bluuebunny/crossref_metadata_2025 huggingface dataset:
PARA ALÉM DA ARITMÉTICA: POR UMA ALGEBRIZAÇÃO DO CURRÍCULO DOS PRIMEIROS ANOS
Authors: Ciríaco Klinger Teodoro
Published: August 2020

O texto destaca elementos de um ensaio teórico, objeto de uma prova de Concurso Público para Ingresso na Carreira do Magistério do Ensino Superior, em que busco refletir acerca da inclusão do pensamento algébrico no Ensino Fundamental, em particular no currículo dos primeiros anos. Recorro ao debate contemporâneo em que o Brasil vivencia na área da Educação Matemática, desde 2017, frente a necessidade de se abordar aspectos da “Álgebra” no trabalho efetivo com crianças menores de 11 anos de idade. Face à problematização decorrente da temática em questão, objetivo, com este paper, contribuir com discussões teórico-metodológicas e conceituais ao apresentar, na leitura interpretativa que faço, a definição deste tipo de pensamento e suas correlações no campo de atuação dos pedagogos.
A ENTRADA EM ATIVIDADE NO ENSINO DE FÍSICA PARA ESTUDANTES COM DEFICIÊNCIA VISUAL POR MEIO DA PRÁTICA ESPORTIVA DO GOALBALL
Authors: Nascimento Willdson Robson Silva do , Camargo Eder Pires de , Correia Eanes Dos Santos
Published: August 2020

A pesquisa aqui relatada objetivou desenvolver um ensino de física para estudantes com deficiência visual de forma a promover a entrada   dos mesmos em atividade, visando motivá-los à uma mobilização de conhecimentos.  Nesse sentido, apresentamos o Goalball, esporte criado para cegos, como objeto social no processo de mobilização da aprendizagem de dois estudantes de uma escola estadual de Aracaju/SE sobre os conceitos de gravidade, peso e massa.  Fundamentamos a investigação nos   referenciais: "Relação com o Saber" de Bernard Charlot e na Teoria da Atividade de Leontiev. Trata-se de uma pesquisa qualitativa do tipo participativa, tendo como instrumentos de constituição de dados a Entrevista semiestruturada, as Observações das aulas, o Diário de Campo e um Aplicativo de mensagem instantânea. Os resultados demonstram a importância de ações planejadas para a promoção da entrada em atividade, levando em consideração o objeto e o motivo da mesma. Adicionalmente, mostrou-se relevante   o reconhecimento dos estudantes como sujeitos sociais, que se constroem por uma identidade social e uma identidade singular, centradas no “eu” e no “outro’, pois, é no reconhecimento do outro, ou seja, do “não eu” que o sujeito imerge no mundo das relações e versões da realidade simbólica identidade/diferença.
Aceleradores e detectores de partículas no ensino médio: uma sequência de ensino-aprendizagem
Authors: Dos Reis Silva Yasmin Alves , Montanha Larruana , Siqueira Maxwell Roger Da Purificação
Published: August 2020

O presente artigo apresenta uma proposta de sequência de ensino e aprendizagem sobre Aceleradores e Detectores de Partículas (ADP), pautado na discussão sobre a inserção de tópicos de Física Moderna e Contemporânea (FMC) na educação básica. A proposta utilizou o referencial teórico-metodológico da Teaching Learning Sequences (TLS) fundamentada na Design Based Research (DBR), que visa a construção da sequência a partir de um processo cíclico que desenvolve, aplica e reconstrói sequências baseadas em tópicos específicos. Após construção, a sequência foi implementada em uma escola pública federal, em turmas da 3a série do Ensino Médio. As informações coletadas foram analisadas tendo em vista as fases da estrutura didática (protoprincípio de design) propostas no referencial de construção desse tipo de sequência, permitindo avaliá-la e redesenhá-la para uma nova implementação. Nesta análise foi possível encontrar lacunas de aprendizagem e a necessidade de sistematização do conhecimento, porém permitiu encontrar indícios de apropriação de conhecimentos científicos pelos estudantes, que é um indicativo de potencialidade da sequência.
Construção de uma maquete experimental automatizada para a determinação da constante de Planck com o auxílio da plataforma Arduíno
Authors: Oliveira Ivanor Nunes , Ramos Jorge Anderson P. , Silva Wilton Lacerda , Chaves Valteni Douglas , De Melo Clênia Andrade Oliveira
Published: August 2020

Ao modelar um corpo radiante como um conjunto de osciladores harmônicos, o físico Max Planck, em 1900, fez a hipótese de que os osciladores das substâncias podem ter apenas um conjunto discreto de energia e, no processo de interação com o campo de radiação, pode mudar sua energia em porções iguais a . A constante de Plancké uma das constantes fundamentais da física, juntamente com outras como, por exemplo, a carga e a massa do elétron, a velocidade da luz e a constante de Boltzmann. Como mostra o subsequente desenvolvimento da física quântica, todos os momentos mecânicos dos átomos, moléculas, elétrons e núcleos são expressos em unidades de . Além disso, a constante de Planck entra numa série de relações que desempenham um papel fundamental na física quântica e que determinam os estados discretos das micropartículas, a dualidade onda-partícula de suas propriedades. No presente artigo, propõe-se determinar a constante de Planck através do estudo das curvas características de LASER´s de semicondutor, obtidas com o auxílio de uma maquete experimental automatizada que contém uma placa Arduino ligada a um circuito eletrônico com tais dispositivos eletrônicos. A maquete experimental é constituída pelas seguintes partes: banco óptico com suportes; régua com zero central, onde se projetam o máximo principal e máximos secundários do espectro da luz emitida pelo LASER, obtidos com o auxílio de uma rede de difração unidimensional com período conhecido; apontador LASER de semicondutor; placa Arduino Uno com circuito eletrônico que se comunica com o computador via USB; computador. A plataforma Arduino para equipamentos técnicos é recomendada para o processo educacional. Tendo em conta a facilidade de programação e a sua capacidade de estudar processos físicos em tempo real, propõe-se utilizar placas Arduino na modernização de laboratórios didáticos de Física Geral automatizados manipulados remotamente.
Relação Museu Escola: Influências da Escola nas Abordagens Museais
Authors: Santos Thiago Da Silva , Germano Marcelo Gomes
Published: August 2020

Os Museus de Ciências são considerados espaços educativos com particularidades que aproximam suas perspectivas das políticas públicas de difusão e popularização das ciências. Mas, em países como o Brasil onde a cultura de visitação a Museus de Ciências não é muito forte, constata-se uma grande concentração de visitantes pertencentes ao grupo dos professores e estudantes da educação básica, dificultando a aproximação dos museus com o público em geral. Este fato tem atraído a atenção de alguns pesquisadores e favorecido a consolidação de estudos e pesquisas com interesse na relação Museu-Escola. Neste artigo apresentamos os resultados de uma pesquisa que objetivou investigar a relação Museu-Escola considerada a perspectiva da influencia que o público escolar pode exercer nas atividades museais. Desenvolvida no contexto do Museu Vivo da Ciência e do Espaço Energia em Campina Grande/PB a pesquisa de natureza qualitativa partiu da hipótese de um possível processo de escolarização dos museus e, a partir de entrevistas semiestruturadas orientadas aos coordenadores e monitores dos referidos espaços, procurou identificar traços característicos dessa influência e, ao mesmo tempo, discutir as implicações que esse processo pode causar naqueles espaços educativos. Os resultados apontaram que a presença de um público majoritariamente escolar introduz modificações nos discursos dos monitores e coordenadores, de modo que os Museus de Ciências, teoricamente orientados para atrair e dialogar com o público em geral, terminam por adequar o seu discurso, as suas práticas e a estrutura de funcionamento, aos sistemas formais de educação.
Oxygen budget for the north-western Mediterranean deep
convection region
Authors: Ulses Caroline , Estournel Claude , Fourrier Marine , Coppola Laurent , Kessouri Fayçal , Lefèvre Dominique , Marsaleix Patrick
Published: August 2020

Abstract. The north-western Mediterranean deep convection plays a crucial role in the general circulation and biogeochemical cycles of the Mediterranean Sea. The DEWEX (DEnse Water EXperiment) project aimed to better understand this role through an intensive observation platform combined with a modelling framework. We developed a 3 dimensional coupled physical and biogeochemical model to estimate the cycling and budget of dissolved oxygen in the entire north-western Mediterranean deep convection area over the period September 2012 to September 2013. After showing that the simulated dissolved oxygen concentrations are in a good agreement with the in situ data collected from research cruises and Argo floats, we analyze the seasonal cycle of the air-sea oxygen exchanges, as well as physical and biological oxygen fluxes, and we estimate an annual oxygen budget. Our study indicates that the annual air-to-sea fluxes in the deep convection area amounted to 20 mol m−2 yr−1. 88 % of the annual uptake of atmospheric oxygen, i.e. 18 mol m−2, occurred during the intense vertical mixing period. The model shows that an amount of 27 mol m−2 of oxygen, injected at the sea surface and produced through photosynthesis, was transferred under the euphotic layer, mainly during deep convection. An amount of 20 mol m−2 of oxygen was then gradually exported in the aphotic layers to the south and west of the western basin, notably, through the spreading of dense waters recently formed. The decline in the deep convection intensity in this region predicted by the end of the century in recent projections, may have important consequences on the overall uptake of atmospheric oxygen in the Mediterranean Sea and on the oxygen exchanges with the Atlantic Ocean, that appear necessary to better quantify in the context of the expansion of low-oxygen zones.
GOCO06s – A satellite-only global gravity field model
Authors: Kvas Andreas , Brockmann Jan Martin , Krauss Sandro , Schubert Till , Gruber Thomas , Meyer Ulrich , Mayer-Gürr Torsten , Schuh Wolf-Dieter , Jäggi Adrian , Pail Roland
Published: August 2020

Abstract. GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistent high-accuracy and best possible spatial resolution of the Earth's gravity field. The motivation for the new release was in the availability of reprocessed observation data for GRACE and GOCE, updated background models, and substantial improvements in the processing chains of the individual contributions. Due to the long observation period, the model consists not only of a static gravity field, but comprises additionally modeled temporal variations. These are represented by time variable spherical harmonic coefficients, using a deterministic model for a regularized trend and annual oscillation. The main focus within the GOCO combination process is on the proper handling of the stochastic behavior of the input data. Appropriate noise modelling for the used observations result in realistic accuracy information for the derived gravity field solution. This accuracy information, represented by the full variance-covariance matrix, is extremely useful for further combination with, for example, terrestrial gravity data and is published together with the solution. The primary model data (Kvas et al., 2019b) consisting of potential coefficients representing Earth's static gravity field, together with secular and annual variations are available on International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/, last accessed 2020-06-11). This data set is identified with the DOI https://doi.org/10.5880/ICGEM.2019.002. Supplementary material consisting of the full variance-covariance matrix of the static potential coefficients and estimated co-seismic mass changes are available on https://ifg.tugraz.at/GOCO (last accessed 2020-06-11).
RECONSTRUCTION OF THE SLIP DISTRIBUTION ALONG THE WEST HELANSHAN FAULT, NORTHERN CHINA BASED ON HIGH-RESOLUTION TOPOGRAPHY
Authors: Bi H. , Zheng W. , Lei Q. , Zeng J.
Published: August 2020

Abstract. The increasing wealthy of high-resolution topography allows for remotely measuring and analysing offset features and their associated surface slip distributions at a very high resolution and along a significant length of a fault, hence providing important insights into many aspects of the fault behaviour. The West Helanshan Fault is a Holocene active fault located at the junction of the Tibetan Plateau, Alashan, and Ordos blocks. Despite its special tectonic location, it has rarely been studied before. In this study, a 2-m-resolution DEM of the West Helanshan Fault was built from the high-resolution (0.5 m) WorldView-3 stereo satellite imagery based on the photogrammetry method, and a total of 181 strike-slip offsets and 201 vertical displacements were acquired along different segments of the fault. By statistical analysis of the offset observations, we conclude that at least six large paleoearthquakes have ruptured the fault, producing a minimum rupture length of ∼50 km, and the paleoearthquakes have followed a characteristic slip pattern with a coseismic strike slip of ∼3 m and a vertical slip of ∼1 m, corresponding to a geologic moment magnitude of 7.1–7.5.
TOWARDS THE MODELING OF GLACIER MICROTOPOGRAPHY USING HIGH-RESOLUTION DATA FROM UNMANNED AERIAL SURVEY
Authors: Florinsky I. V. , Skrypitsyna T. N. , Bliakharskii D. P. , Ishalina O. T. , Kiseleva A. S.
Published: August 2020

Abstract. Glaciated areas are important targets for interdisciplinary research. In the last quarter of the 20th century, there has been a significant shift in glacier observation approaches from direct fieldwork to remote sensing. Over the past 15 years, unmanned aerial systems have been increasingly used for this purpose. In this article, we briefly describe a newly launched Russian–Chinese project aimed at developing a theory and methodology for digital modeling and analysis of the glacier microtopography using very high resolution data from unmanned aerial surveys. We argue the relevance of the study and review key publications on the application of digital terrain modeling and geomorphometry in glaciology. Next, we discuss the aim of the project and tasks performed by the Russian side, as well as materials and methods used in the study. As initial data, we use multi-temporal, digital aerial images of very high resolution (5 cm) collected by the unmanned aerial survey of the ice sheet and glaciers near the Larsemann Hills, East Antarctic. Finally, we present some examples for geomorphometric analysis of glacier microtopography including snow/ice features of eolian origin.
MINDFLOW BASED DENSE MATCHING BETWEEN TIR AND RGB IMAGES
Authors: Zhu J. , Ye Z. , Xu Y. , Hoegner L. , Stilla U.
Published: August 2020

Abstract. Image registration is a fundamental issue in photogrammetry and remote sensing, which targets to find the alignment between different images. Recently, registration of images from difference sensors become the hot topic. The registered images from different sensors are able to offer additional information, which help with different tasks like segmentation, classification, and even emergency analysis. In this paper, we proposed a registration strategy to calculate the dominant orientation difference and then achieve the dense alignment of Thermal Infrared (TIR) image and RGB image with MINDflow. Firstly, the orientation difference of TIR images and RGB images is calculated by finding the dominant image orientations based on phase congruency. Then, the modality independent neighborhood descriptor (MIND) together with global optical flow algorithm are adopted as MINDflow for dense matching. Our method is tested in the image sets containing TIR images and RGB images captured separately but in the same construction site areas. The results show that it is able to achieve the optimal results with features of significance even for dramatically radiometric differences between TIR images and RGB images. By comparing the results with other descriptor, our method is more robust and keep the features of objects in the images.
IMPACT OF FLIGHT ALTITUDE ON UNMANNED AERIAL PHOTOGRAMMETRIC SURVEY OF THE SNOW HEIGHT ON MOUNT LEBANON
Authors: Abou Chakra C. , Somma J. , Gascoin S. , Fanise P. , Drapeau L.
Published: August 2020

Abstract. In Lebanon, the seasonal snowpack is poorly monitored despite its importance for water resource supply. The snow accumulates on Mount Lebanon in karstic depressions named “sinkholes.” It is important to monitor the evolution of the snow height inside those “sinkholes”, because of their key role as “containers” for seasonal snow. UAV photogrammetry is a major technological breakthrough which allows an accurate monitoring of the snow height. Because the impact of flight parameters on snow height retrievals is not well documented yet, this research aims to evaluate the impact of UAV flight altitude on the resolution and accuracy of the resulting orthomosaic and DSM. The flight missions were done using the Phantom DJI which generated five DSMs. These are validated using total station measurements.The results indicate that the snow DSMs can be retrieved by adopting a resolution of 8 to 84 cm, a point density between 1.43 and 153 points/sqm and a RMSE of 13 to 41 cm. The testing was done using an elevation varying between 50 and 500 m. The results will be compared to total station observations. These results allow the user to choose the suitable flight altitude for required resolution and points density. We suggest that a flight altitude of 100 m is sufficient for the survey of the snow cover elevation.
EXPERIMENTAL ASSESSMENT OF 3D NARROW SPACE MAPPING TECHNOLOGIES
Authors: Piniotis G. , Soile S. , Bourexis F. , Tsakiri M. , Ioannidis C.
Published: August 2020

Abstract. Narrow space surveying and mapping is of high importance for many applications, with the demand for digital 3D models in a low-cost and relatively fast way growing rapidly. This paper examines SLAM-based and image-based mapping systems for indoor mapping and focuses on the comparison of the 3D point clouds acquired from two commercial mapping systems, Leica BLK2GO and Matterport Pro2 3D (MC250). Issues that are addressed include the effect of the mapping trajectory on the produced mapping result, and the amount of mapping propagation error in narrow space surveying. The two systems are experimentally compared against both an ideal geometric target facility and in an example environment of a narrow corridor. Finally, results are given with derived metrics that can be possibly extended to several real applications.
ICE DETECTION ON AIRPLANE WINGS USING A PHOTOGRAMMETRIC POINT CLOUD: A SIMULATION
Authors: Aicardi I. , Lingua A. , Mazzara L. , Musci M. A. , Rizzo G.
Published: August 2020

Abstract. This study describes some tests carried out, within the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of ice), for the geometrical ice detection on airplane wings. The purpose of these analysis is to estimate thickness and shape of the ice that an RGB sensor is able to detect on large aircrafts as Boeing 737-800. However, field testing are not available yet, therefore, in order to simulate the final configuration, a steel panel has been used to reproduce the aircraft surface. The adopted methodology consists in defining a reference surface and modelling its 3D shape with and without ice through photogrammetric acquisitions collected by a DJI Mavic Air drone hosting a RGB camera and processed by Agisoft Metashape software. The comparison among models with and without the ice has been presented and results show that it is possible to identify the ice, even though some noise still remains due to the geometric reconstruction itself. Finally, using 3dReshaper and Matlab software, the authors develop various analysis defining the operative limits, the processing time, the correct setting up of Metashape for a more accurate ice detection, the optimization of the methodology in terms of processing time, precision and completeness. The procedure can certainly be more reliable considering the usage of the hyperspectral sensor technique as future implementation.
DTM CORRECTION IN AREAS OF STEEP SLOPES
Authors: Häufel G. , Böge M. , Bulatov D.
Published: August 2020

Abstract. Computation of a DTM from a DSM is a well-known and very important task. We derive the DTM by a procedure consisting of ground points extraction, surface interpolation and triangulation by a canonical mesh if the terrain is flat or has only moderate changes in elevation. In regions with steep slopes, such as at riversides, and with man-made 3D structures, such as around bridges, interpolation artifacts and suppression of high-resolution details can lead to coarse errors in local elevations even for the building detection task. The eligible regions must be therefore detected and at least locally reprocessed. For detection, we search for connected components of a certain minimum size with negative relative elevations. For reconstruction, we suppress the points with erroneously reconstructed DSM values and interpolate the surface by means of L1 splines. Finally, these meshes must be fused into one single DTM mesh. We applied land cover classification to demonstrate the usability of our correction. The overall accuracy amounts to around 88% while the number of faulty assignments due to incorrect DTMs can be significantly reduced.
SIMULATION-BASED DATA AUGMENTATION USING PHYSICAL PRIORS FOR NOISE FILTERING DEEP NEURAL NETWORK
Authors: Jameela M. , Chen L. , Sit A. , Yoo J. , Verheggen C. , Sohn G.
Published: August 2020

Abstract. LiDAR (Light Detection and Ranging) mounted with static and mobile vehicles has been rapidly adopted as a primary sensor for mapping natural and built environments for a range of civil and military applications. Recently, technology advancement in electro-optical engineering enables acquiring laser returns at high pulse repetition frequency (PRF) from 100Hz to 2MHz for airborne LiDAR, which leads to an increase in the density of 3D point cloud significantly. Traditional systems with lower PRF had a single pulse-in-air zone (PIA) big enough to avoid a mismatch between pulse pair at the receiver. Modern multiple pulses-in-air (MPIA) technology ensures multiple windows of operational ranges for single flight line and no blind-zones; downside of the technology is projection of atmospheric returns closer to same PIA zone of neighbouring ground points and more likely to be overlapping with objects of interest. These characteristics of noise compromise the quality of the scene and encourage usage of noise filtering neural network as existing filters are not effective. A noise filtering deep neural network requires a considerable volume of the diverse annotated dataset, which is expensive. We developed simulation for data augmentation based on physical priors and Gaussian generative function. Our study compares deep learning networks for noise filtering and shows performance gain on 3D U-Net. Then, we evaluate 3D U-Net for simulation-based data augmentation, which shows an increase in precision and F1-score. We also provide an analysis of the underline spatial distribution of points and their impact on data augmentation, and noise filtering.
CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES
Authors: Schachtschneider J. , Brenner C.
Published: August 2020

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.
INTEGRATION OF AERIAL, MMS, AND BACKPACK IMAGES FOR SEAMLESS 3D MAPPING IN URBAN AREAS
Authors: Li Z. , Wu B. , Li Y.
Published: August 2020

Abstract. Photorealistic three-dimensional (3D) models play an indispensable role in the spatial data infrastructure (SDI) of a smart city. Recent developments in aerial oblique photogrammetry, and the popularity of terrestrial mobile mapping systems (MMSs) offer possibilities for deriving 3D models with centimeter-level accuracy in urban areas. Additionally, advances in image matching and bundle adjustment have allowed 3D models derived from the integration of aerial and ground imagery to overcome typical problems related to 3D mapping in urban areas (e.g., geometric defects, blurred textures on building façades). Nevertheless, this approach may not be suitable for all scenarios owing to innate differences between each platform. Besides, MMS images may not cover regions that cannot be reached by mobile vehicles in urban areas (e.g., narrow alleys, areas far from roads). Meanwhile, backpack systems have garnered attention from the photogrammetry community in recent years due to their flexibility, and regions neglected in previous works can be adequately reconstructed from images collected by backpack systems. This paper presents an approach for effectively integrating multi-source images collected by aerial, MMS, and backpack platforms for seamless 3D mapping in urban areas. The approach includes three main steps: (1) data pre-processing, (2) combined structure-from-motion, and (3) optimal generation of a textured 3D mesh model. The experimental results using aerial, MMS, and backpack datasets collected in a typical urban area in Hong Kong demonstrate the promising performance of the proposed approach. The described work is significant for boosting various types of imagery for integrated 3D mapping in both city scale and street level to facilitate various applications.
A FRAMEWORK TO EXTRACT STRUCTURAL ELEMENTS OF CONSTRUCTION SITE FROM LASER SCANNING
Authors: Truong-Hong L. , Lindenbergh R. C.
Published: August 2020

Abstract. This paper proposes a framework to automatic extract structural elements of reinforced concrete buildings from laser scanning data, which can be used in dimensional quality control and surface defect identification. The framework deploys both spatial information of a point cloud and contextual knowledge of building structures to extract the structural elements in a sequential order: floors and ceilings, walls, columns and beams. The method starts to extract a subset data containing candidate points of the structural elements and segmentation methods and filtered based contextual knowledge subsequently apply to obtain the final points of the elements. In this framework, a combination between kernel density estimation and a cell-patch-based region growing are to extract the floors, ceilings and walls, while the points of the columns and beams are achieved through a voxel-based region growing. 23.5 million data points of one story of the building is used to test a performance of the proposed framework. Results showed all structural components are successfully extracted. Moreover, completeness, correctness, and quality indicated through point-based performance report larger than 96.0%, 96.9% and 93.0%, respectively while overlap rates of the floors, ceilings and walls are no less than 95.3%. Interestingly, an executing time of the proposed method is about 7.7seconds per a million point.
MOVING OBJECTS AWARE SENSOR MESH FUSION FOR INDOOR RECONSTRUCTION FROM A COUPLE OF 2D LIDAR SCANS
Authors: Wu T. , Vallet B. , Demonceaux C. , Liu J.
Published: August 2020

Abstract. Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system) dataset and compared with Poisson and Delaunay based reconstruction methods.
DENSE MATCHING COMPARISON BETWEEN CLASSICAL AND DEEP LEARNING BASED ALGORITHMS FOR REMOTE SENSING DATA
Authors: Xia Y. , d’Angelo P. , Tian J. , Reinartz P.
Published: August 2020

Abstract. Deep learning and convolutional neural networks (CNN) have obtained a great success in image processing, by means of its powerful feature extraction ability to learn specific tasks. Many deep learning based algorithms have been developed for dense image matching, which is a hot topic in the community of computer vision. These methods are tested for close-range or street-view stereo data, however, not well studied with remote sensing datasets, including aerial and satellite data. As more high-quality datasets are collected by recent airborne and spaceborne sensors, it is necessary to compare the performance of these algorithms to classical dense matching algorithms on remote sensing data. In this paper, Guided Aggregation Net (GA-Net), which belongs to the most competitive algorithms on KITTI 2015 benchmark (street-view dataset), is tested and compared with Semi-Global Matching (SGM) on satellite and airborne data. GA-Net is an end-to-end neural network, which starts from an stereo pair and directly outputs a disparity map indicating the scene’s depth information. It is based on a differentiable approximation of SGM embedded into a neural network, performing well for ill-posed regions, such as textureless areas, slanted surfaces, etc. The results demonstrate that GA-Net is capable of producing a smoother disparity map with less errors, particularly for across track data acquired at different dates.
RESEARCH AND APPLICATION OF HISTORICAL 3D MODELING BASED ON ARCHIVED NON-OBLIQUE AERIAL PHOTOS
Authors: Zhu X. , Pang G. , Chen C.
Published: August 2020

Abstract. Nowadays, the oblique and multi-view, large-overlap aerial photography and airborne LiDAR are the main sources to build the 3D scene model. However, most of our archived aerial photos are acquired by non-oblique, normal photography. Because of low resolution, low overlay and poor model texture, there were less relative research and application. With the development of pixel-level matching technology, especially the application of Semi-Global Matching (SGM) and Multi-View Stereo (MVS) algorithm, the normal (non-oblique, non-large overlap) aerial photos could also be explored to restore the dense Digital Surface Model (DSM) and 3D scene model. In this paper, the method of the 3D scene modelling with the non-oblique aerial photos are summarized into 4 steps consisting of Data preprocessing, Ground Control Points (GCPs) collection and aerial triangulation (AT), DSM extraction and editing, 3D modelling and visualization. For the archived non-oblique aerial photos, including the aerial photographic films, digital frame photos and push-broom aerial data, the key steps of the 3D modelling method with these non-oblique aerial photos are discussed. Based on the experiments, the method can effectively explore the archived normal aerial data for large range restoration, 3D restoration, time series change detection and etc., providing new valuable spatio-temporal data for the urban historical research.
A REAL-TIME PHOTOGRAMMETRIC SYSTEM FOR MONITORING HUMAN MOVEMENT DYNAMICS
Authors: Chen L. , Wu B. , Zhao Y.
Published: August 2020

Abstract. The human body posture is rich with dynamic information that can be captured by algorithms, and many applications rely on this type of data (e.g., action recognition, people re-identification, human-computer interaction, industrial robotics). The recent development of smart cameras and affordable red-green-blue-depth (RGB-D) sensors has enabled cost-efficient estimation and tracking of human body posture. However, the reliability of single sensors is often insufficient due to occlusion problems, field-of-view limitations, and the limited measurement distances of the RGB-depth sensors. Furthermore, a large-scale real-time response is often required in certain applications, such as physical rehabilitation, where human actions must be detected and monitored over time, or in industries where human motion is monitored to maintain predictable movement flow in a shared workspace. Large-scale markerless motion-capture systems have therefore received extensive research attention in recent years.In this paper, we propose a real-time photogrammetric system that incorporates multithreading and a graphic process unit (GPU)-accelerated solution for extracting 3D human body dynamics in real-time. The system includes a stereo camera with preliminary calibration, from which left-view and right-view frames are loaded. Then, a dense image-matching algorithm is married with GPU acceleration to generate a real-time disparity map, which is further extended to a 3D map array obtained by photogrammetric processing based on the camera orientation parameters. The 3D body features are acquired from 2D body skeletons extracted from regional multi-person pose estimation (RMPE) and the corresponding 3D coordinates of each joint in the 3D map array. These 3D body features are then extracted and visualised in real-time by multithreading, from which human movement dynamics (e.g., moving speed, knee pressure angle) are derived. The results reveal that the process rate (pose frame-rate) can be 20 fps (frames per second) or above in our experiments (using two NVIDIA 2080Ti and two 12-core CPUs) depending on the GPU exploited by the detector, and the monitoring distance can reach 15 m with a geometric accuracy better than 1% of the distance.This real-time photogrammetric system is an effective real-time solution to monitor 3D human body dynamics. It uses low-cost RGB stereo cameras controlled by consumer GPU-enabled computers, and no other specialised hardware is required. This system has great potential for applications such as motion tracking, 3D body information extraction and human dynamics monitoring.
DEEP SEMANTIC SEGMENTATION FOR THE OFF-ROAD AUTONOMOUS DRIVING
Authors: Sgibnev I. , Sorokin A. , Vishnyakov B. , Vizilter Y.
Published: August 2020

Abstract. This paper is devoted to the problem of image semantic segmentation for machine vision system of off-road autonomous robotic vehicle. Most modern convolutional neural networks require large computing resources that go beyond the capabilities of many robotic platforms. Therefore, the main drawback of such models is extremely high complexity of the convolutional neural network used, whereas tasks in real applications must be performed on devices with limited resources in real-time. This paper focuses on the practical application of modern lightweight architectures as applied to the task of semantic segmentation on mobile robotic systems. The article discusses backbones based on ResNet18, ResNet34, MobileNetV2, ShuffleNetV2, EfficientNet-B0 and decoders based on U-Net and DeepLabV3 as well as additional components that can increase the accuracy of segmentation and reduce the inference time. In this paper we propose a model using ResNet34 and DeepLabV3 decoding with Squeeze & Excitation blocks that was optimal in terms of inference time and accuracy. We also demonstrate our off-road dataset and simulated dataset for semantic segmentation. Furthermore, we present that using pre-trained weights on simulated dataset achieves to increase 2.7% mIoU on our off-road dataset compared pre-trained weights on the Cityscapes. Moreover, we achieve 75.6% mIoU on the Cityscapes validation set and 85.2% mIoU on our off-road validation set with a speed of 37 FPS for a 1,024×1,024 input on one NVIDIA GeForce RTX 2080 card using NVIDIA TensorRT.
REAL-TIME SLAM FOR THE OFF-ROAD AUTONOMOUS DRIVING
Authors: Vishnyakov B. , Sheverdin V.
Published: August 2020

Abstract. In this paper we propose a new SLAM algorithm that is robust to the changing environment of the countryside. The hardware part consists of two separate machine vision cameras, joined in stereo, and can be supplemented with LiDAR, IMU and GPS. We introduce a method that can be used to reliably calculate the position of a vehicle in natural environments. To estimate the pose and produce a three-dimensional reconstruction we use a stereo camera rig, inertial measurement unit and the global positioning system. While solving the problem of visual odometry in outdoor scenes we faced a number of difficulties, arising from high dynamic range, as well as the presence of a large number of "similar elements", such as leaves, grass, trees and etc. Under these conditions, it becomes difficult to match feature points in image sequences. HOG-based methods, such as SIFT, SURF and others often do not obtain good matching due to noise, lack of a sufficient number of gradients, and the presence of identical domains. Using neighborhood-based detectors such as DAISY often allows to identify the correct matches, but using them is worth it too expensive. These methods are very demanding on the computational resources and prone to drift. We needed a method that is less expensive, but at the same time provides sufficient accuracy in the trajectory estimation. Direct methods, such as optical flow calculating or direct image matching allow us to map point-to-point in these conditions with high reliability. They also have disadvantages that can be eliminated by using an IMU and modern algorithms. To improve the quality of the algorithms, we solve the reconstruction problem for several frames using the Levenberg-Marquardt optimization method for bundle adjustment. Each pass optimizes frames that are directly related to the last one, we use two threads that perform partial and full optimization of the entire trajectory using graphs to significantly increase the performance of the method.
CLASSIFICATION OF UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS OF RIVERINE SPECIES USING MACHINE LEARNING ALGORITHMS: A CASE STUDY IN THE PALANCIA RIVER, SPAIN
Authors: Carbonell-Rivera J. P. , Estornell J. , Ruiz L. A. , Torralba J. , Crespo-Peremarch P.
Published: August 2020

Abstract. The management of riverine areas is fundamental due to their great environmental importance. The fast changes that occur in these areas due to river mechanics and human pressure makes it necessary to obtain data with high temporal and spatial resolution. This study proposes a workflow to map riverine species using Unmanned Aerial Vehicle (UAV) imagery. Based on RGB point clouds, our work derived simple geometric and spectral metrics to classify an area of the public hydraulic domain of the river Palancia (Spain) in five different classes: Tamarix gallica L. (French tamarisk), Pinus halepensis Miller (Aleppo pine), Arundo donax L. (giant reed), other riverine species and ground. A total of six Machine Learning (ML) methods were evaluated: Decision Trees, Extra Trees, Multilayer Perceptron, K-Nearest Neighbors, Random Forest and Ridge. The method chosen to carry out the classification was Random Forest, which obtained a mean score cross-validation close to 0.8. Subsequently, an object-based reclassification was done to improve this result, obtaining an overall accuracy of 83.6%, and individually a producer’s accuracy of 73.8% for giant reed, 87.7% for Aleppo pine, 82.8% for French tamarisk, 93.5% for ground and 80.1% for other riverine species. Results were promising, proving the feasibility of using this cost-effective method for periodic monitoring of riverine species. In addition, the proposed workflow is easily transferable to other tasks beyond riverine species classification (e.g., green areas detection, land cover classification) opening new opportunities in the use of UAVs equipped with consumer cameras for environmental applications.
ARCHIVE AND WARTIME AERIAL PHOTOGRAPHS AND PROCEDURES OF THEIR TREATMENT
Authors: Šafář V. , Staňková H. , Pospíšil J. , Kaňa D.
Published: August 2020

Abstract. The article presents with the use of archive aerial photographs. The first task was to search and identify drainage detail from archive aerial photographs. The second task is to create procedures for processing aerial reconnaissance images (from WWII) to identify sites with potential pyrotechnic load. Both of these tasks are connecting by the effort to determine the internal orientation parameters of the cameras for using and easier calculation of exterior parameters by image correlation. Complete automation process searching of fiducial mark (FM) identification was implemented. The coordinates of all FM are calculated automatically from archive aerial photographs. In addition, the edges of the photographs are automatically found and a program was created to minimize of the cropping of the archive aerial photographs. The next part of the paper describes the procedures of averaging the values of the relative position of FM and transforming archive aerial photographs to a uniform dimension from a set of images taken with the same camera. The second part of the paper describes the process of creating a historical ortophoto with the standard calculation of bundle adjustment performed by an external process in the background of the OrthoEngine module using the Celery library installed as a python service. Finding of external image orientation parameters through bundle adjustment calculation are parameters, in the first, defined in the local system and then transformed into the national geodetic system of the Czech Republic. This entire section is available and free to use for on the internet. The third part of the article describes the practical procedure of the interpretation of archive and wartime photographs with aim of identification of the drainage detail and the procedures leading to the interpretation, identification, location and calculation of the position of unexploded air ammunition.
MAPPING IRRIGATED AREAS USING RANDOM FOREST BASED ON GF-1 MULTI-SPECTRAL DATA
Authors: Lu Y. , Song W. , Su Z. , Lü J. , Liu Y. , Li M.
Published: August 2020

Abstract. The irrigation districts need high-resolution spatial distribution information of irrigated fields to manage irrigation water effectively and achieve sustainable water resources management, especially for fragmented croplands such as China. However, most irrigated area mapping methods by remote sensing are based on MODIS time series with a relatively low resolution of 250–1000 m. To fill this gap, this study attempted to use pixel-based random forest to map irrigated areas based on two multi-spectral images from GF-1 satellite with a resolution of 16 m in an irrigated district of China, during the winter-spring irrigation period of 2018. Accuracy of the retrieved 16-m map was assessed by accuracy error matrix using 210 ground-truth samples. The result had an overall accuracy of 93.33% with a Kappa Coefficient of 0.9164. The 16-m resulting map shows that the area of irrigated wheat, rain-fed wheat, irrigated fruit tree, and fallow croplands in the study area were 52066.48 ha, 12932.33 ha, 18104.32 ha, and 4641.25 ha respectively, accounting for 52.57%, 13.06%, 18.28% and 4.69% of the total study area, which are basically consistent with those obtained from field investigations. Compared with SVM, the random forest results are more accurate with fewer misclassifications. The pixel-based random forest for irrigated area mapping at high resolution can obtain more refined spatial distribution of irrigated areas than low-resolution images, which is suitable for fragmented croplands. Besides, this method can effectively distinguish irrigated crops from rain-fed crops, proving the classification ability of random forest in high-resolution irrigation area mapping only by two images.
CENTRAL AXIS ESTIMATION FOR ANCIENT CHINESE PAGODAS BASED ON GEOMETRIC MODELLING AND UAV-BASED PHOTOGRAMMETRY
Authors: Chan T. O. , Xia L. , Tang J. , Liu M. , Lang W. , Chen T. , Xiao H.
Published: August 2020

Abstract. Pagodas are common historical structures in China and some other Asian countries. Symmetries of the pagodas play an important role on cultural and structural aspects. In this paper, we proposed a method estimating the central axis of a typical ancient pagoda in China for examination of its symmetry. We developed a novel geometric model to fit to the point cloud of the pagoda obtained from photogrammetric reconstruction based on UAV imagery. More specifically, we developed a novel geometric model possessing 16 parameters to fit to the point cloud of different parts of the pagodas simultaneously to estimate a central axis for the symmetry. The results show that the proposed model is superior to the conventional cylindrical models for the central axis estimation. More importantly, the estimated parameters are in realistic ranges, as well as the reconstructed central axis and the sextant number are well visualized, so the symmetry of the pagodas can be readily analysed.
DIRECT SPARSE VISUAL ODOMETRY WITH STRUCTURAL REGULARITIES FOR LONG CORRIDOR ENVIRONMENTS
Authors: Cheng F. , Liu C. , Wu H. , Ai M.
Published: August 2020

Abstract. Simultaneous Localization and Mapping are the key requirements for many practical applications of robotics. However, traditional visual approaches rely on features extracted from textured surfaces, so they barely work well in indoor scenes (e.g. long corridors containing large proportions of smooth walls). In this work, we propose a novel visual odometry method to overcome these limitations, which integrates structural regularities of man-made environments in a direct sparse visual odometry system. By fully exploiting structural lines that align with the dominant direction in the Manhattan world, our approach becomes more accurate and robust to texture-less indoor environments, specially, long corridors. Given a series of image inputs, we first use the direct sparse method to obtain the coarse relative pose between camera frames, and then calculate vanishing points on each frame. Secondly, we use structural lines as rotation constraints, and perform a sliding window optimization to reduce both photometric and rotation errors, to further improve the trajectory accuracy. Through the benchmark test, it is proved that our method performs better than that of the existing visual odometry approach in long corridor environments.