denominada análise fatorial por ela possuir a capacidade de determinar o grau de influência de uma dada variável na explicação de um fator. Para o tratamento dos dados, se utilizará de diversos métodos estatísticos. Este estudo busca encontrar evidências acerca do impacto que a variável cognição do gestor experimenta na presença de sobrecarga de informação. Suas principais contribuições seriam: (i) a verificação da existência de sobrecarga de informação na tomada de decisão e eventual relação com a necessidade de cognição do decisor; (ii) a mensuração da sobrecarga de informação e a necessidade de cognição do decisor em situações caracterizadas no âmbito organizacional; (iii) elaboração de um quadro conceitual por meio do qual seja possível a visualização de eventual relação entre o volume de informação e a necessidade de cognição do tomador de decisão.
COMPARISON OF DIFFERENT METHODS FOR GENERATION AND ABSORPTION OF WATER WAVES
Authors: Conde J. M. P.
Published: June 2019

The knowledge of water wave characteristics (generation, propagation, transformation and breaking) is fundamental for hydrodynamic studies and the design of ocean, coastal and port structures. In addition to the small-scale experimental studies, the use of numerical models is also a very important tool in hydrodynamic studies. To have reliable numerical results a proper validation is required. The main objective of this paper is to compare different methods of wave generation and wave absorption in a numerical flume, and to find what is the most suited to simulate non-breaking regular wave propagation in a two-dimensional flume in deep water condition. The numerical simulations were made using the OpenFOAM® software package. Two solvers, waves2Foam and IHFoam/OlaFlow, the utility GroovyBC and a mesh stretching technique are compared. These numerical codes solve the transient Navier-Stokes equations and use a VoF (Volume of Fluid) method to identify the free surface. A solution dependence study with the methods of wave generation and wave absorption is presented. The results are also compared with the theoretical wave and experimental data. The results show that the different methods of generation produce waves similar to the theoretical and the experimental ones, only slightly differences were visible. The three method of wave dissipation considered produce very different results: IHFoam/OlaFlow is not able to dissipate the wave tested; the mesh stretching technique is able to dissipate the waves but produces a water level rise; the waves2Foam solver is able to dissipate properly the wave tested.