bluuebunny/crossref_metadata_2025 huggingface dataset:
The role of contact angle and pore width on pore condensation and freezing
Authors: David Robert O. , Fahrni Jonas , Marcolli Claudia , Mahrt Fabian , Brühwiler Dominik , Kanji Zamin A.
Published: August 2020

Abstract. It has recently been shown that pore condensation and
freezing (PCF) is a mechanism responsible for ice formation under cirrus
cloud conditions. PCF is defined as the condensation of liquid water in
narrow capillaries below water saturation due to the inverse Kelvin effect,
followed by either heterogeneous or homogeneous nucleation depending on the
temperature regime and presence of an ice-nucleating active site. By using
sol–gel synthesized silica with well-defined pore diameters, morphology and
distinct chemical surface-functionalization, the role of the water–silica
contact angle and pore width on PCF is investigated. We find that for the
pore diameters (2.2–9.2 nm) and water contact angles (15–78∘) covered in this study, our results reveal that the water contact angle
plays an important role in predicting the humidity required for pore filling,
while the pore diameter determines the ability of pore water to freeze. For
T>235 K and below water saturation, pore diameters and water
contact angles were not able to predict the freezing ability of the
particles, suggesting an absence of active sites; thus ice nucleation did not
proceed via a PCF mechanism. Rather, the ice-nucleating ability of the
particles depended solely on chemical functionalization. Therefore,
parameterizations for the ice-nucleating abilities of particles in cirrus
conditions should differ from parameterizations at mixed-phase clouds
conditions. Our results support PCF as the atmospherically relevant ice
nucleation mechanism below water saturation when porous surfaces are
encountered in the troposphere.
A measurement and model study on ozone characteristics in marine air at a remote island station and its interaction with urban ozone air quality in Shanghai, China
Authors: Gu Yixuan , Yan Fengxia , Xu Jianming , Qu Yuanhao , Gao Wei
Published: August 2020

Abstract. To understand the characteristics and changes of baseline ozone (O3) in oceanic air in East China, a six-year measurement of O3 concentration was conducted from 1 January 2012 to 15 September 2017 at a remote offshore station located on the Sheshan Island (SSI) near the megacity of Shanghai. The observed monthly mean O3 concentrations at SSI ranged from 33.4 to 61.4 ppbv during the study period, which were about 80 % and 12 % higher, respectively than those measured at downtown and rural sites in Shanghai. Compared to the remarkable O3 increases observed at urban and rural sites in Shanghai, observed O3 concentrations at SSI exhibited statistically insignificant increasing changes (1.12 ppbv yr−1, α > 0.10) during the observation period, suggesting less impacts of anthropogenic emissions on O3 levels in oceanic air. In addition, an insignificant decreasing change (−0.72 ppbv yr−1, α > 0.10) was detected in O3 concentrations at SSI in September and October when the influence of regional transport was minimum throughout the year, providing a good proxy to study the baseline oxidation capacity of the oceanic atmosphere. City plumes from Shanghai usually carried higher levels of NOx, resulting in decreased O3 concentrations at SSI during southwesterly and westerly winds. However, In MAM (March–May) and JJA (June–August), due to the enhanced oxidation of oxygenated volatile organic compounds, O3 could be continuously produced during daytime in aged city plumes, resulting in elevated O3 concentrations transported to SSI. The impacts of the offshore O3 on O3 levels in Shanghai are quantified during an easterly wind dominant episode (September 1–30, 2014) using the WRF-Chem model. Sensitivity results suggest that O3 in the oceanic air inflows can lead to 20–30 % increases in urban O3 concentrations, which should be crucially considered in dealing with urban O3 pollution in large coastal cities like Shanghai.
Quantification of the role of stabilized Criegee intermediates in the formation of aerosols in limonene ozonolysis
Authors: Gong Yiwei , Chen Zhongming
Published: August 2020

Abstract. Stabilized Criegee intermediates (SCIs) have the potential to oxidize trace species and to produce secondary organic aerosols (SOA), making them important factors in tropospheric chemistry. This study quantitatively investigates the performance of SCIs in SOA formation at different relative humidity (RH), and the first- and second-generation oxidations of endo- and exo-cyclic double bonds ozonated in limonene ozonolysis are studied separately. Through regulating SCIs scavengers, the yields and rate constants of SCIs in reaction system were derived, and the amounts of SCIs were calculated. The amount of SOA decreased by more than 20 % under low-humidity conditions (10–50 % RH), compared to that under dry conditions due to the reactions of SCIs with water, while the inhibitory effect of water on SOA formation was not observed under high-humidity conditions (60–90 % RH). When using excessive SCIs scavengers to exclude SCIs reactions, it was found that the effect of water on SOA formation with the presence of SCIs was different from that without the presence of SCIs, suggesting that SCIs reactions were relevant to the non-monotonic impact of water. The fractions of SCIs contribution to SOA were similar between dry and high-humidity conditions, where the SCIs reactions accounted for ~ 63 % and ~ 73 % in SOA formation in the first- and second-generation oxidation, however, marked differences in SOA formation mechanisms were observed. SOA formation showed a positive correlation with the amount of SCIs, and the SOA formation potential of SCIs under high-humidity conditions was more significant than that under dry and low-humidity conditions. It was estimated that 20–30 % of SCIs could convert into SOA under high-humidity conditions, while this value decreased nearly by half under dry and low-humidity conditions. The contributions of limonene-derived SCIs to SOA in atmosphere were evaluated, and it was estimated that the contribution of SCIs to SOA was the lowest under low-humidity conditions. Under high-humidity conditions, the contribution of limonene-derived SCIs to SOA was (8.21 ± 0.15) × 10−2 μg m−3 h−1 in forest, (6.66 ± 0.12) × 10−2 μg m−3 h−1 in urban area, and (3.95 ± 0.72) × 10−1 μg m−3 h−1 in indoor area. Water was an uncertainty on the role of SCIs playing in SOA formation, and the contribution of SCIs to SOA formation needed consideration even under high RH in the atmosphere.
Influence of Saharan dust on Atlantic tropical cyclones
Authors: Zhang Zhenxi , Zhou Wen
Published: August 2020

Abstract. The influence of Saharan dust outbreaks on summertime Atlantic tropical cyclone (TC) activity is explored using continuous atmospheric reanalysis products and TC track data from 1980 to 2019. Analyses reveal that the Saharan dust plume over the tropical Atlantic can affect TC activity by affecting the atmospheric hydrology and radiation absorbed by the earth's surface, which can be classified into three mechanisms. (1) A strong Saharan dust plume indirectly induces the reduction of atmospheric moisture, which further suppresses TC track, number of TC days, and intensity, with the influence covering the whole tropical Atlantic. (2) A strong Saharan dust plume enhances atmospheric moisture just along the North Atlantic ITCZ through the dust microphysical effect, which further promotes TC activity along 10º N latitude in June. (3) The climatological influence of dust on TC activity is caused by the strong radiative forcing of Saharan dust over the eastern tropical Atlantic in June, which produces an evident reduction in SST and lessens the duration and intensity of regional TC activity in June, according to the 40-yr average from 1980 to 2019.
High-resolution optical constants of crystalline ammonium nitrate for infrared remote sensing of the Asian Tropopause Aerosol Layer
Authors: Wagner Robert , Testa Baptiste , Höpfner Michael , Kiselev Alexei , Möhler Ottmar , Saathoff Harald , Ungermann Jörn , Leisner Thomas
Published: August 2020

Abstract. Infrared spectroscopic observations have shown that crystalline ammonium nitrate (AN) particles are an abundant constituent of the upper tropospheric aerosol layer which is formed during the Asian summer monsoon period, the so-called Asian Tropopause Aerosol Layer (ATAL). At upper tropospheric temperatures, the thermodynamically stable phase of AN is different from that at 298 K, meaning that presently available room-temperature optical constants of AN, that is, the real and imaginary parts of the complex refractive index, cannot be applied for the quantitative analysis of these infrared measurements. In this work, we have retrieved the first low-temperature data set of optical constants for crystalline AN in the 800–6000 cm−1 wavenumber range with a spectral resolution of 0.5 cm−1. The optical constants were iteratively derived from an infrared extinction spectrum of 1 micrometer-sized AN particles suspended in a cloud chamber at 223 K. The uncertainties of the new data set were carefully assessed in a comprehensive sensitivity analysis. We show that our data accurately fit aircraft-borne infrared measurements of ammonium nitrate particles in the ATAL.
Orbitool: A software tool for analyzing online Orbitrap mass spectrometry data
Authors: Cai Runlong , Li Yihao , Clément Yohann , Li Dandan , Dubois Clément , Fabre Marlène , Besson Laurence , Perrier Sebastien , George Christian , Ehn Mikael , Huang Cheng , Yi Ping , Ma Yingge , Riva Matthieu
Published: August 2020

Abstract. The Orbitrap mass spectrometer has recently been proved to be a powerful instrument to accurately measure gas-phase and particle-phase organic compounds with a greater mass resolving power than other widely-used online mass spectrometers in atmospheric sciences. We develop an open-source software tool (Orbitool, https://orbitrap.catalyse.cnrs.fr) to facilitate the analysis of long-term online Orbitrap data. Orbitool can average long-term data while maintaining the mass accuracy by re-calibrating each mass spectrum, identify chemical compositions and isotopes of measured signals, and export time series and mass defect plots. The noise reduction procedure in Orbitool can separate signal peaks from noise and greatly reduce the computational and storage expenses. Chemical-ionization Orbitrap data from laboratory experiments on ozonolysis of monoterpenes and ambient measurements in urban Shanghai were used to successfully test Orbitool. For the test dataset, the average mass accuracy was improved from
Munich permanent urban greenhouse gas column observing network
Authors: Dietrich Florian , Chen Jia , Voggenreiter Benno , Aigner Patrick , Nachtigall Nico , Reger Björn
Published: August 2020

Abstract. In order to mitigate climate change, it is crucial to understand the urban greenhouse gas (GHG) emissions precisely as more than two third of the anthropogenic GHG emissions worldwide originate from cities. Nowadays, urban emission estimates are mainly based on bottom-up calculation approaches with high uncertainties. A reliable and long-term top-down measurement approach could reduce the uncertainty of these emission inventories significantly. We present the world’s first urban sensor network that is permanently measuring GHGs based on the principle of differential column measurements (DCM) starting in summer 2019. These column measurements are relatively insensitive to vertical redistribution of tracer masses and surface fluxes upwind of the city. Therefore, they are well-suited to quantify GHG emissions. However, setting up such a stationary sensor network requires an automated measurement principle. We developed our own fully automated enclosure systems for measuring CO2, CH4 and CO column-averaged concentrations with a solar-tracking Fourier Transform spectrometer (EM27/SUN) in a fully automated and long-term manner. This includes also a software that starts and stops the measurements autonomously and can be used independently from the enclosure system. Furthermore, we demonstrate the novel applications of such a sensor network by presenting the measurement results of our five sensor systems that are deployed in and around Munich. These results include the seasonal cycle of CO2 since 2015 as well as concentration gradient measurements upwind and downwind of the city. Thanks to the automation we were also able to continue the measurements during the COVID-19 lockdown in spring 2020. By correlating the CO2 column concentration gradients to the traffic amount, we demonstrate that our network is well capable to detect variations in urban emissions. The measurements from our unique sensor network will be combined with an inverse modeling framework that we are currently developing, in order to monitor urban GHG emissions over years, identify unknown emission sources and assess how effective the current mitigation strategies are. In summary, our achievements in automating column measurements of GHGs will allow researchers all over the world to establish this novel measurement approach as a new standard for determining GHG emissions.
Luxembourgite, AgCuPbBi4Se8, a new mineral species from Bivels, Grand Duchy of Luxembourg
Authors: Philippo Simon , Hatert Frédéric , Bruni Yannick , Vignola Pietro , Sejkora Jiří
Published: August 2020

Abstract. Luxembourgite, ideally AgCuPbBi4Se8, is a new selenide
discovered at Bivels, Grand Duchy of Luxembourg. The mineral forms tiny
fibres reaching 200 µm in length and 5 µm in diameter, which are
deposited on dolomite crystals. Luxembourgite is grey, with a metallic
lustre and without cleavage planes; its Mohs hardness is 3 and its calculated
density is 8.00 g cm−3. Electron-microprobe analyses indicate an
empirical formula Ag1.00(Cu0.82Ag0.20Fe0.01)Σ1.03Pb1.13Bi4.11(Se7.72S0.01)Σ7.73,
calculated on the basis of 15 atoms per formula unit. A single-crystal
structure refinement was performed to R1=0.0476, in the
P21∕m space group, with a=13.002(1), b=4.1543(3), c=15.312(2) Å, β=108.92(1)∘, V=782.4(2) Å3,
Z=2. The crystal structure is similar to that of litochlebite and
watkinsonite and can be described as an alternation of two types of anionic
layers: a pseudotetragonal layer four atoms thick and a pseudohexagonal
layer that is one atom thick. In the pseudotetragonal layers the
Bi1, Bi2 ,Bi3, Pb, and Ag1 atoms are localised, while the Cu2 and Bi4 atoms occur between
the pseudotetragonal and the pseudohexagonal layers. Bi1, Bi2, and Bi3 atoms
occur in weakly distorted octahedral sites, whereas Bi4 occurs in a
distorted 7-coordinated site. Ag1 occupies a fairly regular octahedral site,
Cu2 a tetrahedral position, and Pb occurs on a very distorted 8-coordinated
site.
An improved Terra–Aqua MODIS daily cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019
Authors: Muhammad Sher , Thapa Amrit
Published: August 2020

Abstract. Snow is a dominant water resource in High Mountain Asia (HMA) and crucial for the mountain communities and downstream population. Snow cover monitoring is significant to understand regional climate change, managing meltwater, and associated hazards/disasters. The uncertainties in passive optical remote sensing snow products mainly underestimation caused by cloud-cover and overestimation associated with sensorsˈ limitations hamper the understand snow dynamics. We reduced the biases in Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily snow data and generated a combined daily snow product for High Mountain Asia between 2002 and 2019. An improved MODIS 8-day composite MOYDGL06* product was used as a base for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by the corresponding 8-day composite image of the MOYDGL06* product by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8-day composite snow extent. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory (RGI) Version 6.0 to make a more complete cryosphere product. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a probabilistic approach for deriving snow cover statistics from our final snow product. The pixels with values 200, 242, and 252 indicate snow in both Terra and Aqua and has a 100 % probability, whereas pixels with snow in one of the Terra or Aqua products have a 50 % probability. The data associated with this paper are available for the end-users mainly useful for observation and simulation of climate, hydro-glaciological forcings, calibration, validation, and other water-related studies. The data are available at https://doi.pangaea.de/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).
LUCAS Copernicus 2018: Earth Observation relevant in-situ data
on land cover throughout the European Union
Authors: d'Andrimont Raphaël , Verhegghen Astrid , Meroni Michele , Lemoine Guido , Strobl Peter , Eiselt Beatrice , Yordanov Momchil , Martinez-Sanchez Laura , van der Velde Marijn
Published: August 2020

Abstract. The Land Use/Cover Area frame Survey (LUCAS) is a regular in-situ land cover and land use ground survey exercise that extends over the whole of the European Union. LUCAS was carried out in 2006, 2009, 2012, 2015, and 2018. A new LUCAS module specifically tailored to Earth Observation was introduced in 2018: the LUCAS Copernicus module, aiming at surveying land cover extent up to 51 meters in four cardinal directions around a point of observation. This paper first summarizes the LUCAS Copernicus protocol to collect homogeneous land cover on a surface area of up to a 0.52 ha. Secondly, it proposes a methodology to create a ready-to-use dataset for Earth Observation land cover and land use applications with high resolution satellite imagery. As a result, a total of 63,364 LUCAS points distributed over 26 level-2 land cover classes were surveyed on the ground. Using homogeneous extent information in the four cardinal direction, a polygon was delineated for each of such point. Through geo-spatial analysis and by semantically linking the LUCAS core and Copernicus land cover observations, 58,428 polygons are provided with a level-3 land cover (66 specific classes including crop type) and land use (38 classes) information as inherited from the LUCAS core observation. The open-access dataset supplied with this manuscript (https://doi.org/10.6084/m9.figshare.12382667.v3) provides a unique opportunity to train and validate decametric sensor-based products such as those obtained from the Copernicus Sentinel-1 and -2 satellites. A follow-up of the LUCAS Copernicus module is already planned for 2022. In 2022, a simplified version of the LUCAS Copernicus module will be carried out on 150,000 LUCAS points for which in-situ surveying is planned. This guarantees a continuity in the effort to find synergies between statistical in-situ surveying and the need to collect in-situ data relevant for Earth Observation in the European Union.
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Authors: Brunner Manuela I. , Gilleland Eric
Published: August 2020

Abstract. Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but as yet unobserved streamflow time series with the same temporal and distributional characteristics as the observed data. However, the representation of non-stationarities and spatial dependence among sites remains a challenge in stochastic modeling. We investigate whether the use of frequency-domain instead of time-domain models allows for the joint simulation of realistic, continuous streamflow time series at daily resolution and spatial extremes at multiple sites. To do so, we propose the stochastic simulation approach called Phase Randomization Simulation using wavelets (PRSim.wave) which combines an empirical spatio-temporal model based on the wavelet transform and phase randomization with the flexible four-parameter kappa distribution. The approach consists of five steps: (1) derivation of random phases, (2) fitting of the kappa distribution, (3) wavelet transform, (4) inverse wavelet transform, and (5) transformation to kappa distribution. We apply and evaluate PRSim.wave on a large set of 671 catchments in the contiguous United States. We show that this approach allows for the generation of realistic time series at multiple sites exhibiting short- and long-range dependence, non-stationarities, and unobserved extreme events. Our evaluation results strongly suggest that the flexible, continuous simulation approach is potentially valuable for a diverse range of water management applications where the reproduction of spatial dependencies is of interest. Examples include the development of regional water management plans, the estimation of regional flood or drought risk, or the estimation of regional hydropower potential. Highlights.
Stochastic simulation of continuous streamflow time series using an empirical, wavelet-based, spatio-temporal model in combination with the parametric kappa distribution. Generation of stochastic time series at multiple sites showing temporal short- and long-range dependence, non-stationarities, and spatial dependence in extreme events. Implementation of PRSim.wave in R package PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization.
3D DATA GENERATION USING LOW-COST CROSS-VIEW IMAGES
Authors: Qin R. , Song S. , Huang X.
Published: August 2020

Abstract. 3D data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight. In cases such data are unavailable, for example, areas of interest inaccessible from aerial platforms, alternative sources to be considered can be quite heterogeneous and come in the form of different accuracy, resolution and views, which challenge the standard data processing workflows. Assuming only overview satellite and ground-level go-pro images are available, which we call cross-view data due to the significant view differences, this paper introduces a framework from our project, consisting of a few novel algorithms that convert such challenging dataset to 3D textured mesh models containing both top and façade features. The necessary methods include 3D point cloud generation from satellite overview images and ground-level images, geo-registration and meshing. We firstly introduce the problems and discuss the potential challenges and introduce our proposed methods to address these challenges. Finally, we practice our proposed framework on a dataset consisting of twelve satellite images and 150k video frames acquired through a vehicle-mounted Go-pro camera and demonstrate the reconstruction results. We have also compared our results with results generated from an intuitive processing pipeline that involves typical geo-registration and meshing methods.
USING MOBILE LASER SCANNING POINT CLOUDS TO EXTRACT URBAN ROADSIDE TREES FOR ECOLOGICAL BENEFITS ESTIMATION
Authors: Fan W. , Yang B. , Liang F. , Dong Z.
Published: August 2020

Abstract. Roadside trees in the city play a crucial role in addressing the issues of air pollution, urban heat island effects, road noise, and so on. This paper proposes an efficient and robust method to automatically extract individual roadside trees with morphological parameters from mobile laser scanning (MLS) point clouds for ecological benefits estimation. The proposed method consists of four steps: MLS data pre-processing, pole-like objects classification, individual tree extraction, morphological parameters calculation for ecological benefits estimation. The proposed method is verified using three complex datasets in Shanghai, China. Comprehensive experiments demonstrate that the proposed method achieves good performance in extracting individual tree in terms of average precision and recall (91.83%, 92.60%), and provides detailed information for ecological benefits estimation.
CLINIFACE: PHENOTYPIC VISUALISATION AND ANALYSIS USING NON-RIGID REGISTRATION OF 3D FACIAL IMAGES
Authors: Palmer R. L. , Helmholz P. , Baynam G.
Published: August 2020

Abstract. Facial appearance has long been understood to offer insight into a person’s health. To an experienced clinician, atypical facial features may signify the presence of an underlying rare or genetic disease. Clinicians use their knowledge of how disease affects facial appearance along with the patient’s physiological and behavioural traits, and their medical history, to determine a diagnosis. Specialist expertise and experience is needed to make a dysmorphological facial analysis. Key to this is accurately assessing how a face is significantly different in shape and/or growth compared to expected norms. Modern photogrammetric systems can acquire detailed 3D images of the face which can be used to conduct a facial analysis in software with greater precision than can be obtained in person. Measurements from 3D facial images are already used as an alternative to direct measurement using instruments such as tape measures, rulers, or callipers. However, the ability to take accurate measurements – whether virtual or not – presupposes the assessor’s facility to accurately place the endpoints of the measuring tool at the positions of standardised anatomical facial landmarks. In this paper, we formally introduce Cliniface – a free and open source application that uses a recently published highly precise method of detecting facial landmarks from 3D facial images by non-rigidly transforming an anthropometric mask (AM) to the target face. Inter-landmark measurements are then used to automatically identify facial traits that may be of clinical significance. Herein, we show how non-experts with minimal guidance can use Cliniface to extract facial anthropometrics from a 3D facial image at a level of accuracy comparable to an expert. We further show that Cliniface itself is able to extract the same measurements at a similar level of accuracy – completely automatically.
ORIENTATION OF POINT CLOUDS FOR COMPLEX SURFACES IN MEDICAL SURGERY USING TRINOCULAR VISUAL ODOMETRY AND STEREO ORB-SLAM2
Authors: Kahmen O. , Haase N. , Luhmann T.
Published: August 2020

Abstract. In photogrammetry, computer vision and robotics, visual odometry (VO) and SLAM algorithms are well-known methods to estimate camera poses from image sequences. When dealing with unknown scenes there is often no reference data available and also the scene needs to be reconstructed for further analysis. In this contribution a trinocular visual odometry approach is implemented and compared to stereo VO and ORB-SLAM2 in an experimental setup imitating the scene of a knee replacement surgery. Two datasets are analysed. While a test-field provides excellent conditions for feature detection algorithms with its artificial texture assembled, extracted images show the knee joint itself solely in order to use only the homogenous, but in real application stable, region of the knee joint. The camera trajectories of VO and ORB-SLAM2 are transformed to corresponding coordinate systems and are subsequently evaluated. The tracking algorithms show poor quality when only the inappropriate surface of the knee is used but perform well when the artificial texture of the test-field is used. The third camera does not lead to a significant advantage in this setup using our implementation. Possible reasons, e.g. less overlap, are discussed in this contribution. Nevertheless, the quality of the oriented point clouds, obtained by trinocular dense matching, is less than 1mm for most of the analysed data. The experiment will be used to focus on further developments, e.g. dealing with specular reflections, and for evaluation purposes using different SLAM/ VO algorithms.
COMPUTED TOMOGRAPHY DATA COLOURING BASED ON PHOTOGRAMMETRIC IMAGES
Authors: Zhan K. , Song Y. , Fritsch D. , Mammadov G. , Wagner J.
Published: August 2020

Abstract. Nowadays various methods and sensors are available for 3D reconstruction tasks; however, it is still necessary to integrate advantages of different technologies for optimizing the quality 3D models. Computed tomography (CT) is an imaging technique which takes a large number of radiographic measurements from different angles, in order to generate slices of the object, however, without colour information. The aim of this study is to put forward a framework to extract colour information from photogrammetric images for corresponding Computed Tomography (CT) surface data with high precision. The 3D models of the same object from CT and photogrammetry methods are generated respectively, and a transformation matrix is determined to align the extracted CT surface to the photogrammetric point cloud through a coarse-to-fine registration process. The estimated pose information of images to the photogrammetric point clouds, which can be obtained from the standard image alignment procedure, also applies to the aligned CT surface data. For each camera pose, a depth image of CT data is calculated by projecting all the CT points to the image plane. The depth image is in principle should agree with the corresponding photogrammetric image. The points, which cannot be seen from the pose, but are also projected on the depth image, are excluded from the colouring process. This is realized by comparing the range values of neighbouring pixels and finding the corresponding 3D points with larger range values. The same procedure is implemented for all the image poses to obtain the coloured CT surface. Thus, by using photogrammetric images, we achieve a coloured CT dataset with high precision, which combines the advantages from both methods. Rather than simply stitching different data, we deep-dive into the photogrammetric 3D reconstruction process and optimize the CT data with colour information. This process can also provide an initial route and more options for other data fusion processes.
LONG-SHORT SKIP CONNECTIONS IN DEEP NEURAL NETWORKS FOR DSM REFINEMENT
Authors: Bittner K. , Liebel L. , Körner M. , Reinartz P.
Published: August 2020

Abstract. Detailed digital surface models (DSMs) from space-borne sensors are the key to successful solutions for many remote sensing problems, like environmental disaster simulations, change detection in rural and urban areas, 3D urban modeling for city planning and management, etc. Traditional methodologies, e.g., stereo matching, used to generate photogrammetric DSMs from stereo imagery, usually deliver low-quality results due to the matching errors in homogeneous areas or the lack of information when observing the scene under different viewing angles. This makes the tasks related to building reconstruction very challenging since in most cases it is difficult to recognize the type of roofs, especially if overlaid with trees. This work represents a continuation of research regarding the automatic optimization of building geometries in photogrammetric DSMs with half-meter resolution and introduces an improved generative adversarial network (GAN) architecture which allows to reconstruct complete and detailed building structures without neglecting even low-rise urban constructions. The generative part of the network is constructed in a way that it simultaneously processes height and intensity information, and combines short and long skip connections within one architecture. To improve different aspects of the surface, several loss terms are used, the contributions of which are automatically balanced during training. The obtained results demonstrate that the proposed methodology can achieve two goals without any manual intervention: improve the roof surfaces by making them more planar and also recognize and optimize even small residential buildings which are hard to detect.
REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING
Authors: Kim H. , Ham S. , Lee I.
Published: August 2020

Abstract. Facilities such as road, parking lots play an important role in our lives nowadays. Damage to such a vehicle facility can cause human injury, as well as inconvenience and cost. To prevent this, facility monitoring is performed periodically, but the current monitoring method is low efficiency by blocking the facility or performing it late at night. In order to increase the efficiency of monitoring, research using images, especially drone images, was conducted. However, when using a drone image, there is a trade-off relationship between accuracy and processing time. In this study, we propose a real-time drone mapping based on reference images for efficient vehicle facility monitoring. The real-time drone mapping based on the reference image is composed of reference images build, aerial triangulation (AT) based on reference images (refAT), and orthophoto generation. The refAT refers to a method of performing AT by using a reference images as reference data. We compared the processing time and processing accuracy of direct georeferencing and refAT. We built 154 drone reference images in the target area. The refAT showed a processing time of about 8.95 seconds and an accuracy of 3.4 cm, and the direct georeferencing method showed a processing time of about 1.49 seconds and an accuracy of 22.5 m. If the method of this study is used for facility monitoring, it is expected that the efficiency of monitoring will be improved with speed and accuracy.
STUDIES ON EXTERNAL GEOMETRY OF A PLANE WITH PHOTOGRAMMETRIC METHODS AND LASER SCANNING
Authors: Mikrut S. , Brzęczek J.
Published: August 2020

Abstract. The authors of the paper were looking for a simple methods of the examination the airframe structure framework as part of the scientific and research project. The main tasks were: carrying out tests and analyses in defining external geometry of a selected plane with three independent methods i.e., geodetic (surveying), photogrammetric and laser scanning and selection of one of these methods as fast and acceptable accurate (below 3 mm) measurement applied to examine plane geometry. The carried out experiments were mainly aimed at answering the question if tested measurement methods give satisfying and acceptable results in case of works referring to the objects such as plane and if the assumed accuracy is realistic to be achieved by these measurement techniques. The authors testing the available on the market methods of the measuring the geometry of planes in the aspect of the selection of most accurate, but at the same time, fast measurement method. Thus three independent methods were tested: classical geodetic measurement, laser scanning by means of two independent scanners and photogrammetric method (based on the generated cloud of points). Experiments carried out so far in various centres worldwide usually included measurements of single elements or small models of planes. In surveying measurements of large objects, mainly buildings are predominant (e.g. surveying or photogrammetry in the inventory of historic architectural monuments, studies of the geometry of engineering constructions, etc.). The objective of the experiment carried out by the authors was to find good solution somewhere in the middle, i.e., apply geodetic measurement methods, but apply them for so-called medium-size objects such as planes.
AUTOMATIC 3D BUILDINGS COMPACT RECONSTRUCTION FROM LIDAR POINT CLOUDS
Authors: Nys G.-A. , Billen R. , Poux F.
Published: August 2020

Abstract. Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The generation of consistent large-scale 3D city models from this real-world data is a major challenge. Specifically, the integration in workflows usable by decision-making scenarios demands that the data is structured, rich and exchangeable. CityGML permits new advances in terms of interoperable endeavour to use city models in a collaborative way. Efforts have led to render good-looking digital twins of cities but few of them take into account their potential use in finite elements simulations (wind, floods, heat radiation model, etc.). In this paper, we target the automatic reconstruction of consistent 3D city buildings highlighting closed solids, coherent surface junctions, perfect snapping of vertices, etc. It specifically investigates the topological and geometrical consistency of generated models from aerial LiDAR point cloud, formatted following the CityJSON specifications. These models are then usable to store relevant information and provides geometries usable within complex computations such as computational fluid dynamics, free of local inconsistencies (e.g. holes and unclosed solids).
SATELLITE IMAGING DIRECTION ANGLES ESTIMATION METHOD BASED ON RATIONAL POLYNOMIAL COEFFICIENTS
Authors: Yi H. , Chen X. , Wang D. , Du S. , Guo N. , Ma Y.
Published: August 2020

Abstract. Satellite imaging direction angles, including the azimuth angle and the incidence angle, are the basic information used for satellite camera network structure analysis. They play an important role in 3D reconstruction using satellite images. In this paper, a satellite imaging direction angle estimation method based on rational polynomial coefficients is proposed for use when the satellite imaging direction angles are not available. Using rational polynomial coefficients, a vertical line on the ground is projected into the image plane, and the satellite imaging direction angles are estimated by analyzing the projection. Satellite images acquired by SPOT6, SOPT7 and Pleiades with different satellite imaging direction angles were used to test the feasibility of the proposed method. The experimental results were analyzed in detail combined with the method and the data. The experimental results show that the azimuth angle estimation error is less than 1.30 degrees, and the incidence angle estimation error is less than 0.83 degrees. This level of accuracy is sufficient for satellite camera network structure analysis.
ADVANCED APPROACH FOR AUTOMATIC RECONSTRUCTION OF 3D BUILDINGS FROM AERIAL IMAGES
Authors: Yu D. , Wei S. , Liu J. , Ji S.
Published: August 2020

Abstract. In this work, a novel automatic 3D building reconstruction approach is proposed to extract accurate LoD1 building models from multi-view aerial images. The proposed approach consists of three main parts. The first step is to generate digital surface models (DSMs) from aerial images, which is implemented with the Smart3D software and can be replaced by other open-source multi-view stereo (MVS) algorithms as well. The second step is to produce structured 2D building footprints using combined deep learning and regularization. The initial building segmentation maps are obtained by the multi-scale aggregation fully convolutional network (MA-FCN), which takes both the images and DSM as input, through supervised learning. The initial segmentation maps are further refined with another segmentation maps that are derived from the DSM. After that, the contour extraction and regularization technology are applied to produce structured building footprints. In the last step, the elevations of the top and base of each individual building are reliably estimated by adopting an adaptive terrain generation approach and the neighbourhood buffer analysis. The georeferenced building footprint polygons and the elevations of building top and base form the watertight LoD1 building models. The qualitative and quantitative evaluations in Jinghai District, Tianjin, China demonstrate the robustness and effectiveness of the proposed approach.
SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM
Authors: Vishnyakov B. , Blokhinov Y. , Sgibnev I. , Sheverdin V. , Sorokin A. , Nikanorov A. , Masalov P. , Kazakhmedov K. , Brianskiy S. , Andrienko Е. , Vizilter Y.
Published: August 2020

Abstract. In this paper we describe a new multi-sensor platform for data collection and algorithm testing. We propose a couple of methods for solution of semantic scene understanding problem for land autonomous vehicles. We describe our approaches for automatic camera and LiDAR calibration; three-dimensional scene reconstruction and odometry calculation; semantic segmentation that provides obstacle recognition and underlying surface classification; object detection; point cloud segmentation. Also, we describe our virtual simulation complex based on Unreal Engine, that can be used for both data collection and algorithm testing. We collected a large database of field and virtual data: more than 1,000,000 real images with corresponding LiDAR data and more than 3,500,000 simulated images with corresponding LiDAR data. All proposed methods were implemented and tested on our autonomous platform; accuracy estimates were obtained on the collected database.
LEARNING THE 3D POSE OF VEHICLES FROM 2D VEHICLE PATCHES
Authors: Koetsier C. , Peters T. , Sester M.
Published: August 2020

Abstract. Estimating vehicle poses is crucial for generating precise movement trajectories from (surveillance) camera data. Additionally for real time applications this task has to be solved in an efficient way. In this paper we introduce a deep convolutional neural network for pose estimation of vehicles from image patches. For a given 2D image patch our approach estimates the 2D coordinates of the image representing the exact center ground point (cx, cy) and the orientation of the vehicle - represented by the elevation angle (e) of the camera with respect to the vehicle’s center ground point and the azimuth rotation (a) of the vehicle with respect to the camera. To train a accurate model a large and diverse training dataset is needed. Collecting and labeling such large amount of data is very time consuming and expensive. Due to the lack of a sufficient amount of training data we show furthermore, that also rendered 3D vehicle models with artificial generated textures are nearly adequate for training.
HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS
Authors: Li Z. , Zhang W. , Shan J.
Published: August 2020

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.
IMPROVING DEEP LEARNING BASED SEMANTIC SEGMENTATION WITH MULTI VIEW OUTLIER CORRECTION
Authors: Peters T. , Brenner C. , Song M.
Published: August 2020

Abstract. The goal of this paper is to use transfer learning for semi supervised semantic segmentation in 2D images: given a pretrained deep convolutional network (DCNN), our aim is to adapt it to a new camera-sensor system by enforcing predictions to be consistent for the same object in space. This is enabled by projecting 3D object points into multi-view 2D images. Since every 3D object point is usually mapped to a number of 2D images, each of which undergoes a pixelwise classification using the pretrained DCNN, we obtain a number of predictions (labels) for the same object point. This makes it possible to detect and correct outlier predictions. Ultimately, we retrain the DCNN on the corrected dataset in order to adapt the network to the new input data. We demonstrate the effectiveness of our approach on a mobile mapping dataset containing over 10’000 images and more than 1 billion 3D points. Moreover, we manually annotated a subset of the mobile mapping images and show that we were able to rise the mean intersection over union (mIoU) by approximately 10% with Deeplabv3+, using our approach.
A NOVEL SELF-TAUGHT LEARNING FRAMEWORK USING SPATIAL PYRAMID MATCHING FOR SCENE CLASSIFICATION
Authors: Yang Y. , Zhu D. , Ren F. , Cheng C.
Published: August 2020

Abstract. Remote sensing earth observation images have a wide range of applications in areas like urban planning, agriculture, environment monitoring, etc. While the industrial world benefits from availability of high resolution earth observation images since recent years, interpreting such images has become more challenging than ever. Among many machine learning based methods that have worked out successfully in remote sensing scene classification, spatial pyramid matching using sparse coding (ScSPM) is a classical model that has achieved promising classification accuracy on many benchmark data sets. ScSPM is a three-stage algorithm, composed of dictionary learning, sparse representation and classification. It is generally believed that in the dictionary learning stage, although unsupervised, one should use the same data set as classification stage to get good results. However, recent studies in transfer learning suggest that it might be a better strategy to train the dictionary on a larger data set different from the one to classify.In our work, we propose an algorithm that combines ScSPM with self-taught learning, a transfer learning framework that trains a dictionary on an unlabeled data set and uses it for multiple classification tasks. In the experiments, we learn the dictionary on Caltech-101 data set, and classify two remote sensing scene image data sets: UC Merced LandUse data set and Changping data set. Experimental results show that the classification accuracy of proposed method is compatible to that of ScSPM. Our work thus provides a new way to reduce resource cost in learning a remote sensing scene image classifier.