bluuebunny/crossref_metadata_2025 huggingface dataset:
Decision Making and Machine Learning Algorithms’ Selection with Artificial Intelligent Rule-Based Expert System
Authors: MURAINA Ismail Olaniyi , AGOI Moses Adeolu , OMOROJOR Benjamin Oghomena , ADEDOKUN Akeem Ademola , AJETUNMOBI Rasheed Olatunde
Published: May 2022

Everybody is confronted daily with cluster of decisions that must be appropriately taken in the process of making accurate decision; individuals are faced with and most often fall prey to series of common biases, fallacies, and many other decision making odds. In determining which algorithm to apply for analysis (with machine learning algorithms/models) open to critical steps to be taken and also highly depend on many factors ranging from the type of problem at hand, the condition to choose a model and to the expected outcomes. The study looks at how artificial intelligent approach with expert system would be helpful in making timely decision on which type of algorithm(s) is/are capable to be applied and implemented to have desired results. The study also uses VisiRule software to model series of successful channels to arrive at a good decision making means. The use of VisiRule (Artificial Intelligent Based Expert System) was employed to give directional path ways to the selection of appropriate algorithms from supervised and unsupervised machine learning to different classification methods, regression methods, clustering approaches, dimensionality reduction methods, and association rules. The outcome of this study demonstrates the easy way through paths to select relevant and most appropriate model or algorithm that best fit the analysis at hand with detailed explanation of each alternative option. The use of VisiRule software has proven the easy way to achieve decision making problems without any codes requirement for such actions. Decision making challenges could be resolved by just implementing artificial intelligent rule-based expert system which require less time, coding free, and highly achievable accurate outcomes.
Modelo potencialista: a lógica da necessidade e da possibilidade na filosofia clássica
Authors: Contarato Thiago Sebastião Reis
Published: May 2022

Apresentaremos algumas semânticas que descrevem logicamente o uso das modalidades aléticas: necessidade, possibilidade, contingência e impossibilidade. A descrição lógica largamente reconhecida é realizada segundo o modelo da Semântica de Mundos Possíveis, mas defenderei que esse modelo é insuficiente para descrever as modalidades aléticas em sistemas filosóficos que admitam que há somente um único mundo. Nesse contexto, Tomás de Aquino será nossa principal referência, mas também faremos apontamentos segundo Aristóteles, Tomás de Aquino descreve as modalidades aléticas se utilizando das noções de potência e ato. Contudo, de acordo com a necessidade dos temas da filosofia medieval, Tomás precisou aprofundar e refinar essas noções. As nossas análises desse aprofundamento e refinamento desembocarão em um Modelo Potencialista.
RELIGION AND SECULAR EDUCATION IN THE UPBRINGING OF THE CONSCIOUS GENERATION
Authors:   Abai Kazakh National pedagogical university, Zhumagulov M.О.
Published: May 2022

The proposed article describes the place and role of secular education and religion in the upbringing of the conscious generation. Major political events of the 90s of the XX century had a profound impact on the spiritual values of society. Based on the analysis of scientific works written within the framework of this topic, the author dwells on the nature of the concept of secular education and describes its influence on the formation of personality. The formation of knowledge and skills necessary for young people to properly use the freedom of religion guaranteed by the Constitution is carried out by the family and educational institutions. However, religious communities enjoy special trust in Kazakh society. Despite the fact that the education and upbringing of young people in the secular plan is placed under the competence of secondary education institutions, the people consider religious associations to be one of the institutions of spiritual, moral and patriotic education. Achieving the right balance of secular and religious education contributes to the integration of the values of the individual and the social environment. The correlation of secular education and religion, pursuing a common cultural, ideological, systemic and educational goal, serves the national interests of the Kazakh people.
HUMAN AND HUMANITY: SOCIAL ASPECTS
Authors:   Abai Kazakh National Pedagogical University, Almaty, Kazakhstan, Absattarov R. , Rau I. ,   Academy of Leading Personnel of the Bundeswehr, Hamburg, Germany
Published: May 2022

The article deals with the social aspects of human and humanity, which have not yet been sufficiently studied in sociological and political science. The article in more detail discusses the issues - general in the definition of a person, altruism as a fundamental value of a person, a human and an animal, a human and human-likeness, about "extra" people, the number of mankind, about the future of mankind. The article notes that a person who is a product of culture, since the latter appeared, has the ability to move from culture to culture. In the early stages of a child's development, this is a local culture; in later stages, it is regional or global. At the same time, human is a product of biological evolution, which confirms all the organs of his body, including his brain. Humanity is not biologically transmitted from biological fathers and mothers. It is given along with the soul, its potentialities of freedom and creativity. Also, the article pays attention to debatable issues.
EDUCATIONAL MIGRATION: REASONS AND FEATURES OF OBTAINING HIGHER EDUCATION ABROAD
Authors:   Al-Farabi Kazakh national university,Almaty Kazakhstan, Kalmukhanbetova M. , Shnarbekova M. ,   Al-Farabi Kazakh national university,Almaty Kazakhstan
Published: May 2022

The article discusses migration trends occurring in the world, migration in higher education. International educational migration is rapidly developing in the modern world. Educational migration largely determines the dynamics and nature of the country's economic development. In this context, an increasing problem for Kazakhstan is the regulation of educational migration in conditions of fierce competition in the international market of educational services. In this regard, the term «knowledge migration» appears in the global educational space, and this trend is becoming relevant in relation to all countries of the world. The importance of professional education and the formation of a multicultural and universal personality in the context of global globalization has increased. Students' perception of the culture of other countries as values, skills of using a foreign language in the professional sphere are highly valued by modern employers in establishing long-term ties with foreign partners. It can even be emphasized that knowledge of a foreign language is a kind of means of socialization and adaptation in a multicultural society. These social changes cause international educational migration and become a subject requiring public study.
Detection of Stratospheric Air Intrusion Events From Ground-based High-resolution 10Be / 7Be by Accelerator Mass Spectrometry
Authors: Liu Xu-Ke , Fu Yun-Chong , Zhang Li , Burr George S. , Bi Yan-Ting , Zhao Guo-Qing
Published: May 2022

Abstract. Locally rapid stratospheric air intrusions facilitate the transport of stratospheric material to the troposphere. Long-term continuous monitoring of such events by traditional techniques, such as sounding technology, is challenging. Beryllium-7 (7Be) and beryllium-10 (10Be) offer an alternative. These isotopes are formed by cosmic rays and are mainly produced in the lower stratosphere and upper troposphere. Due to their similar geochemical properties and substantial difference in half-lives favor relatively high 10Be / 7Be ratios in the stratosphere, as compared to the troposphere. Monitoring surface 10Be / 7Be ratios affords a potential means to identify stratospheric air intrusions. However, high temporal resolution 10Be / 7Be observational records must be taken and corrected for dust-borne 10Be to identify stratospheric air intrusions. In this study, we use Accelerator Mass Spectrometry to measure both 7Be and 10Be in rain and aerosol (down to ~ 200 cubic meters air) with an error of ~ 1.5 %. We correct for dust-borne 10Be using soil Al. This method provides precise measurements with daily resolution. We present annual beryllium isotopes (7Be, 10Be, and 10Be / 7Be ratio) record for the Chinese Loess Plateau that includes several regional sites. We show that for the city of Xi'an, the proportion of dust-borne (resuspended) 10Be was ~24 % in 2020/21. Our results confirm that stratospheric air intrusion events in the Loess Plateau are frequent and rapid throughout the year and are strongest in the spring (March–July), when 10Be / 7Be values were observed to increase about a factor of 3. Even in winter, weaker stratospheric air intrusion events can be detected. Calculated Δ(10Be / 7Be) values in winter suggest stratospheric ozone transport can lead to an ~25 % cumulative increase the surface ozone.
Boundary-layer height and surface stability at Hyytiälä, Finland, in ERA5 and observations
Authors: Sinclair Victoria Anne , Ritvanen Jenna , Urbancic Gabin , Erner Irene , Batrak Yurii , Moisseev Dmitri , Kurppa Mona
Published: May 2022

Abstract. We investigate the boundary-layer (BL) height at Hyytiälä in southern Finland diagnosed from radiosonde observations, a microwave radiometer (MWR) and ERA5 reanalysis. Four different, pre-existing algorithms are used to diagnose the BL height from the radiosondes. The diagnosed BL height is sensitive to the method used. The level of agreement, and the sign of systematic bias between the four different methods, depends on the surface-layer stability. For very unstable situations, the median BL height diagnosed from the radiosondes varies from 600 to 1500 m depending on which method is applied. Good agreement between the BL height in ERA5 and diagnosed from the radiosondes using Richardson-number-based methods is found for almost all stability classes, suggesting that ERA5 has adequate vertical resolution near the surface to resolve the BL structure. However, ERA5 overestimates the BL height in very stable conditions, highlighting the ongoing challenge for numerical models to correctly resolve the stable BL. Furthermore, ERA5 BL height differs most from the radiosondes at 18:00 UTC, suggesting ERA5 does not resolve the evening transition correctly. BL height estimates from the MWR are also found to be reliable in unstable situations but often are inaccurate under stable conditions when, in comparison to ERA5 BL heights, they are much deeper. The errors in the MWR BL height estimates originate from the limitations of the manufacturer's algorithm for stable conditions and also the misidentification of the type of BL. A climatology of the annual and diurnal cycle of BL height, based on ERA5 data, and surface-layer stability, based on eddy covariance observations, was created. The shallowest (353 m) monthly median BL height occurs in February and the deepest (576 m) in June. In winter there is no diurnal cycle in BL height; unstable BLs are rare, yet so are very stable BLs. The shallowest BLs occur at night in spring and summer, and very stable conditions are most common at night in the warm season. Finally, using ERA5 gridded data, we determined that the BL height observed at Hyytiälä is representative of most land areas in southern and central Finland. However, the spatial variability of the BL height is largest during daytime in summer, reducing the area over which BL height observations from Hyytiälä would be representative.
Downscaling of climate change scenarios for a high-resolution, site-specific assessment of drought stress risk for two viticultural regions with heterogeneous landscapes
Authors: Hofmann Marco , Volosciuk Claudia , Dubrovský Martin , Maraun Douglas , Schultz Hans R.
Published: May 2022

Abstract. Extended periods without precipitation, observed for example in central Europe including Germany during the seasons from 2018 to 2020, can lead to water deficit and yield and quality losses for grape and wine production. Irrigation infrastructure in these regions to possibly overcome negative effects is largely non-existent. Regional climate models project changes in precipitation amounts and patterns, indicating an increase in frequency of the occurrence of comparable situations in the future. In order to assess possible impacts of climate change on the water budget of grapevines, a water balance model was developed, which accounts for the large heterogeneity of vineyards with respect to their soil water storage capacity, evapotranspiration as a function of slope and aspect, and
viticultural management practices. The model was fed with data from soil
maps (soil type and plant-available water capacity), a digital elevation
model, the European Union (EU) vineyard-register, observed weather data, and
future weather data simulated by regional climate models and downscaled by a
stochastic weather generator. This allowed conducting a risk assessment of
the drought stress occurrence for the wine-producing regions Rheingau and
Hessische Bergstraße in Germany on the scale of individual vineyard
plots. The simulations showed that the risk for drought stress varies
substantially between vineyard sites but might increase for steep-slope
regions in the future. Possible adaptation measures depend highly on local
conditions and are needed to make targeted use of water resources, while
an intense interplay of different wine-industry stakeholders, research,
knowledge transfer, and local authorities will be required.
Concentrations and fluxes of suspended particulate matter and associated contaminants in the Rhône River from Lake Geneva to the Mediterranean Sea
Authors: Lepage Hugo , Gruat Alexandra , Thollet Fabien , Le Coz Jérôme , Coquery Marina , Masson Matthieu , Dabrin Aymeric , Radakovitch Olivier , Labille Jérôme , Ambrosi Jean-Paul , Delanghe Doriane , Raimbault Patrick
Published: May 2022

Abstract. The Rhône River is among the main rivers of western Europe and the
biggest by freshwater discharge and sediment delivery to the Mediterranean
Sea. Its catchment is characterized by distinct hydrological regimes that
may produce annual sediment deliveries ranging from 1.4 to
18.0 Mt yr−1.
Its course meets numerous dams, hydro and nuclear power plants as well as
agricultural, urban and industrial areas. Moreover, with the climatic crisis
we are currently facing, it is proven that the occurrence and the intensity
of extreme events (floods or droughts) will increase. Therefore, it is
crucial to monitor the concentrations and fluxes of suspended particulate
matter (SPM) and associated contaminants to study the current trends and
their evolution. In the Rhône River (from Lake Geneva to the
Mediterranean Sea), a monitoring network of 15 stations (3 on the
Rhône River and 12 on tributaries) has been set up in the past decade by
the Rhône Sediment Observatory (OSR) to investigate the concentrations
and the fluxes of SPM and associated contaminants as well as their sources.
The main purpose of the OSR is to assess the long-term trend of the
main contaminant concentrations and fluxes, and to understand their behavior
during extreme events, such as floods or dam flushing operations. The dataset
presented in this paper contains the concentrations and fluxes of SPM as
well as the concentrations and fluxes of several particle-bound contaminants
of concern, e.g., polychlorinated biphenyl (PCB), trace metal elements (TME) and radionuclides, the particle size distribution and the
particulate organic carbon of SPM. Sediment traps or continuous flow
centrifuges were used to collect sufficient amount of SPM in order to
conduct the measurements, and data completion was applied to reconstruct
missing values. This observatory is on-going since 2011 and the database is
regularly updated. All the data are made publicly available in French and
English through the BDOH OSR database at https://doi.org/10.15454/RJCQZ7 (Lepage et al., 2021).
Improving age–depth relationships by using the LANDO (“Linked age and depth modeling”) model ensemble
Authors: Pfalz Gregor , Diekmann Bernhard , Freytag Johann-Christoph , Syrykh Liudmila , Subetto Dmitry A. , Biskaborn Boris K.
Published: May 2022

Abstract. Age–depth relationships are the key elements in paleoenvironmental studies
to place proxy measurements into a temporal context. However, potential
influencing factors of the available radiocarbon data and the associated
modeling process can cause serious divergences of age–depth relationships
from true chronologies, which is particularly challenging for
paleolimnological studies in Arctic regions. This paper provides
geoscientists with a tool-assisted approach to compare outputs from
age–depth modeling systems and to strengthen the robustness of age–depth
relationships. We primarily focused on the development of age determination
data from a data collection of high-latitude lake systems (50 to 90∘ N, 55 sediment cores, and a total of 602 dating points).
Our approach used five age–depth modeling systems (Bacon, Bchron, clam, hamstr, Undatable) that we linked through
a multi-language Jupyter Notebook called LANDO (“Linked age and depth
modeling”). Within LANDO we implemented a pipeline from data
integration to model comparison to allow users to investigate the outputs of the modeling systems. In this paper, we focused on highlighting three
different case studies: comparing multiple modeling systems for one sediment
core with a continuously deposited succession of dating points (CS1), for
one sediment core with scattered dating points (CS2), and for multiple
sediment cores (CS3). For the first case study (CS1), we showed how we
facilitate the output data from all modeling systems to create an ensemble
age–depth model. In the special case of scattered dating points (CS2), we
introduced an adapted method that uses independent proxy data to assess the
performance of each modeling system in representing lithological changes.
Based on this evaluation, we reproduced the characteristics of an existing
age–depth model (Lake Ilirney, EN18208) without removing age determination
data. For multiple sediment cores (CS3) we found that when considering the
Pleistocene–Holocene transition, the main regime changes in sedimentation
rates do not occur synchronously for all lakes. We linked this behavior to
the uncertainty within the dating and modeling process, as well as the local variability in catchment settings affecting the accumulation rates of the sediment cores within the collection near the glacial–interglacial
transition.
GRAPH-THEORETICAL APPROACH TO ROBUST 3D NORMAL EXTRACTION OF LIDAR DATA
Authors: Kusari A. , Sun W.
Published: May 2022

Abstract. Low dimensional primitive feature extraction from LiDAR point clouds (such as planes) forms the basis of majority of LiDAR data processing tasks. A major challenge in LiDAR data analysis arises from the irregular nature of LiDAR data that forces practitioners to either regularize the data using some form of gridding or utilize a triangular mesh such as triangulated irregular network (TIN). While there have been a handful applications using LiDAR data as a connected graph, a principled treatment of utilizing graph-theoretical approach for LiDAR data modelling is still lacking. In this paper, we try to bridge this gap by utilizing graphical approach for normal estimation from LiDAR point clouds. We formulate the normal estimation problem in an optimization framework, where we find the corresponding normal vector for each LiDAR point by utilizing its nearest neighbors and simultaneously enforcing a graph smoothness assumption based on point samples. This is a non-linear constrained convex optimization problem which can then be solved using projected conjugate gradient descent to yield an unique solution. As an enhancement to our optimization problem, we also provide different weighted solutions based on the dot product of the normals and Euclidean distance between the points. In order to assess the performance of our proposed normal extraction method and weighting strategies, we first provide a detailed analysis on repeated randomly generated datasets with four different noise levels and four different tuning parameters. Finally, we benchmark our proposed method against existing state-of-the-art approaches on a large scale synthetic plane extraction dataset. The code for the proposed approach along with the simulations and benchmarking is available at https://github.com/arpan-kusari/graph-plane-extraction-simulation.
RAILWAY LIDAR SEMANTIC SEGMENTATION WITH AXIALLY SYMMETRICAL CONVOLUTIONAL LEARNING
Authors: Manier A. , Moras J. , Michelin J.-C. , Piet-Lahanier H.
Published: May 2022

Abstract. This paper presents a new deep-learning-based method for 3D Point Cloud Semantic Segmentation specifically designed for processing real-world LIDAR railway scenes. The new approach relies on the use of spatial local point cloud transformations for convolutional learning. These transformations allow an increased robustness to varying point cloud densities while preserving metric information and a sufficient descriptive ability. The resulting performances are illustrated with results on railway data from two distinct LIDAR point cloud datasets acquired in industrial settings. The quality of the extraction of useful information for maintenance operations and topological analysis is pointed together with a noticeable robustness to point cloud variations in distribution and point redundancy.
IMPROVING PAIRWISE DSM WITH 3SGM: A SEMANTIC SEGMENTATION FOR SGM USING AN AUTOMATICALLY REFINED NEURAL NETWORK
Authors: Dumas L. , Defonte V. , Steux Y. , Sarrazin E.
Published: May 2022

Abstract. The amount of very high resolution optical satellite images at our disposal is continuously increasing. Besides, associated satellite programs often come with high revisit rates and geometric properties that allow for either opportunistic or by-design 3D stereo reconstruction. Digital Surface Models (DSM) computed from these satellite images offer new possibilities. In the past, the high revisit rate has largely benefited glacier monitoring studies. Now, DSM with increased resolution provided on urban areas can be used for smart city applications as well. However, most of these require 3D modeling of buildings with level of details ranging from 0 to 2. This is where the need for better reconstructed buildings inside DSM arises. Indeed, building edges and corners tend to be smoothed and softened by the stereo matching step of a DSM computation pipeline. This undesired behavior can mostly be linked to the difficult task of optimizing the Disparity Space Image, thus finding good balance between smoothing untextured areas while conserving sharp discontinuities where needed. In this paper, we show how the optimization can benefit from an input building semantic segmentation. We also provide a method to create it from a very high satellite image in epipolar geometry using a convolutional neural network. To help our network generalize well on unseen areas we propose an interactive learning method based on clicked annotations. Eventually, we show that annotations can be automatically created, hence removing the need for an operator and making our solution suitable for operational conditions.
AN INVESTIGATION OF THE INFLUENCES OF TRAINING DATA PROPERTIES ON AERIAL IMAGE SEGMENTATION
Authors: Dinga G. T.
Published: May 2022

Abstract. While numerous studies are being conducted to improve neural network performance for image segmentation, studies on the impact of training data in terms of data quality, bias and labeling noise are comparatively scarce. When opening state of the art algorithms to a large and varying dataset, they do not achieve the same results as under optimal and controlled conditions due to a mismatch of the data used for training and the data that is to be predicted. This paper presents an approach to show the influences of diverging image properties such as scale, contrast, brightness and saturation between the training data of a model and data that is to be predicted. For this purpose, a U-Net is trained to segment buildings in aerial images. It was found that while changes in brightness have a strong effect on precision, recall and F-score, a change in saturation does not have too much or even positive effect on segmentation. In general, however, it can be said that any differences between training and prediction data have a negative effect on segmentation results.
LEARNING FROM THE PAST: CROWD-DRIVEN ACTIVE TRANSFER LEARNING FOR SEMANTIC SEGMENTATION OF MULTI-TEMPORAL 3D POINT CLOUDS
Authors: Kölle M. , Walter V. , Soergel U.
Published: May 2022

Abstract. The main bottleneck of machine learning systems, such as convolutional neural networks, is the availability of labeled training data. Hence, much effort (and thus cost) is caused by setting up proper training data sets. However, models trained on specific data sets often perform unsatisfactorily when used to derive predictions for another (yet related) data set. We aim to overcome this problem by employing active learning to iteratively adapt an existing classifier to another domain. Precisely, we are concerned with semantic segmentation of 3D point clouds of multiple epochs. We first establish a Random Forest classifier for the first epoch of our data set and adapt it for successful prediction to two more temporally disjoint point clouds of the same but extended area. The point clouds, which are part of the newly introduced Hessigheim 3D benchmark data set, incorporate different characteristics with respect to the acquisition date and sensor configuration. We demonstrate that our workflow for domain adaptation is designed in such a way that it i) offers the possibility to greatly reduce labeling effort compared to a passive learning baseline or to an active learning baseline trained from scratch, if the domain gap is small enough and ii) at least does not cause more expenses (compared to a newly initialized active learning loop), if the domain gap is severe. The latter is especially beneficial in scenarios where the similarity of two different domains is hard to assess.
MULTI-MODAL SEMANTIC MESH SEGMENTATION IN URBAN SCENES
Authors: Laupheimer D. , Haala N.
Published: May 2022

Abstract. The semantic segmentation of the huge amount of acquired 3D data has become an important task in recent years. Meshes have evolved into a standard representation next to Point Clouds (PCs) – not least because of their great visualization possibilities. Compared to PCs, meshes have commonly smaller memory footprints while jointly providing geometrical and high-resolution textural information. For this reason, we opt for semantic mesh segmentation, which is a widely overlooked topic in photogrammetry and remote sensing yet. In this work, we perform an extensive ablation study on multi-modal handcrafted features adapting the Point Cloud Mesh Association (PCMA) (Laupheimer et al., 2020) which establishes explicit connections between faces and points. The multi-modal connections are used in a two-fold manner: (i) to extend per-face descriptors with features engineered on the PC and (ii) to annotate meshes semi-automatically by propagating the manually assigned labels from the PCs. In this way, we derive annotated meshes from the ISPRS benchmark data sets Vaihingen 3D (V3D) and Hessigheim 3D (H3D). To demonstrate the effectiveness of the multi-modal approach, we use well-established and fast Random Forest (RF) models deploying various feature vector compositions and analyze their performances for semantic mesh segmentation. The feature vector compositions consider features derived from the mesh, the PC or both. The results indicate that the combination of radiometric and geometric features outperforms feature sets of a single feature type only. Besides, we observe that relative height is the most crucial feature. The main finding is that the multi-modal feature vector integrates the complementary strengths of the underlying modalities. Whereas the mesh provides outstanding textural information, the dense PCs are superior in geometry. The multi-modal feature descriptor achieves the best performance on both data sets. It significantly outperforms feature sets that incorporate only features derived from the mesh by +7.37 pp and +2.38 pp for mF1 and Overall Accuracy (OA) on V3D. The registered improvement is +9.23 pp and +4.33 pp for mF1 and OA on H3D.
BUILDING SEGMENTATION BASED ON STEREO INFORMATION FROM SATELLITE IMAGES
Authors: Roux M. , Dumas L.
Published: May 2022

Abstract. Building semantic segmentation is key to many applications relying on 3D modeling of city buildings such as urban planning or business intelligence. Recent works have shown great improvements in this area thanks to artificial intelligence, but even state of the art neural networks encounter difficulties to generalize to buildings that are different from the training dataset. 3D modeling applications also requires the elevation information often retrieved from a pair of High Resolution satellite images. In this article, we show that using both images of a stereo pair as inputs to a neural network trained for building semantic segmentation achieves better results than using a single view. Especially, stereo training gives a greater ability to generalize. We show that using neural networks designed for disparity estimation performs well for building semantic segmentation from a pair of satellite views in epipolar geometry. We also discuss how radiometry and disparity both affect the definition of what a building is depending on the multi-view network architecture.
ROTATION ONLY BUNDLE-ADJUSTMENT FOR THE GENERALIZED CAMERA MODEL AND ITS APPLICATION FOR LARGE-SCALE UNDERWATER IMAGE-SETS
Authors: Elnashef B. , Filin S.
Published: May 2022

Abstract. The generalized camera model allows handling a broad range of imaging systems for which the common central perspective model no longer applies. While offering greater modeling flexibility, designated orientation procedures need to support its application. In that respect, models that facilitate the computation of the bundle-adjustment solution are imperative as a means to solve the motion and structure of a large set of images. Being non-linear, the bundle adjustment solution requires good initial estimates and the filtering of outlying matches. To facilitate such a solution, we propose in this paper a method for estimating the global rotations of a set of views independent of their translation and the scene structure. This method is then used to improve the robustness, efficiency, and convergence of the solution when used as a refinement to the global rotations. The proposed pipeline is evaluated by experiments on synthetic and real-world data of axial flat-refractive cameras. It shows that the proposed method produces more accurate and optimal initial estimates of the global rotations than the state-of-the-art rotation averaging method.
AN EFFICIENT SOLUTION TO RAY TRACING PROBLEMS FOR HEMISPHERICAL REFRACTIVE INTERFACES
Authors: Rofallski R. , Menna F. , Nocerino E. , Luhmann T.
Published: May 2022

Abstract. Refraction effects, their description and modeling are important aspects of underwater and multimedia photogrammetry. For hemispherical interfaces, the usual approach to refraction is to rely on standard pinhole representations, e.g. by employing the Brown model. This is strictly only possible if entrance pupil of the lens and dome center coincide which is not trivial to achieve. However, simulations and other authors show that systematic residual errors occur with these approaches up to considerable margins if offsets of some millimeters are present. Hence, we propose a novel efficient, yet strict optimization algorithm to account for offsets between dome port centers and entrance pupil. It is about two orders of magnitude faster than standard ray tracing implementations that account for refraction while providing similar or equal results. The algorithm is employed for analysis on a simulation and two real data sets and performance of additionally estimating the dome center is investigated. Our method is capable of improving accuracy in one data set at a maximum of 30% but even so cannot provide improvements for the second data sets. An explicit calibration model is hence to be chosen carefully and most likely relies on the offset’s margins and each individual application.
EXTRACTION OF ORTHOGONAL BUILDING BOUNDARY FROM AIRBORNE LIDAR DATA BASED ON FEATURE DIMENSION REDUCTION
Authors: Chen Y. , Yao W.
Published: May 2022

Abstract. Building boundary extraction is an active research topic in the field of feature extraction from airborne LiDAR point cloud data. Owing to the high complexity of most building extraction algorithms based on point clouds, multiple feature parameters must often be combined with iterative operations, particularly in the process of mitigating the sawtooth phenomenon using the sleeve algorithm and its improved versions. To improve the degree of automation and ensure accuracy, this study proposes a fast corner point detection method based on a dimensionality reduction technique, which utilizes reduced data mapping from 3D to 2D. We converted the boundaries extracted by the alpha shape algorithm to a 2D image and applied recursive Gaussian filtering with a relatively high level of automation to smoothen the image edges and mitigate the sawtooth phenomenon, thereby improving upon the sleeve algorithm, which requires a large number of iterations. Subsequently, the Douglas Peucker algorithm is used to retrieve the contour key points after extracting the contour lines and obtaining the regularized building contours using the grouped orthogonal regularization method. To verify the accuracy of the algorithm, it was compared with a cluster and adjustment (CAA)method based on the sleeve algorithm using three major evaluation metrics with respect to four representative building instances in two experimental datasets of urban areas. The value of the RMSE was reduced by an average of 43.79%. In addition, the time complexity decreased from O(n2) to O(n). These results demonstrate that the proposed method improves not only the accuracy of boundary extraction, but also the efficiency of data processing.
MAPPING STREET OBSTRUCTIONS IN AN URBAN STREET ENVIRONMENT USING MLS DATA
Authors: Liu C. , Qi Y.
Published: May 2022

Abstract. It is crucial to ensure no obstruction above urban streets for drivers. If street obstructions limit the sight of drivers, it may reduce the ability of drivers to perceive the surrounding road conditions. Street obstructions are usually caused by a variety of different factors. It includes street trees overgrowing into the space above the road or the wrong placement of the infrastructure. To ensure road traffic safety, transportation agencies are required to obtain the existence of street obstructions. To address the issue, this paper proposes a novel method to detect street obstructions. The method consists of two main steps: non-obstruction space construction by the Alpha-Smooth method and street obstruction analysis. The proposed approach is able to map the thematic map of street obstructions in the urban environment. The algorithm was tested on urban streets in Shanghai, China, and successfully obtained obstructions information. The results indicated that street trees are the main components of road obstructions. The proposed approach can make contribution to street maintenance and obstructions monitoring that aim to develop safer urban street environments.
IMAGE-BASED CONTROL POINT DETECTION AND CORRESPONDENCE-FREE GEOREFERENCING
Authors: Benz C. , Rodehorst V.
Published: May 2022

Abstract. In order to appropriately measure properties in 3D models, accurate georeferencing plays a vital role in structural health monitoring. For that purpose, control points are attached to the surface of the structure and measured geodetically. These points can be recovered in the virtual model and associated with the geodetic measurements. Automating the process of detecting and associating control points and geodetic measurements, facilitates the accurate georeferencing of large 3D models. While the number of marker types for control points is steadily increasing, this work claims that – under a plausible assumption – comparatively simple and commonly used marker designs can serve for accurate and robust georeferencing. By assuming that control points are asymmetrically distributed over the surface of the structure, the correspondence of points is determined by their geometric interrelation. In this work, an image-based detector for relatively simple control point types is proposed, applying transfer learning on hierarchical multi-scale attention (HMA) (Tao et al., 2020). For associating detected and geodetically measured points, a RANSAC-based procedure is presented that determines a geometrically consistent transformation between detected and measured points.
UTILIZING SINGLE PHOTON LASER SCANNING DATA FOR ESTIMATING INDIVIDUAL TREE ATTRIBUTES
Authors: Simula J. , Holopainen M. , Imangholiloo M.
Published: May 2022

Abstract. Mapping and monitoring forest resources require collection of spatially explicit and timely remote sensing (RS) data. Although field measurements are still important, the RS-based forest inventory helps mapping large areas to be cheaper, faster, less labor intensive, and spatially more explicit. The single-photon laser (SPL) scanning data has been exploited for different forestry applications but lacks deep examination in mapping individual trees as well as being compared with ordinary laser scanning (Linear-mode, LML) data and different individual tree detection (ITD) methods. Hence, this research focuses on applying and comparing two datasets (SPL and LML) for extracting attributes of individual trees by applying two tree crown segmentation methods (local maxima and watershed segmentation) on both datasets. The results were validated over 49 field measured plots of different species, located in southern boreal forest.The SPL yielded more accurate results for both tree density and height estimation. Watershed segmentation method yielded more accurate results for tree density and height estimation in both LML and SPL datasets. Tree density was underestimated by 4.7% (rRMSE: 32.3%) for all species. Comparing tree density estimation of different species, it was most accurate in deciduous plots (rBias: −9.5, rRMSE: 17.0%). Tree height estimation with SPL explained the variations of field-measured height very well (R2 = 0.93), and was reliably accurate, underestimated by 3.4% (rRMSE: 7.0%). The mean tree height estimation was most accurate in pine plots (rBias: 4.9%, rRMSE: 1.1%). In this research, SPL represented reliable and usable point cloud data for estimating tree height and density.
IMAGE INTERPOLATION ON THE CPU AND GPU USING LINE RUN SEQUENCES
Authors: Frommholz D.
Published: May 2022

Abstract. This paper describes an efficient implementation of an image interpolation algorithm based on inverse distance weighting (IDW). The time-consuming search for support pixels bordering the voids to be filled is facilitated through gapless sweeps of different directions over the image. The scanlines needed for the sweeps are constructed from a path prototype per orientation whose regular substructures get reused and shifted to produce aligned duplicates covering the entire input bitmap. The line set is followed concurrently to detect existing samples around nodata patches and compute the distance to the pixels to be newly set. Since the algorithm relies on integer line rasterization only and does not need auxiliary data structures beyond the output image and weight aggregation bitmap for intensity normalization, it will run on multi-core central and graphics processing units (CPUs and GPUs). Also, occluded support pixels of non-convex void patches are ignored, and over- or undersampling close-by and distant valid neighbors is compensated. Runtime and accuracy compared to generated IDW ground truth get evaluated for the CPU and GPU implementation of the algorithm on single-channel and multispectral bitmaps of various filling degrees.
JOINT ESTIMATION OF DEPTH AND ITS UNCERTAINTY FROM STEREO IMAGES USING BAYESIAN DEEP LEARNING
Authors: Mehltretter M.
Published: May 2022

Abstract. The necessity to identify errors in the context of image-based 3D reconstruction has motivated the development of various methods for the estimation of uncertainty associated with depth estimates in recent years. Most of these methods exclusively estimate aleatoric uncertainty, which describes stochastic effects. On the other hand, epistemic uncertainty, which accounts for simplifications or incorrect assumptions with respect to the formulated model hypothesis, is often neglected. However, to accurately quantify the uncertainty inherent in a process, it is necessary to consider all potential sources of uncertainty and to model their stochastic behaviour appropriately. To approach this objective, a holistic method to jointly estimate disparity and uncertainty is presented in this work, taking into account both aleatoric and epistemic uncertainty. For this purpose, the proposed method is based on a Bayesian Neural Network, which is trained with variational inference using a probabilistic loss formulation. To evaluate the performance of the method proposed, extensive experiments are carried out on three datasets considering real-world indoor and outdoor scenes. The results of these experiments demonstrate that the proposed method is able to estimate the uncertainty accurately, while showing a similar and for some scenarios improved depth estimation capability compared to the dense stereo matching approach used as deterministic baseline. Moreover, the evaluation reveals the importance of considering both, aleatoric and epistemic uncertainty, in order to achieve an accurate estimation of the overall uncertainty related to a depth estimate.
ANALYTICAL FORWARD-PROJECTION AND SYSTEM CALIBRATION FOR IMAGING THROUGH REFRACTIVE WINDSHIELDS IN AUTOMOTIVE APPLICATIONS
Authors: Elnashef B. , Filin S.
Published: May 2022

Abstract. We study in this paper models to mitigate effect of a windshield-related refraction on imaging systems. The literature shows that the distortions introduced by this curved surface are non-linear in their effect and reduce the performance of image-based analyses and depth estiamtion algorithms. We show that using geometric optics and local approximation of the windshield’s surface to a spherical one, a direct analytical refractive forward projection (RFP) form of a 3-space point onto the image plane can be derived. However, an exact form requires solving a 22-degree polynomial which may become numerically unstable. To stabilize the solution, we demonstrate how the introduction of valid local assumptions on the interface allows reducing the polynomial degree down to 8 and 4. Utilizing these forms we then show that the RFP can be used to jointly estimate the camera pose and the windshield’s surface parameters through minimization of the reprojection error. The proposed models are tested on simulated data and validated on real-world observations. Results show stability and a sub-millimeter level of reconstruction accurecy, alluding to the validity and quality of our representations.
ADVANCES IN OPTICAL POLARIZATION REMOTE SENSING FOR MARINE OBSERVATION: A CASE STUDY IN NANCHANG RIVER PARK
Authors: Zhang F. , Zhang Z. , Yan L. , Ding J. , Jiang K. , Zhang Y. , Cui Z.
Published: May 2022

Abstract. Marine observation is a worldwide challenge, which implicates for a large number of social, economic and scientific problems. Satellite remote sensing provides incredible convenience for marine observation, and remote sensing techniques with different wavelength range have been developed for scientific use related to oceanography, among of which optical polarization remote sensing is a rapidly growing field in the recent decade. Although some attempts have been made about utilizing optical polarization technique for marine observation, the potential of optical polarization remote sensing is far from being fully released and the current skills of optical polarization image processing are too coarse to extract deep information from raw images. In our experiment at Nanchang river park, three application scenarios are selected to illustrate advances in optical polarization remote sensing for marine observation, specifically including sun-glint observation, phytoplankton monitoring and coastal topography mapping. A baseline for optical polarization image processing is established for marine observation and the advantages of optical polarization technique are assessed qualitatively and quantitatively, proving that: For marine observation, optical polarization remote sensing can reduce overexposure rate, enhance dynamic range, depict subsurface phytoplankton and map coastal topography.
PSDEFOPAT – TOWARDS AUTOMATIC MODEL BASED PSI POST-PROCESSING
Authors: Evers M. , Thiele A. , Hammer H. , Hinz S.
Published: May 2022

Abstract. The deformation of the surface of the earth is triggered by numerous naturally occurring and artificial processes such as global isostatic adjustment, aseismic and coseismic movement, varying amounts of groundwater or gas stored underground, and soil consolidation due to urbanization. Monitoring these surface deformations is essential to understand the underlying processes and provide authorities and the public with hazard assessments. Remote sensing techniques, such as Persistent Scatterer Interferometry (PSI), have the capability of mapping these deformations. Besides a spatial analysis of the deformation patterns, PSI also provides information on their temporal evolution. Post-processing strategies to analyze the displacement time series have gained interest in recent years. This paper presents our PSI post-processing strategy, which incorporates different deformation models and automatically chooses the best-fitting one based on statistical tests.
PERFORMANCE OF NUMERICAL WEATHER PRODUCTS FOR INSAR TROPOSPHERIC CORRECTION: A CASE STUDY OF A TROPICAL REGION
Authors: Kirui P. , Riedel B. , Gerke M.
Published: May 2022

Abstract. Tropospheric delay variability remains a significant source of error in the InSAR-derived measurements. Numerical weather models have been proposed as an alternative technique to mitigate tropospheric delays in InSAR and have become a standard procedure for some multi-temporal InSAR processing workflows. This study evaluates the viability of three numerical weather models for mitigating tropospheric delay in InSAR for a tropical region. We assess their performance in correcting tropospheric delay in Sentinel-1 interferograms at different spatial wavelengths using variograms. Their performance is validated using GNSS tropospheric delay and our proposed SAR-derived tropospheric delay estimates. The results indicate that numerical weather model estimates do not mitigate short-wavelength turbulent delays, but can mitigate long-wavelength stratified delays to some extent, which may also introduce additional errors in interferograms. At a spatial wavelength of 40 km, 36% of the interferograms showed increased spatial autocorrelation after correction with GACOS, 55% with ERA-5, and 51% with MERRA-2. In contrast the InSAR-derived tropospheric delays resulted in a significant reduction in variance at all wavelengths indicating the ability to mitigate both turbulent and stratified delays. Our study demonstrates the limited potential of numerical weather model estimates to satisfactorily mitigate trophospheric noise in InSAR and the capability of InSAR-derived trophospheric delay to significantly correct tropospheric noise in InSAR.