bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection and Diagnosis faults in Machine asynchronous based on single processing
Authors: Chouidira Ibrahim , Khodja Djalal Eddine , Benguesmia Hani
Published: August 2020

In this work, we proposed multi-winding model for the simulation of broken bars in squirrel cage asynchronous machine, this model allows to study the influence of the broken bar defects on the behavior general of machines in different operating conditions (healthy and faulty). The breaking of the most frequent bars of the rotor causes oscillations of the torque, speed, and the current, the increase of the resistance of the rotor creates the defects proportional with the number of breaks bar K .The diagnosis fault using technique of single processing based on Spectrum analysis for detection broken bar. The results of the simulation obtained allowed us to show the importance of this technique for detection broken bar.
Elastic, mechanical and thermodynamic properties of zinc blende III-X (X= As, Sb): ab-initio calculations
Authors: IBRIR Miloud
Published: August 2020

In this work, density functional theory plane-wave full potential method, with local density approximation (LDA) are used to investigate the structural, mechanical and thermodynamic properties of of zincblende III-X ( X= As, Sb) compends. Comparison of the calculated equilibrium lattice constants and experimental data shows very good agreement. The elastic constants were determined from a linear fit of the calculated stress-strain function according to Hooke’s law. From the elastic constants, the bulk modulus B, shear modulus G, Young’s modulus E, Poisson’s ratio σ, anisotropy factor A, the ratio B/G and the hardness parameter H for zincblende III-X ( X= As, Sb) compound are obtained. Our calculated elastic constants indicate that the ground state structure of III-X ( X= As, Sb) is mechanically stable. The sound velocities and Debye temperature are also predicted from elastic constants.
Simulation and optimization of CH3NH3PbI3 based inverted planar heterojunction solar cell using SCAPS software
Authors: Hima Abdelkader
Published: August 2020

In order to improve the efficiency of a planar heterojunction organic-inorganic solar cell, this work is carried out using SCAPS software. The studied inverted P-I-N structure is PEDOT:PSS/ CH3NH3PbI3/ PCBM where PEDOT:PSS is the hole transporting layer (HTL), CH3NH3PbI3 is the Perovskite absorber layer (PVK) and PCBM is the electron transporting layer (ETL). The simulated structure is sandwiched between SnO2: FTO and Al which are the transparent and aluminum electrodes respectively. Simulation efforts are focused on thickness and density of states (donor’s and acceptors) effect on solar cell efficiency. Found results improved the power conversion efficiency (PCE) from 11.73% up to 19.58 %.
Comparative Study Between Fuzzy and Sliding Mode Control Based on a BDFIG
Authors: Ouada Laid , Sebti B.
Published: August 2020

Brushless doubly fed induction (BDFIG) is one of the best solutions of the previous research efforts about wind-power converter, which is represented by the absence of a brush gear and less maintenance cost, especially in the harsh regions. This task presents a comparative evaluation between a sliding mode and Fuzzy control. It demonstrates also the robustness of these two regulators against supply voltage and load disturbances. Furthermore, we have shown the undesirable phenomenon of oscillations having a finite frequency and amplitude, which is known as ‘chattering’ resulted from a sliding mode control which is based on Lyapunov approach theorem. The control of the system and BDFIG are both integrated in variable speed wind energy conversion. The performances of these control systems have been tested and analyzed in Matlab Simulink.
A TRNSYS dynamic simulation model for a parabolic trough solar thermal power plant
Authors: Remlaoui Ahmed , . M. Benyoucef, D. Assi, D. Nehari
Published: August 2020

This paper presents a validated TRNSYS model for a thermodynamic plant with parabolic trough solar thermal power (PT). The system consist of trough solar collector (PTC) as well as auxiliary components.. The simulation of the system has been done during the day (01/01) under the meteorological conditions of Ain Témouchent city (Algeria). The model compared the energy performance of the systems: case (1) - Rankine cycle facility with solar field and case (2)- Rankine cycle facility without solar field. The results showed that the present model has a good agreement with the experimental data of the literature. In case (1), PTC fluid outlet temperature reach the maximum value 330 ° C, Work of the steam turbine increase from the 9hr to reach its maximum value 856 KJ/Kg at 13 hr. In case (2), the maximum value of the power remains constant from the beginning of the simulation to 1hr00. Since the flow of fuel (gas natural) consumed does not change throughout the operating period.
Effects of continental emissions on cloud condensation nuclei (CCN) activity in the northern South China Sea during summertime 2018
Authors: Cai Mingfu , Liang Baoling , Sun Qibin , Zhou Shengzhen , Chen Xiaoyang , Yuan Bin , Shao Min , Tan Haobo , Zhao Jun
Published: August 2020

Abstract. Aerosol particles in marine atmosphere have been shown to significantly affect cloud formation, atmospheric optical properties, and climate
change. However, high temporally and spatially resolved atmospheric measurements over the sea are currently sparse, limiting our understanding of
aerosol properties in marine atmosphere. In this study, a ship-based cruise campaign was conducted over the northern South China Sea (SCS) region during
summertime 2018. The chemical composition of non-refractory PM1 (NR-PM1), the particle number size distribution (PNSD), and size-resolved cloud
condensation nuclei (CCN) activity were measured by a time-of-flight aerosol chemical speciation monitor (ToF-ACSM) and the combination of a cloud
condensation nuclei counter (CCNc) and a scanning mobility particle sizer (SMPS). Overall, aerosol particles exhibited a unimodal
distribution centering at 60–80 nm and the chemical composition of the NR-PM1 was dominated by sulfate (∼ 46 %), which
likely originated from anthropogenic emissions rather than dimethyl sulfide (DMS) oxidation. Two polluted episodes (P1 and P2) were observed, and both were characterized by high particle number concentrations (NCN) which originated from local emissions
and from emissions in inland China via long-range transport. The concentrations of trace gases (i.e., O3, CO, NOx) and
particles (NCN and NCCN at ss = 0.34 %) were elevated during P2 at the end of the campaign and decreased with offshore distance, further suggesting important impacts of anthropogenic emissions from the inland Pearl River Delta (PRD) region. Two relatively
clean periods (C1 and C2) prior to and after tropical storm Bebinca were classified and the air was affected by air masses from the southwest and from the Indo-Chinese Peninsula, respectively. Chemical composition measurements showed an increase in organic mass fraction during P2 compared to C2; however,
no obviously different κ values were obtained from the CCNc measurements, implying that the air masses carried pollutants from local sources
during long-range transport. We report an average value of about 0.4 for the aerosol hygroscopicity parameter κ, which falls within the literature
values (i.e., 0.2–1.0) for urban and remote marine atmosphere. In addition, our results showed that the CCN fraction
(NCCN∕NCN, tot) and the κ values had no clear correlation either with the offshore distance or with concentrations of
the particles. Our study highlights dynamical variations in particle properties and the impact of long-range transport from continental China and the Indo-Chinese Peninsula on the northern SCS region during summertime.
Effect of Polyoxymethylene (POM-H Delrin) offgassing within Pandora head sensor on direct sun and multi-axis formaldehyde column measurements in 2016–2019
Authors: Spinei Elena , Tiefengraber Martin , Müller Moritz , Gebetsberger Manuel , Cede Alexander , Valin Luke , Szykman James , Whitehill Andrew , Kotsakis Alexander , Santos Fernando , Abbuhasan Nader , Zhao Xiaoyi , Fioletov Vitali , Lee Sum Chi , Swap Robert
Published: August 2020

Abstract. Analysis of formaldehyde measurements by the Pandora spectrometer systems between 2016 and 2019 suggested that there was a temperature dependent process inside Pandora head sensor that emitted formaldehyde. Some parts in the head sensor were manufactured from thermal plastic polyoxymethylene homopolimer (E.I. Du Pont de Nemour & Co., USA: POM-H Delrin®) and were responsible for formaldehyde production. Laboratory analysis of the four Pandora head sensors showed that internal formaldehyde production had exponential temperature dependence with a damping coefficient of 0.0911 ± 0.0024 °C−1 and the exponential function amplitude ranging from 0.0041 DU to 0.049 DU. No apparent dependency on the head sensor age and heating/cooling rates was detected. The total amount of formaldehyde internally generated by the POM-H components and contributing to the direct sun measurements were estimated based on the head sensor temperature and solar zenith angle of the measurements. Measurements in winter, during cold days in general and at high solar zenith angles (> 75 °) were minimally impacted. Measurements during hot days and small solar zenith angles had up to 1 DU contribution from POM-H parts. Multi-axis differential slant column densities were minimally impacted (
Vertical distribution of planktic foraminifera through an Oxygen Minimum Zone:
how assemblages and shell morphology reflect oxygen concentrations
Authors: Davis Catherine V. , Wishner Karen , Renema Willem , Hull Pincelli M.
Published: August 2020

Abstract. Oxygen-depleted regions of the global ocean are rapidly expanding, with important implications for global biogeochemical cycles. However, our ability to make projections of a future deoxygenated ocean is limited by a lack of empirical data with which to test and constrain the behavior of global climatic and oceanographic models. We use depth-stratified plankton tows to demonstrate that some species of planktic foraminifera are adapted to life in the heart of the pelagic Oxygen Minimum Zone (OMZ). In particular, we identify two species, Globorotaloides hexagonus and Hastigerina parapelagica, living within the Eastern Tropical North Pacific OMZ. The shells of the former are preserved in marine sediments and could be used to trace the extent and intensity of low-oxygen pelagic habitats in the fossil record. Additional morphometric analyses of G. hexagonus show that shells found in the lowest oxygen environments are larger, more porous, less dense, and have more chambers in the final whorl. The association of this species with the OMZ and the apparent plasticity of its shell in response to ambient oxygenation invites the use of G. hexagonus shells in sediment cores as potential proxies for both the presence and intensity of overlying OMZs.
ESD Reviews: Evidence of multiple inconsistencies between representations of terrestrial and marine ecosystems in Earth System Models
Authors: Pellerin Félix , Porada Philipp , Hense Inga
Published: August 2020

Abstract. Terrestrial and marine ecosystems interact with other Earth system components through different biosphere-climate feedbacks that are very similar among ecosystem types. Despite these similarities, terrestrial and marine systems are often treated relatively separately in Earth System Models (ESM). In these ESM, the ecosystems are represented by a set of biological processes that are able to influence the climate system by affecting the chemical and physical properties of the environment. While most of the climate-relevant processes are shared between ecosystem types, model representations of terrestrial and marine ecosystems often differ. This raises the question whether inconsistencies between terrestrial and marine ecosystem models exist and potentially skew our perception of the relative influence of each ecosystem on climate. Here we compared the terrestrial and marine modules of 17 Earth System Models in order to identify inconsistencies between the two ecosystem types. We sorted out the biological processes included in ESM regarding their influence on climate into three types of biosphere-climate feedbacks (i.e. the biogeochemical pumps, the biogeophysical mechanisms and the gas and particle shuttles), and critically compare their representation in the different ecosystem modules. Overall, we found multiple evidences of unjustified differences in process representations between terrestrial and marine ecosystem models within ESM. These inconsistencies may lead to wrong predictions about the role of biosphere in the climate system. We believe that the present comparison can be used by the Earth system modeling community to increase consistency between ecosystem models. We further call for the development of a common framework allowing the uniform representation of climate-relevant processes in ecosystem modules of ESM.
Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation
Authors: Toms Benjamin A. , Kashinath Karthik ,   Prabhat, Yang Da
Published: August 2020

Abstract. We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden-Julian Oscillation. The Madden-Julian Oscillation is a multi-scale pattern within the tropical atmosphere that has been extensively studied over the past decades, which makes it an ideal test case to ensure the interpretability methods can recover the current state of knowledge regarding its spatial structure. The neural networks can, indeed, reproduce the current state of knowledge and can also provide new insights into the seasonality of the Madden-Julian Oscillation and its relationships with atmospheric state variables. The neural network identifies the phase of the Madden-Julian Oscillation twice as accurately as linear regression, which means that nonlinearities used by the neural network are important to the structure of the Madden-Julian Oscillation. Interpretations of the neural network show that it accurately captures the spatial structures of the Madden-Julian Oscillation, suggest that the nonlinearities of the Madden-Julian Oscillation are manifested through the uniqueness of each event, and offer physically meaningful insights into its relationship with atmospheric state variables. We also use the interpretations to identify the seasonality of the MJO, and find that the conventionally defined extended seasons should be shifted later by one month. More generally, this study suggests that neural networks can be reliably interpreted for geoscientific applications and may thereby serve as a dependable method for testing geoscientific hypotheses.
SURVEY ACCURACY AND SPATIAL RESOLUTION BENCHMARK OF A CAMERA SYSTEM MOUNTED ON A FAST FLYING DRONE
Authors: Meißner H. , Stebner K. , Kraft T. , Geßner M. , Berger R.
Published: August 2020

Abstract. Many drones are used to obtain high resolution imagery. Subsequent 3D object point derivation from images of these systems is an established technique. While rotor-craft drones are often used to capture fine, detailed structures and objects in small-scale areas fixed-wing versions are commonly used to cover larger areas even far beyond line of sight. Usually, these drones fly at much higher velocities during data acquisition and therefore the according sensor requirements are much higher.This paper presents the evaluation of a prototype camera system for fast flying fixed-wing drones. Focus of investigation is to find out if higher operating velocities, up to 100 km/h during image acquisition, has any influence on photogrammetric survey and image quality itself. It will be shown that images, obtained by the presented camera system and carrier, do not suffer from motion blur and that the overall survey accuracy is approximately 1/4 of ground sample distance.Survey accuracy analysis is carried out using standard photgrammetric procedures using signaled control- and checkpoints and verifying their conformity in image space and object space.Fundamentals of image quality will be introduced, as well asan approach to determine and evaluate motion smear of remote sensing senors (in theory and practical use case). Furthermore, it will be shown that the designed camera system mounted on a fixed-wing carrier does not suffer from motion smear.
SYNCHRONIZATION OF PICAM CAMERAS FOR THREE-DIMENSIONAL STUDY OF DYNAMIC MULTI-DOMAINS NATURAL SCENES
Authors: Avanthey L. , Beaudoin L. , Villard C. , Mellouk S. , Treglia R.
Published: August 2020

Abstract. In this article, we study the interest of PiCam and its possibilities offered for the realization of a light payload (small and inexpensive) in order to perform the 3D reconstruction of dynamic scenes (underwater or aerial) in close-range remote sensing. We see that on these observation scales, movements of the scenes due to flora and fauna cannot be ignored if we want these objects to be part of the final model. We review the sensors used in the literature for 3D reconstruction and then present the arguments in favor of PiCam with regard to the constraints posed by the use of light and agile vectors. The main issue is the synchronization of these low cost sensors, which is not native: we explain the different steps to obtain a satisfactory synchronization rate with regard to the dynamism of the studied scenes and present the results obtained.
HIGH-PRECISION OBJECT DELINEATION WITH UAV – DEMONSTRATED ON A TRACK SYSTEM
Authors: Gerke M. , Ghassoun Y. , Alamouri A. , Bobbe M. , Khedar Y. , Plöger F.
Published: August 2020

Abstract. The proper function of rail-based transport networks relies on the accurate positioning of the tracks. Regular control and maintenance intervals are in place to guarantee safe and reliable operation. This also holds for the crane rails of the storage cranes in the container terminal in the Hamburg harbour. Especially in the terminal “Altenwerder” the geomorphological conditions of the soil lead to a permanent subsidence of the tracks and thus ask for intensive surveying and maintenance activities. The allowed tolerances are in the range of 10mm in the XY-plane on a stretch of 300m. In the daily practice, the measurements are done using traditional tachymetric survey, in combination with a rail car carrying a reflector. This method is reliable but comes with the disadvantage that the operation of cranes needs to be interrupted. In this paper we present an alternative, automatic approach which employs state-of-the-art UAV-based photogrammetry to measure the actual location of the rail. The mid-format camera system combined with a 150mm tele-lens results in a GSD of 0.9mm at 35m flying height. Challenges addressed concern the proper setup and installation of the ground control network, the flight planning and bundle adjustment. Furthermore, an automated rail delineation in the derived surface model was developed. First experiments show that an automatic workflow is possible, including the delineation task. Remaining obstacles concern, for instance, the compliance with the requirements regarding absolute positional accuracy, since the inner block geometry is theoretically much more accurate than the realised control point network.
THEORETICAL MODEL AND DESIGN OF A NOVEL STATIC POLARIZATION WIND IMAGING INTERFEROMETER (NSPWII)
Authors: Zhang C. , Yan T. , Mu T. , He Y.
Published: August 2020

Abstract. Polarization array based polarization Michelson wind field detection interferometer is a new type of interferometer for detecting atmospheric temperature and wind field velocity. We proposed a theoretical model and design of the novel static polarization wind imaging interferometer (NSPWII). It consists of a group of polarizers and waveplates, a polarization beamsplitter, a field widened Michelson interferometer, a pyramid prism, and a polarization array. Based on the principle of polarization interference, a polarization array composed of four polarizers with 45° polarization directions differences is directly in front of the detector, and four modulated light beams from the pyramid prism pass through the four polarizers. Then, interferograms with four different intensity are imaged on the detector simultaneously, which further provides the atmospheric temperature and wind field velocity. The advantages of this instrument are static (no moving parts), achromatic, and temperature compensated. It is capable of measuring the upper atmospheric wind field in real time with a high precision.
DERIVING EXCLUSION MAPS FROM C-BAND SAR TIME-SERIES: AN ADDITIONAL INFORMATION LAYER FOR SAR-BASED FLOOD EXTENT MAPPING
Authors: Zhao J. , Chini M. , Pelich R. , Matgen P. , Hostache R. , Cao S. , Wagner W.
Published: August 2020

Abstract. Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images as floodwater often leads to a substantial decrease of backscatter. However, limitations still exist in many areas, such as shadow, layover, urban areas and densely vegetated areas, where the SAR backscatter is not sufficiently impacted by floodwater-related surface changes. This study focuses on these so-called exclusion areas, i.e. areas where SAR does not allow detecting water based on change detection. Our approach considers both pixel-based time series analyses and object-based spatial analyses using 20m Sentinel-1 Interferometric Wide Swath data, including 922 Sentinel-1 tiles covering the River Severn basin (UK) and the Lake Maggiore area (Italy). The results show that our exclusion map presents a good agreement (∼63%) with reference data derived from different data sources and indicate that it may complement SAR-derived flood extent maps. Allowing to accurately identify potential misclassifications in flood extent mapping, our exclusion map provides valuable information for flood management and, in particular, flood forecasting and prediction.
POINTING BIAS CALIBRATION OF GAOFEN-7 LASER ALTIMETER BASED ON SINGLE LASER FOOTPRINT IMAGE
Authors: Xie J. , Liu R. , Mo F. , Tang H. , Jiao H. , Mei Y. , Yang C.
Published: August 2020

Abstract. The GaoFen-7 (GF-7) satellite is successfully launched on November 3, 2019, and its laser altimeter system is officially and firstly employed as the main payload for earth observations in China, which includes two sets of laser altimeters and laser footprint cameras. The Laser Footprint Image (LFI) is used to capture laser spots on the ground. In order to make up for the shortcomings of high cost field work for the traditional laser altimeter ground detector-based calibration method, this paper proposes a novel laser altimeter calibration method based on LFI. Firstly, the spaceborne laser calibration model and the Laser Footprint Camera (LFC) geolocation model are established. Secondly, the image coordinates of laser spot centroid are extracted from LFI, and the ground location of is obtained by ray intersecting with the reference Digital Surface Model (DSM). Finally, the centroid of laser spot is considered as Ground Control Point (GCP), and the pointing bias of GF-7 laser altimeter is calibrated by the Least Squares Estimation (LSE). The ALOS Global Digital Surface Model “ALOS World 3D-30m” (AW3D30) is used to evaluate the elevation accuracy of GF-7 laser altimeter before and after the calibration. The results indicate that elevation accuracy of the GF-7 laser altimeter is improved significantly after calibration. The proposed method can be effectively applied for high-frequency geometric calibration of GF-7 laser altimeter.
UNCERTAINTY ESTIMATION FOR END-TO-END LEARNED DENSE STEREO MATCHING VIA PROBABILISTIC DEEP LEARNING
Authors: Mehltretter M.
Published: August 2020

Abstract. Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning based methods have shown convincing results. However, most of these methods only model the uncertainty contained in the data, while ignoring the uncertainty of the employed dense stereo matching procedure. Additionally modelling the latter, however, is particularly beneficial if the domain of the training data varies from that of the data to be processed. For this purpose, in the present work the idea of probabilistic deep learning is applied to the task of dense stereo matching for the first time. Based on the well-known and commonly employed GC-Net architecture, a novel probabilistic neural network is presented, for the task of joint depth and uncertainty estimation from epipolar rectified stereo image pairs. Instead of learning the network parameters directly, the proposed probabilistic neural network learns a probability distribution from which parameters are sampled for every prediction. The variations between multiple such predictions on the same image pair allow to approximate the model uncertainty. The quality of the estimated depth and uncertainty information is assessed in an extensive evaluation on three different datasets.
A NEW SATELLITE IMAGERY STEREO PIPELINE DESIGNED FOR SCALABILITY, ROBUSTNESS AND PERFORMANCE
Authors: Michel J. , Sarrazin E. , Youssefi D. , Cournet M. , Buffe F. , Delvit J. M. , Emilien A. , Bosman J. , Melet O. , L’Helguen C.
Published: August 2020

Abstract. This paper presents a new Multiview Stereo Pipeline (MVS), called CARS, dedicated to satellite imagery. This pipeline is intended for massive Digital Surface Model (DSM) production and has therefore been designed to maximize scalability robustness and performance. Those two properties have driven the design of the workflow as well as the choice of algorithms and parameter trends, making our pipeline unique with respect to existing solutions in literature. This paper intends to serve as a reference paper for the pipeline implementation, and therefore provides a detailed description of algorithms and workflow. It also demonstrates the pipeline robustness and stability in several use cases, and compares its accuracy with the state-of-the-art pipelines on a reference dataset.
LIVE EXTRACTION OF CURVILINEAR STRUCTURES FROM LIDAR RAW DATA
Authors: Even P. , Ngo P.
Published: August 2020

Abstract. In this paper, a general framework is proposed for live extraction of curvilinear structures such as roads or ridges from airborne LiDAR raw data, in the scope of present and past man-environment interaction studies. Unlike most approaches in literature, classified ground points are directly processed here, rather than derived products such as digital terrain models (DTM). This allows to detect possible lacks of ground points due to LiDAR signal occlusions caused by dense coniferous canopies. An efficient and simple solution based on discrete geometry tools is described for supervised context in which the user just indicates where the extraction should take place. Fast response times are required to ensure a good man-system interaction.The framework performance is first evaluated on the example of the extraction of forest roads in a mountainous area, as these objects are well marked in the DTM and hence provide some kind of ground truth. Good execution time and accuracy level are reported. Then this framework is applied to the detection of prominent curvilinear structures, which are much more diffuse objects, but of greater interest than roads in the scope of the present project. Achieved results show high potential of the proposed approach to help archaeologists and geomorphologists in finding areas of interest for future prospection using LiDAR data.
EFFICIENT ESTIMATION OF 3D SHIFTS BETWEEN POINT CLOUDS USING LOW-FREQUENCY COMPONENTS OF PHASE CORRELATION
Authors: Huang R. , Xu Y. , Hoegner L. , Stilla U.
Published: August 2020

Abstract. Registration of multiple point clouds acquired via terrestrial laser scanning (TLS) is usually compulsory to obtain the scanned data covering a whole urban scene. However, the automated processing of aligning multiple scans is still a concern because of the complex urban environment. To this end, we propose a fast and sturdy estimation of 3D shifts between point clouds by an automated markerfree process using global features, converting translation measurement between two point clouds in the space domain to the frequency domain and estimating the phase difference. By using the low-frequency components from the normalized cross-power spectrum, accurate 3D shifts are calculated by solving parameters in the linear equation representing phase difference angles, with the help of a robust estimator. The results of experiments using TLS datasets of different scenes show that the proposed approach is both practical and efficient. In particular, the proposed approach can achieve results with a translation error of less than about 1.0 m on test datasets.
DEEP LEARNING BASED FEATURE MATCHING AND ITS APPLICATION IN IMAGE ORIENTATION
Authors: Chen L. , Rottensteiner F. , Heipke C.
Published: August 2020

Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.
SLAM-BASED BACKPACK LASER SCANNING FOR FOREST PLOT MAPPING
Authors: Shao J. , Zhang W. , Luo L. , Cai S. , Jiang H.
Published: August 2020

Abstract. Acquisition of three-dimensional (3D) structural information is significant for forest measurements. To achieve faster data collection in forests, we design a backpack laser scanning (BLS) system using a single mobile laser scanning (MLS) scanner and specific to forest environments. The simultaneous localization and mapping (SLAM) approach based on the natural geometric characteristics of trees is used for BLS-based forest mapping, in which the skeleton line of the individual tree is extracted for scan matching and the incremental maps are adopted for global optimization of all the BLS point clouds. The final experimental results show that the SLAM-based BLS system achieves accurate forest plots mapping and allows reaching low mapping errors, in which the mean errors are approximately 3 cm in the horizontal and 2 cm in the vertical direction.
SEMANTIC SEGMENTATION OF POINT CLOUDS WITH POINTNET AND KPCONV ARCHITECTURES APPLIED TO RAILWAY TUNNELS
Authors: Soilán M. , Nóvoa A. , Sánchez-Rodríguez A. , Riveiro B. , Arias P.
Published: August 2020

Abstract. Transport infrastructure monitoring has lately attracted increasing attention due to the rise in extreme natural hazards posed by climate change. Mobile Mapping Systems gather information regarding the state of the assets, which allows for more efficient decision-making. These systems provide information in the form of three-dimensional point clouds. Point cloud analysis through deep learning has emerged as a focal research area due to its wide application in areas such as autonomous driving. This paper aims to apply the pioneering PointNet, and the current state-of-the-art KPConv architectures to perform scene segmentation of railway tunnels, in order to validate their employability over heuristic classification methods. The approach is to perform a multi-class classification that classifies the most relevant components of tunnels: ground, lining, wiring and rails. Both architectures are trained from scratch with heuristically classified point clouds of two different railway tunnels. Results show that, while both architectures are suitable for the proposed classification task, KPConv outperforms PointNet with F1-scores over 97% for ground, lining and wiring classes, and over 90% for rails. In addition, KPConv is tested using transfer learning, which gives F1-scores slightly lower than for the model training from scratch but shows better generalization capabilities.
MIN-CUT BASED SEMANTIC BUILDING LABELING FOR AIRBORNE LIDAR DATA
Authors: Ural S. , Shan J.
Published: August 2020

Abstract. Classification and segmentation of buildings from airborne lidar point clouds commonly involve point features calculated within a local neighborhood. The relative change of the features in the immediate surrounding of each point as well as the spatial relationships between neighboring points also need to be examined to account for spatial coherence. In this study we formulate the point labeling problem under a global graph-cut optimization solution. We construct the energy function through a graph representing a Markov Random Field (MRF). The solution to the labeling problem is obtained by finding the minimum-cut on this graph. We have employed this framework for three different labeling tasks on airborne lidar point clouds. Ground filtering, building classification, and roof-plane segmentation. As a follow-up study on our previous ground filtering work, this paper examines our building extraction approach on two airborne lidar datasets with different point densities containing approximately 930K points in one dataset and 750K points in the other. Test results for building vs. non-building point labeling show a 97.9% overall accuracy with a kappa value of 0.91 for the dataset with 1.18 pts/m2 average point density and a 96.8% accuracy with a kappa value of 0.90 for the dataset with 8.83 pts/m2 average point density. We can achieve 91.2% overall average accuracy in roof plane segmentation with respect to the reference segmentation of 20 building roofs involving 74 individual roof planes. In summary, the presented framework can successfully label points in airborne lidar point clouds with different characteristics for all three labeling problems we have introduced. It is robust to noise in the calculated features due to the use of global optimization. Furthermore, the framework achieves these results with a small training sample size.
Y-SHAPED CONVOLUTIONAL NEURAL NETWORK FOR 3D ROOF ELEMENTS EXTRACTION TO RECONSTRUCT BUILDING MODELS FROM A SINGLE AERIAL IMAGE
Authors: Alidoost F. , Arefi H. , Hahn M.
Published: August 2020

Abstract. Fast and efficient detection and reconstruction of buildings have become essential in real-time applications such as navigation, 3D rendering, augmented reality, and 3D smart cities. In this study, a modern Deep Learning (DL)-based framework is proposed for automatic detection, localization, and height estimation of buildings, simultaneously, from a single aerial image. The proposed framework is based on a Y-shaped Convolutional Neural Network (Y-Net) which includes one encoder and two decoders. The input of the network is a single RGB image, while the outputs are predicted height information of buildings as well as the rooflines in three classes of eave, ridge, and hip lines. The extracted knowledge by the Y-Net (i.e. buildings’ heights and rooflines) is utilized for 3D reconstruction of buildings based on the third Level of Detail (LoD2). The main steps of the proposed approach are data preparation, CNNs training, and 3D reconstruction. For the experimental investigations airborne data from Potsdam are used, which were provided by ISPRS. For the predicted heights, the results show an average Root Mean Square Error (RMSE) and a Normalized Median Absolute Deviation (NMAD) of about 3.8 m and 1.3 m, respectively. Moreover, the overall accuracy of the extracted rooflines is about 86%.
A NOVEL RPC BIAS MODEL FOR IMPROVING THE POSITIONING ACCURACY OF SATELLITE IMAGES
Authors: Dong Y. , Lei R. , Fan D. , Gu L. , Ji S.
Published: August 2020

Abstract. High-precision satellite image geolocation is the basis for advanced processing of satellite image data. Aiming at the optimization of the satellite image positioning accuracy based on rational polynomial coefficients (RPC), we propose an RPC image-space bias model that combines object-space information. Based on a comprehensive analysis of the full-link error of the satellite image geometric imaging process, the real object coordinates are introduced into the RPC correction to make the bias model better fit the actual error. Experiments were performed using several image datasets from the Chinese satellite TianHui-1 (TH-1) and compared with the traditional RPC bias model. The results show that our model has strong robustness and can better correct image positioning errors. Compared with traditional bias models, it can improve the accuracy of plane positioning by approximately 1 pixel.
DATA-DRIVEN MODELING OF BUILDING INTERIORS FROM LIDAR POINT CLOUDS
Authors: Sanchez J. , Denis F. , Dupont F. , Trassoudaine L. , Checchin P.
Published: August 2020

Abstract. This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the reconstruction challenging (anisotropy, noise, clutters, etc.), the automatic methods of the state-of-the-art rely on numerous construction hypotheses which yield 3D models relatively far from initial data. The choice has been done to propose a new modeling method closer to point cloud data, reconstructing only scanned areas of each scene and excluding occluded regions. According to this objective, our approach reconstructs LiDAR scans individually using connected polygons. This modeling relies on a joint processing of an image created from the 2D LiDAR angular sampling and the 3D point cloud associated to one scan. Results are evaluated on synthetic and real data to demonstrate the efficiency as well as the technical strength of the proposed method.
SINGULARITY-FREE BUNDLE ADJUSTMENT
Authors: Garcia E.
Published: August 2020

Abstract. The photogrammetric bundle adjustment is well-behaved in the case of structured aerial imagery looking in the nadir direction. That is less so in the case of ground-level imagery with less structure and potentially looking in any direction. Besides, the cost function based on reprojection errors of tie points is not defined everywhere and exhibits singularities which renders this bundle adjustment process sensitive to initial conditions and outliers. In order to handle difficult configurations without incurring the risks posed by the reprojection function, we propose a new error function that is equivalent to the reprojection error when this error tends to zero, and that enjoys many desirables properties, such as being defined everywhere and being continuous. This allows an easier implementation of a robust bundle adjustment, and incidentally it also allows to solve derivative problems such as triangulating points starting from arbitrary initial positions, or estimating the relative positions of calibrated and oriented cameras starting from arbitrary positions, thus offering a simple solution to the known-orientation structure-from-motion problem.
DEEP DOMAIN ADAPTATION BY WEIGHTED ENTROPY MINIMIZATION FOR THE CLASSIFICATION OF AERIAL IMAGES
Authors: Wittich D.
Published: August 2020

Abstract. Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs.
GEOMETRIC DISTORTION OF HISTORICAL IMAGES FOR 3D VISUALIZATION
Authors: Paiz-Reyes E. , Brédif M. , Christophe S.
Published: August 2020

Abstract. Archivists, historians and national mapping agencies, among others, are archiving large datasets of historical photographs. Nevertheless, the capturing devices used to acquire these images possessed a diversity of effects that influenced the quality of the final resulting picture, e.g. geometric distortion, chromatic aberration, depth of field variation, etc. This paper examines singularly the topic of geometric distortion for a co-visualization of historical photos within a 3D model of the photographed scene. A distortion function of an image is ordinarily estimated only on the image domain by adjusting its parameters to observations of point correspondences. This mathematical function may exhibit overfits, oscillations or may not be well defined outside of this domain. The contribution of this work is the description of a distortion model defined on the whole undistorted image plane. We extrapolate the distortion estimated only on the image domain and then transfer this distortion information to the view of the 3D scene. This enables to look at the scene through an estimated camera and zoom out to see the context around the original photograph with a well-defined and behaved distortion. These findings may be a significant addition to the overall purpose of creating innovative ways to examine and visualize old photographs.
CHANGE DETECTION IN PHOTOGRAMMETRIC POINT CLOUDS FOR MONITORING OF ALPINE, GRAVITATIONAL MASS MOVEMENTS
Authors: Dinkel A. , Hoegner L. , Emmert A. , Raffl L. , Stilla U.
Published: August 2020

Abstract. This contribution discusses the accuracy and the applicability of Photogrammetric point clouds based on dense image matching for the monitoring of gravitational mass movements caused by crevices. Four terrestrial image sequences for three different time epochs have been recorded and oriented using ground control point in a local reference frame. For the first epoch, two sequences are recorded, one in the morning and one in the afternoon to evaluate the noise level within the point clouds for a static geometry and changing light conditions. The second epoch is recorded a few months after the first epoch where also no significant change has occurred in between. The third epoch is recorded after one year with changes detected. As all point clouds are given in the same local coordinate frame and thus are co-registered via the ground control points, change detection is based on calculating the Multiscale-Model-to-Model-Cloud distances (M3C2) of the point clouds. Results show no movements for the first year, but identify significant movements comparing the third epoch taken in the second year. Besides the noise level estimation, the quality checks include the accuracy of the camera orientations based on ground control points, the covariances of the bundle adjustment, and a comparison the Geodetic measurements of additional control points and a laser scanning point cloud of a part of the crevice. Additionally, geological measurements of the movements have been performed using extensometers.
ANALYSIS OF FILTERING TECHNIQUES FOR INVESTIGATING LANDSLIDE-INDUCED TOPOGRAPHIC CHANGES IN THE OETZ VALLEY (TYROL, AUSTRIA)
Authors: Gutierrez I. , Før Gjermundsen E. , Harcourt W. D. , Kuschnerus M. , Tonion F. , Zieher T.
Published: August 2020

Abstract. Landslides endanger settlements and infrastructure in mountain areas across the world. Monitoring of landslides is therefore essential in order to understand and possibly predict their behavior and potential danger. Terrestrial laser scanning has proven to be a successful tool in the assessment of changes on landslide surfaces due to its high resolution and accuracy. However, it is necessary to classify the 3D point clouds into vegetation and bare-earth points using filtering algorithms so that changes caused by landslide activity can be quantified. For this study, three classification algorithms are compared on an exemplary landslide study site in the Oetz valley in Tyrol, Austria. An optimal set of parameters is derived for each algorithm and their performances are evaluated using different metrics. The volume changes on the study site between the years 2017 and 2019 are compared after the application of each algorithm. The results show that (i) the tested filter techniques perform differently, (ii) their performance depends on their parameterization and (iii) the best-performing parameterization found over the vegetated test area will yield misclassifications on non-vegetated rough terrain. In particular, if only small changes have occurred the choice of the filtering technique and its parameterization play an important role in estimating volume changes.
MODELLING PERMAFROST TERRAIN USING KINEMATIC, DUAL-WAVELENGTH LASER SCANNING
Authors: Kukko A. , Kaartinen H. , Osinski G. , Hyyppä J.
Published: August 2020

Abstract. In this paper we introduce the first dual-wavelength, kinematic backpack laser scanning system and its application on high resolution 3D terrain modelling of permafrost landforms. We discuss the data processing pipeline from acquisition to preparation, system calibration and terrain model process. Topographic information is vital for planning and monitoring tasks in urban planning, road construction for mass calculations, and mitigation of flood and wind related risks by structural design in coastal areas. 3D data gives possibility to understand natural processes inducing changes in the terrain, such as the cycles of thaw-freeze in permafrost regions. Through an application case on permafrost landforms in the Arctic we present the field practices and data processing applied, characterize the data output and discuss the precision and accuracy of the base station, trajectory and point cloud data. Two pulsed time of flight ranging, high performance mobile laser scanners were used in combination with a near navigation grade GNSS-IMU positioning on a kinematic backpack platform. The study shows that with a high-end system 15 mm absolute accuracy of 3D data could be achieved using PPP processing for the GNSS base station and multi-pass differential trajectory post-processing. The PPP solution shows millimetre level agreement (Easting 6 mm, Northing 4 mm, and elevation 8 mm standard deviations) for the base station coordinates over an 11 day period. The point cloud residual standard deviation for angular boresight misalignment was 27 mm. The absolute distance between ground surfaces from interactive analysis was 17 mm with 13 mm standard deviation (n = 64). The proposed backpack laser scanning provides accurate and precise 3D data and performance over considerable land surface area for detailed elevation modelling and analysis of the morphology of features of interest. The high density point cloud data permits fusion of the dual-wavelength lidar reflectance data into spectral products.