bluuebunny/crossref_metadata_2025 huggingface dataset:
MATEMÁTICA E O JOGO AVANÇANDO COM O RESTO RELATO DE EXPERIÊNCIA EM MEIO A PANDEMIA
Authors: Pereira Regiane Aparecida Cruz , Peralta Deise Aparecida
Published: August 2020

Diante do cenário proporcionado pela pandemia causada pelo Novo Corona Vírus, as escolas tiveram que se reinventar. Um grande desafio para todos da comunidade educacional! Em especial, nesse relato de experiência, retratamos a mudança enfrentada pela prática do professor diante da realidade frente a novas tecnologias, as quais na atual conjuntura, são fundamentais para promover a proximidade entre professor-aluno e até mesmo, aluno-aluno durante o período de distanciamento social físico. As aulas presenciais, foram substituídas por aulas online. Com tantas mudanças, também se faz necessário repensar as práticas pedagógicas, principalmente quando se pensa em utilizar jogos para o ensino da matemática. Aqui, relatamos a experiência de uma aula de matemática, na turma do 7º ano de um colégio particular de São José do Rio Preto/SP. O jogo Avançando com o resto foi escolhido para retomar alguns conceitos matemáticos, a princípio somente a divisão, mas diante de algumas situações vivenciadas no jogo ao longo da aula, foi possível problematizar uma jogada e explorar assuntos relacionados a porcentagem e probabilidade. Além de trazer conceitos matemáticos para a aula, os alunos estavam solicitando que fosse trabalhado algum jogo, já que antes da quarentena, havia o hábito de trabalhar o jogo durante as aulas, ao menos um a cada quinze dias. Apesar de ocorrer algumas situações que precisam ser melhoradas em relação ao uso do jogo Avançando com o resto em aulas online, foi possível perceber que a maioria dos alunos participou efetivamente da aula e que os objetivos propostos foram alcançados.
PROGRAMA EDUCANDO PARA A DIVERSIDADE: UMA FERRAMENTA DE SUBVERSÃO DA IDENTIDADE DE GÊNERO
Authors: Souza Carla Araujo de , Vieira Mayanna De Vasconcelos , Taveira Flavio Augusto Leite , Camargos Liliane Santos de
Published: August 2020

O presente relato busca explanar sobre duas ações que foram realizadas ao longo do ano de 2019 no âmbito do programa Educando para a Diversidade, realizadas na Faculdade de Engenharia de Ilha Solteira da Universidade Estadual Paulista Júlio de Mesquita Filho (Unesp - Feis). Além da exposição das ações, buscaremos no decorrer deste texto, trazer um pouco da história dos núcleos que foram responsáveis pela realização das ações, nomeadamente o Núcleo de Apoio e Discussão de Gênero e Sexualidade e o Núcleo Afrobrasileiro e Indígena de Ilha Solteira. Por fim, propomos reflexões acerca de como tais atividades impactaram a vida pessoal e acadêmica dos sujeitos participantes dos núcleos.
Uma sequência didática utilizando a literatura de cordel e a arte das histórias em quadrinhos para inserção de tópicos de Física Quântica no Ensino Médio
Authors: Feitosa Samuel Dos Santos , Araujo Khennya Maria , Silva Marcelo Souza da , Nobre Francisco Augusto Silva
Published: August 2020

A inserção de tópicos de Física Moderna e Contemporânea (FMC) no Ensino Médio é um desafio no atual panorama do ensino de Física. Na perspectiva de colaborar com esse debate, apresenta-se, neste artigo, uma sequência didática utilizando material com elementos de expressão artística com o objetivo de auxiliar os professores do Ensino Médio na abordagem de tópicos de Mecânica Quântica voltados aos estudantes. A história em quadrinhos (HQs), Os moídos e pelejas desde o átomo clássico inté o átomo quântico, aborda interpretações básicas das teorias de Planck, Bohr, de Broglie e Schrödinger sobre a estrutura dos átomos. A experiência de aplicação aconteceu em uma turma do terceiro ano mediante uma sequência didática inspirada na Unidade de Ensino Potencialmente Significativa (UEPS), levando em consideração princípios da teoria cognitiva da aprendizagem significativa. Os resultados da experiência foram analisados em uma abordagem quali-quantitativa e indicaram um satisfatório avanço na desenvoltura dos estudantes, com isso, colaborando para o desenvolvimento de uma postura investigativa e demonstrando envolvimento com o material didático, comprometimento com o trabalho em equipe e, principalmente, compreensão inicial dos conceitos e fenômenos estudados. A contextualização do conteúdo em versos de cordel e ilustrações da HQs, mediante a aproximação entre Arte e Ciências, propiciou aos estudantes um pensar e estudar Física sob o viés da criatividade, bem como mais liberdade para o estímulo do desenvolvimento da capacidade imaginativa.
Relato crítico de uma experiência didática acerca de uma temática científica aplicada na educação básica: algumas reflexões epistemológicas e a defesa de um ensino de ciências fundamentado na argumentação dialógica
Authors: Guimarães Ricardo Rangel , Massoni Neusa Teresinha
Published: August 2020

Este trabalho tem como objetivo principal fazer um relato crítico e propor reflexões epistemológicas sobre o uso da metodologia de ensino por argumentação em uma atividade de divulgação de uma temática científica desenvolvida na disciplina de Física, em uma turma de ensino médio de uma escola pública estadual de educação básica de Porto Alegre (RS). Tal atividade, desenvolvida no formato de um júri simulado, foi pensada e elaborada para ser aplicada na forma de um micromódulo de ensino em que assumimos ser possível, mediante este recurso pedagógico, atingir um nível de aprendizado adequado a essa faixa etária, através do debate e da argumentação dialógica. Buscamos, para tanto, oferecer elementos para que os estudantes se envolvam em tal estratégia no ambiente de sala de aula e desenvolvam habilidades de argumentação. Os referenciais teórico-metodológicos utilizados nesta pesquisa são os do ensino por argumentação articulado a uma epistemologia que desenvolva virtudes intelectuais e o pensamento crítico no estudante. O que se pretende neste trabalho, que é parte de uma investigação mais abrangente, é fazer através do relato crítico do estudo de caso, uma defesa desses referenciais como potencializadores para a promoção de uma educação científica mais autônoma e reflexiva.
Tarefas para Aulas Invertidas: relato de experiência docente com deveres de casa on-line em curso de Física
Authors: Faria Alexandre F. , Vaz Arnaldo M.
Published: August 2020

Tarefas para casa – ou deveres escolares – sempre estiveram presentes em Cursos de Física da Educação Básica e da Educação superior. As metodologias ativas atribuíram novas funções às lições de casa. Contudo, essas lições não têm sido objeto de pesquisa empírica, nem de relato docente. Neste relato de experiência, descrevemos o uso, em dois anos letivos, de tarefas on-line para sala de aula invertida junto a cinco turmas da 2ª série do Ensino Médio de uma escola pública federal localizada em Belo Horizonte. O relato inclui: precedentes dessa iniciativa; detalhes do contexto escolar; considerações reflexivo-analíticas; exemplo de desenvolvimento e implementação de uma tarefa; avaliação da iniciativa com base em taxas de resposta e de enquetes sobre as tarefas. Esta avaliação nos mostrou que, embora desinteressados pelos deveres escolares, os estudantes se engajaram na sua realização. Inferimos que seu engajamento decorre do trabalho da equipe de professores e da capacidade destes de convencer os estudantes da importância das tarefas para casa para o aprendizado em física.
Estudo de Colisões Inelásticas por meio da Videoanálise
Authors: Montoli Guilherme Almeida , Cabral Neto João Dos Santos
Published: August 2020

Nos vários níveis de ensino é apresentado ao estudante o fenômeno da colisão. Comumente, privilegia-se de dois tipos de colisões: perfeitamente (ou totalmente) elástica (coeficiente de restituição CR = 1) e perfeitamente (ou totalmente) inelástica (coeficiente de restituição CR = 0). Contudo, boa parte dos fenômenos físicos em que ocorre colisão são do tipo inelástico, e por isso que perguntamos: como descrever o valor do coeficiente de restituição que assume valores 0 < CR < 1  para uma colisão inelástica? Neste trabalho, usando o método experimental em que se deixa cair uma bola ao chão, filmamos o movimento da bola rebotando e por meio da videoanálise, encontramos o valor de CR para uma colisão inelástica. Além disso, usamos o modelo do oscilador harmônico amortecido para modelar o movimento da bola rebotando e determinar e em função da constante de amortecimento dos osciladores.
Geradores elétricos monofásicos e trifásicos como suporte didático para o ensino de Eletromagnetismo
Authors: Alves André Luíz , Motta Yohannes Benincá , Zancanella Antônio Carlos Barbosa
Published: August 2020

A energia elétrica é de fundamental importância para o mundo moderno, pois é fonte de luz, calor e força motriz de todas as máquinas e equipamentos eletroeletrônicos. Buscando mostrar a importância da física no cotidiano dos estudantes, propõe-se neste trabalho a construção e estudo de geradores elétricos de corrente alternada dos tipos monofásicos e trifásicos. O estudante ao se deparar com este experimento identificará a importância da lei de Faraday para a compreensão do funcionamento dos geradores. Foi possível entender a partir desta lei, a conexão entre as bobinas e a disposição dos ímãs nos sistemas estudados. Foram construídos gráficos para investigar a relação entre a frequência da onda gerada e a frequência de rotação do gerador, e também, a relação entre a tensão eficaz e a frequência de rotação. Em especial, no sistema trifásico foi possível compreender por meio de ilustrações, as conexões entre as bobinas para geradores com seis ou mais bobinas nas ligações do tipo estrela e triângulo. O aparato experimental é de grande praticidade, fácil de ser construído, de ser montado e desmontado. O custo médio foi em torno de R$ 300,00, o qual varia com a quantidade de sistemas construídos.
Determinação do momento de inércia de um anel não homogêneo com uso da análise de vídeo
Authors: Silva Victor Abath da , Cruz Frederico Alan de Oliveira
Published: August 2020

O momento de inércia de um corpo é uma grandeza estudada em muitas disciplinas introdutórias de física, nos cursos de ciências exatas do ensino superior, em sua maioria numa abordagem puramente teórica e com maior ênfase nos corpos com distribuição de massa homogênea, devido à complexidade do problema. Para mostrar como isso pode ser afetado quando não existe essa homogeneidade é apresentado, neste trabalho, um método, baseado na análise de vídeo, que busca avaliar o movimento de um anel sobre um plano em diferentes ângulos de inclinação. Os resultados mostram que a metodologia é eficiente na determinação do momento de inércia de um anel homogêneo, uma vez que para esse tipo de objeto o raio de giração esperado é igual a 1 e o valor obtido foi de 0,97, isto é, um desvio de 3% em relação ao valor teórico. Esse resultado serviu para validar o valor de 0,66 do raio de giração de um anel não homogêneo era o correto, visto também ter apresentado variação aproximada de 3% entre os valores obtidos.
A-Train estimates of the sensitivity of warm rain likelihood and efficiency to cloud size, environmental moisture, and aerosols
Authors: Smalley Kevin M. , Rapp Anita D.
Published: August 2020

Abstract. Precipitation efficiency has been found to play an important role in constraining the sensitivity of the climate through its role in controlling cloud cover, yet understanding of its controls are not fully understood. Here we use CloudSat observations to identify individual contiguous shallow cumulus cloud objects and compute the ratio of cloud water path to rain water path as a proxy for warm rain efficiency (WRE). Cloud objects are then conditionally sampled by cloud-top height, relative humidity, and aerosol optical depth (AOD) to analyze changes in WRE as a function of cloud size (extent). For a fixed cloud-top height, WRE increases with extent and environmental humidity following a double power-law distribution, as a function of extent. Similarly, WRE increases holding environmental moisture constant. There is surprisingly little relationship between WRE and AOD when conditioned by cloud-top height, suggesting that once rain drop formation begins, aerosols may not be as important for WRE as cloud size and depth. Consistent with prior studies, results show an increase in WRE with sea surface temperature. However, for a given depth and SST, WRE is also dependent on cloud size and becomes larger as cloud size increases. Given that larger objects become more frequent with increasing SST, these results imply that increasing precipitation efficiencies with SST are due not only to deeper clouds with greater cloud water contents, but also the propensity for larger clouds which may have more protected updrafts.
Coupling a gas chromatograph simultaneously to a flame ionization detector and chemical ionization mass spectrometer for isomer-resolved measurements of particle-phase organic compounds
Authors: Bi Chenyang , Krechmer Jordan E. , Frazier Graham O. , Xu Wen , Lambe Andrew T. , Claflin Megan S. , Lerner Brian M. , Jayne John T. , Worsnop Douglas R. , Canagaratna Manjula R. , Isaacman-VanWertz Gabriel
Published: August 2020

Abstract. Atmospheric oxidation products of volatile organic compounds consist of thousands of unique chemicals that have distinctly different physical and chemical properties depending on their detailed structures and functional groups. Measurement techniques that can achieve molecular characterizations with details down to functional groups (i.e., isomer-resolved resolution) are consequently necessary to provide understandings of differences of fate and transport within isomers produced in the oxidation process. We demonstrate a new instrument coupling the thermal desorption aerosol gas chromatograph (TAG), which enables the separation of isomers, with the high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS), which has the capability of classifying unknown compounds by their molecular formulas, and the flame ion detector (FID), which provide a near-universal response to organic compounds. The TAG-CIMS/FID is used to provide isomer-resolved measurements of samples from liquid standard injections and particle-phase organics generated in oxidation flow reactors. By coupling a TAG to a CIMS, the CIMS is enhanced with an additional dimension of information (resolution of individual molecules) at the cost of time resolution (i.e., one sample per hour instead of per minute). We found that isomers are prevalent in sample matrix with an average number of three to five isomers per formula depending on the precursors in the oxidation experiments. Additionally, a multi-reagent ionization mode is investigated in which both zero air and iodide are introduced as reagent ions, to examine the feasibility of extending the use of an individual CIMS to a broader range of analytes with still selective reagent ions. While this approach reduces iodide-adduct ions by a factor of two, [M−H]− and [M+O2]− ions produced from lower-polarity compounds increase by a factor of five to ten, improving their detection by CIMS. The method expands the range of detected chemical species by using two chemical ionization reagents simultaneously, enabled by the pre-separation of analyte molecules before ionization.
GEOMATIC TECHNIQUES FOR THE OPTIMIZATION OF SKI RESOURCES
Authors: Aicardi I. , Angeli S. , Grasso N. , Lingua A. M. , Maschio P.
Published: August 2020

Abstract. Climate change is already affecting the entire world, with extreme weather conditions such as drought, heat waves, heavy rain, floods and landslides becoming more frequent, including Europe. In according to Paris agreement and relative European announcement of Carbon neutrality (by 2050), the saving of water and energy supplies is a fundamental aspect in the management of resources in production, sports, hospitality facilities and so on. Some methodologies for the optimization of the consumption of natural resources are required. This article describes an activity aimed at measuring, monitoring and analysing the thickness of the snowpack on the ski slopes during the winter season to permit a sustainable approach of snowmaking in alpine ski areas . The authors propose a methodology based on the integration of multitemporal surface (ground/snow) survey by Autonomous Aerial Vehicle (AAV) and low cost GNSS receivers mounted on snow groomers for a RTK (Real Time Kinematic) solution. To obtain a complete snow surface digital models with poor detailed images on ski slopes, some pre-processing techniques have been analysed to locally improve contrast and details with a local high pass filtering. The methodology has been employed in two study areas (Limone Piemonte, Prato Nevoso) located in the province of Cuneo, in the southern alpine area of Piedmont.
ANALYSIS OF GULLY EROSION IN A CATCHMENT AREA IN OLIVE GROVES USING UAS PHOTOGRAMMETRY TECHNIQUES
Authors: Fernández T. , Gómez-Lopez J. M. , Pérez-García J. L. , Cardenal J. , Delgado J. , Mata E. , Sánchez-Gómez M. , Calero J. , Tovar-Pescador J. , Moya F.
Published: August 2020

Abstract. An approach based on images captured by means of an Unmanned Aerial System (UAS) and Structure from Motion – Multi Video Stereo (SfM-MVS) photogrammetry is presented for the study of the gully erosion in a catchment area of about 16 ha. The study area is located in the province of Jaén (SW Spain) where the main land use is the olive groves. Three UAS flights have been made in April 2019, May 2019 and February 2020 with an average GSD of 2–3 cm. The image processing has been carried out using some field surveyed GNSS ground control points and the RTK positioning of the UAV with errors lower than 0.03 m and 0.04 m in XY and Z, respectively. Then DSMs and orthophotographs were obtained with a resolution of 0.10 m and 0.05 m, respectively, and DSM of differences (DoDs) were calculated, with an uncertainty of about ±0.15 m. Finally, the DoDs were analysed in the GIS, in order to calculate the height differences and volumes between the three flights. The analysis has showed the areas affected by the gully erosion processes that correspond mainly to ground descents due to significant channel erosion and mass movements at the steep sidewalls. Considering the balance between depletion and deposition processes, the average height differences is about −0.03 m and the volume is about −42 m3, that is the depletion predominates. The main changes have been detected in the second period (May 2019–February 2020), while in the first one (April 2019–May 2019) they are practically insignificant and limited to small areas.
GROUND TRUTH GENERATION AND DISPARITY ESTIMATION FOR OPTICAL SATELLITE IMAGERY
Authors: Cournet M. , Sarrazin E. , Dumas L. , Michel J. , Guinet J. , Youssefi D. , Defonte V. , Fardet Q.
Published: August 2020

Abstract. Several 3D reconstruction pipelines are being developed around the world for satellite imagery. Most of them implement their own versions of Semi-Global Matching, as an option for the matching step. However, deep learning based solutions already outperform every SGM derived algorithms on Kitti and Middlebury stereo datasets. But these deep learning based solutions need huge quantities of ground truths for training. This implies that the generation of ground truth stereo datasets, from satellite imagery and lidar, seems to be of great interest for the scientific community. It will aim at reducing the potential transfer learning difficulties, that could arise from a training done on datasets such as Middlebury or Kitti. In this work, we present a new ground truth generation pipeline. It produces stereo-rectified images and ground truth disparity maps, from satellite imagery and lidar. We also assess the rectification and the disparity accuracies of these outputs. We finally train a deep learning network on our preliminary ground truth dataset.
IMPROVING DISPARITY ESTIMATION BASED ON RESIDUAL COST VOLUME AND RECONSTRUCTION ERROR VOLUME
Authors: Kang J. , Chen L. , Deng F. , Heipke C.
Published: August 2020

Abstract. Recently, great progress has been made in formulating dense disparity estimation as a pixel-wise learning task to be solved by deep convolutional neural networks. However, most resulting pixel-wise disparity maps only show little detail for small structures. In this paper, we propose a two-stage architecture: we first learn initial disparities using an initial network, and then employ a disparity refinement network, guided by the initial results, which directly learns disparity corrections. Based on the initial disparities, we construct a residual cost volume between shared left and right feature maps in a potential disparity residual interval, which can capture more detailed context information. Then, the right feature map is warped with the initial disparity and a reconstruction error volume is constructed between the warped right feature map and the original left feature map, which provides a measure of correctness of the initial disparities. The main contribution of this paper is to combine the residual cost volume and the reconstruction error volume to guide training of the refinement network. We use a shallow encoder-decoder module in the refinement network and do learning from coarse to fine, which simplifies the learning problem. We evaluate our method on several challenging stereo datasets. Experimental results demonstrate that our refinement network can significantly improve the overall accuracy by reducing the estimation error by 30% compared with our initial network. Moreover, our network also achieves competitive performance compared with other CNN-based methods.
EVALUATING SECTOR RING HISTOGRAM OF ORIENTED GRADIENTS FILTER IN LOCATING HUMANS WITHIN UAV IMAGES
Authors: Ghasemi M. , Varshosaz M. , Pirasteh S.
Published: August 2020

Abstract. Developing systems to find injured people quickly after natural disasters is an important topic. In recent years, special attention has been paid to the use of UAV images for this purpose. In this regard, an accurate and strong feature is required. It is shown that the Sector Ring Histogram of Oriented Gradients, is a feature very much independent from rotation and scale. The aim of this paper is to evaluate the performance of a human detection algorithm which is based on this strong feature. Experiments carried out suggest that using SRHOG feature humans can be detected with an accuracy of 73.69%. However, despite giving good accuracy, SRHOG results contain more than 33.33 % false labels.
MARKER-LESS MOBILE AUGMENTED REALITY APPLICATION FOR MASSIVE 3D POINT CLOUDS AND SEMANTICS
Authors: Kharroubi A. , Billen R. , Poux F.
Published: August 2020

Abstract. Mobile Augmented Reality (MAR) attracts significant research and development efforts from both the industry and academia, but rarely integrate massive 3D dataset’s interactions. The emergence of dedicated AR devices and powerful Software Development Kit (ARCore for android and ARKit for iOS) improves performance on mobile devices (Smartphones and tablets). This is aided by new sensor integration and advances in computer vision that fuels the development of MAR. In this paper, we propose a direct integration of massive 3D point clouds with semantics in a web-based marker-less mobile Augmented Reality (AR) application for real-time visualization. We specifically investigate challenges linked to point cloud data structure and semantic injection. Our solution consolidates some of the overarching principles of AR, of which pose estimation, registration and 3D tracking. The developed AR system is tested on mobile phones web-browsers providing clear insights on the performance of the system. Promising results highlight a number of frame per second varying between 27 and 60 for a real-time point budget of 4.3 million points. The point cloud tested is composed of 29 million points and shows how our indexation strategy permits the integration of massive point clouds aiming at the point budget. The results also gives research directions concerning the dependence and delay related to the quality of the network connection, and the battery consumption since portable sensors are used all the time.
AUTOMATION OF THERMAL POINT CLOUDS ANALYSIS FOR THE EXTRACTION OF WINDOWS AND THERMAL BRIDGES OF BUILDING FACADES
Authors: Macher H. , Landes T. , Grussenmeyer P.
Published: August 2020

Abstract. In the context of building renovation, energy audits of buildings are performed in order to reduce energy consumption and greenhouse gas emissions. Infrared cameras allow analyzing precisely the energetic performances and the thermal comfort of a building and the analysis of thermal images of buildings represents a key step on the way to the reduction of energy consumption. In the last few years, the combination of thermal and metric information has been increasingly studied. The geometry provides the exact location of defects and enables accurate energy analysis. This paper focuses on the automatic analysis of facades point clouds with thermal attributes. The aim of our work is to extract windows and thermal bridges by considering both geometric and thermal features. The extraction of objects or zones of interest in thermal data consists in the detection of significant temperature variations. The developed approach, mainly based on the automatic analysis of histograms, is applied to a point cloud acquired by a Mobile Mapping Unit.
SCANLINE NORMALIZATION FOR MMS DATA MEASURED UNDER DIFFERENT CONDITIONS
Authors: Takahashi G. , Masuda H.
Published: August 2020

Abstract. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner is widely used for mapping. Various automatic mapping methods have been proposed for point clouds measured by the high-density LiDAR scanner on the MMS. However, careful parameter tuning is often required according to measurement conditions. In this paper, we propose a method to generate normalized scanlines from point clouds captured using the MMS. Normalized scanlines are useful to avoid parameter tuning depending on the measurement conditions. In order to evaluate the validity of our method, we extracted road boundaries with the same parameters from two point clouds measured under different conditions. In our evaluation, our method could detect almost the same road boundaries from the two point clouds using the same parameter settings.
SEMANTIC CLASSIFICATION OF SANDSTONE LANDSCAPE POINT CLOUD BASED ON NEIGHBOURHOOD FEATURES
Authors: Tomková M. , Lysák J. , Potůčková M.
Published: August 2020

Abstract. The technology of airborne laser scanning enables fast and accurate gathering spatial data containing also echoes from the terrain below the vegetation canopy that is beneficial for topographic mapping of wooded sandstone landscapes in Czechia, Poland, and Germany. The challengeable task is to determine the ground points in the point cloud because commonly used filtration methods do not successfully distinguish between vegetation and rock pillars and faces. In this paper, we replace filtration with classification approach using the features derived from characteristics of points within a neighbourhood of optimized sizes, such as eigenvalue-based features and echo ratio. Random Forest classifier is trained and tested on the manually labelled dataset with a density of almost 650 points/m2 from the Adršpach-Teplice Rocks. The overall accuracy reaches 87% but recall and precision of non-ground points are unsatisfactory. Misclassified non-ground points are located also within trees, thus we do not consider the result as suitable for DTM processing without further processing.
INDOOR 3D POINT CLOUDS SEMANTIC SEGMENTATION BASES ON MODIFIED POINTNET NETWORK
Authors: Zhao J. , Zhang X. , Wang Y.
Published: August 2020

Abstract. Indoor 3D point clouds semantics segmentation is one of the key technologies of constructing 3D indoor models,which play an important role on domains like indoor navigation and positioning,intelligent city, intelligent robot etc. The deep-learning-based methods for point cloud segmentation take on higher degree of automation and intelligence. PointNet,the first deep neural network which manipulate point cloud directly, mainly extracts the global features but lacks of learning and extracting local features,which causes the poor ability of segmenting the local details of architecture and affects the precision of structural elements segmentation . Focusing on the problems above,this paper put forward an automatic end-to-end segmentation method base on the modified PointNet. According to the characteristic that the intensity of different indoor structural elements differ a lot, we input the point cloud information of 3D coordinate, color and intensity into the feature space of points. Also,a MaxPooling is added into the original PointNet network to improve the ability of attracting and learning local features. In addition, replace the 1×1 convolution kernel of original PointNet with 3×3 convolution kernel in the process of attracting features to improve the segmentation precision of indoor point cloud. The result shows that this method improves the automation and precision of indoor point cloud segmentation for the precision achieves over 80% to segment the structural elements like wall,door and so on ,and the average segmentation precision of every structural elements achieves 66%.
AIRBORNE LIDAR POINT CLOUD CLASSIFICATION FUSION WITH DIM POINT CLOUD
Authors: Zhou M. , Kang Z. , Wang Z. , Kong M.
Published: August 2020

Abstract. Airborne Light Detection And Ranging (LiDAR) point clouds and images data fusion have been widely studied. However, with recent developments in photogrammetric technology, images can now provide dense image matching (DIM) point clouds. To make use of such DIM points, a sample selection framework is introduced. That is, first, the geometric features of LiDAR points and DIM points are extracted. Each feature per point is considered a sample. Then we extend the binary TrAdaboost classifier into a multi-class one to train all the samples. The classifier automatically assigns weights to the samples in the DIM points. The useful samples are assigned large weights and consequently impact the classification results largely and vice versa. As a result, the useful samples of the DIM points are kept to improve on the LiDAR points classification performance. Because only the samples are used, the registration between the DIM points and LiDAR points is not needed. Moreover, the DIM points capturing similar classes but not the same scene as the LiDAR points can also be used. By our framework, existing aerial images can be fully utilized. For testing the generation ability, the framework is applied in a super-voxel-based classification approach by replacing the points-based features with the super-voxel-based features. In the experiments, whether DIM points at the same places as those of LiDAR are used or not, the results after fusion show that, the LiDAR points classification performance has improved. Also, the better the quality of DIM points are, the better the classification performance achieves.
MODELLING A CELL TOWER USING SFM: AUTOMATED DETECTION OF STRUCTURAL ELEMENTS FROM SKELETON EXTRACTION ON A POINT CLOUD
Authors: Deidda M. , Pala A. , Sanna G.
Published: August 2020

Abstract. The surveying and management of telecommunication towers poses a series of engineering challenges. Not only they must be regularly inspected for the purpose of checking for issues that require maintenance interventions, but they are often sub-let by their owners to communication companies, requiring a survey of the many (several thousand per company) installed appliances to check that they respect the established contracts. This requires a surveying methodology that is fast and possibly automated. Photogrammetric techniques using UAV-mounted cameras seem to offer a solution that is both suitable and economical. Our research team was asked to evaluate whether, from the information acquired by small drones it was possible to obtain geometric information on the structure, with what degree of accuracy and what level of detail. The workflow of this process is naturally articulated in three steps: the acquisition, the construction of the point cloud, and the extraction of geometries. The case study is a tower carrying antennas owned by several operators and placed in the industrial district of Cagliari. The article examines the problems found in modelling such structures using point clouds derived from the Structure-from-Motion technique, in order to obtain a model of nodes and beams suitable for the reconstruction of the structure’s geometric elements, and possibly for a finite elements analysis or for populating GIS and BIM, either automatically or with minimal user intervention. In order to achieve this, we have used voxelization and skeleton extraction algorithms to obtain a 3D graph of the structure. The analysis of the results was carried out by varying the parameters relating to the voxel size, which defines the resolution, and the density of the points contained inside each voxel.
THE IMPACT OF POINT CLOUD DENSITY ON BUILDING OUTLINE EXTRACTION
Authors: Drešček U. , Kosmatin Fras M. , Lisec A. , Grigillo D.
Published: August 2020

Abstract. Recently, building outline extraction from point cloud has gained momentum in particular in the context of 3D building modelling based on a data-driven approach, which has also been our motivation. For an accurate building outline extraction from a point cloud, various factors affecting the quality should be considered. In this research, we analysed the influence of point cloud density on the quality of the extracted building outlines. The input data was a classified photogrammetric point cloud, obtained from the dense image matching of images acquired by an optical sensor mounted on the unmanned aerial vehicle (UAV). For outline extraction, we selected two procedures, namely the direct approach and the raster approach. In the direct approach, building outlines are extracted directly from the points that have been classified as buildings. First, a convex hull with the alpha algorithm is estimated, which is further generalised with the Douglas-Peucker algorithm. This is followed by the shape regularisation to ensure perpendicular angles of the outline. In the raster approach, we first rasterised the building points and then extracted the building outlines using the Hough transform. In both approaches, the result is a roof outline in a 2D plane representing the maximum extent of the building above the surface. The building outlines were extracted from point clouds with five different densities. For both approaches, the quality assessment has shown that point cloud density has an impact on the building outline extraction, especially on the completeness of the outlines.
SEMI-AUTOMATIC CITYSCAPE 3D MODEL RESTORATION USING GENERATIVE ADVERSARIAL NETWORK
Authors: Gorbatsevich V. , Kulgildin B. , Melnichenko M. , Vygolov O. , Vizilter Y.
Published: August 2020

Abstract. The paper addresses the problem of a city heightmap restoration using satellite view image and some manually created area with 3D data. We propose the approach based on generative adversarial networks. Our algorithm contains three steps: low quality 3D restoration, buildings segmentation using restored model, and high-quality 3D restoration. CNN architecture based on original ResDilation blocks and ResNet is used for steps one and three. Training and test datasets were retrieved from National Lidar Dataset (United States) and the algorithm achieved approximately MSE = 3.84 m on this data. In addition, we tested our model on the completely different ISPRS Potsdam dataset and obtained MSE = 5.1 m.
PREDICTING THE ACCURACY OF PHOTOGRAMMETRIC 3D RECONSTRUCTION FROM CAMERA CALIBRATION PARAMETERS THROUGH A MULTIVARIATE STATISTICAL APPROACH
Authors: Saponaro M. , Capolupo A. , Caporusso G. , Borgogno Mondino E. , Tarantino E.
Published: August 2020

Abstract. Several tools have been introduced to generate accurate 3D models. Among these, Unmanned Aerial Vehicles (UAVs) are an effective low-cost tool to go beyond on-fields effort limits since they allow to fly over areas difficult to reach and to reduce the time needed to collect and process photogrammetric pictures as well. Combining their versatility with Structure from Motion (SfM) techniques efficiency has provided a widely accessible approach to generate accurate photogrammetric products. However, the outcome resolution and coherences also depend on sensor traits. Therefore, UAVs are usually equipped with low-cost non-metric cameras, with the consequent requirement for a calibration procedure to increase the final 3D models accuracy. Although several researchers have highlighted the strong impact of camera calibration parameters on the photogrammetric outcomes, their linkage has not been explored yet. This paper is aimed at investigating their relationship and to propose a novel predicting function of 3D photogrammetric reconstruction accuracy. Such function was estimated thanks to the application of the Principal Components Analysis (PCA) technique. Four photogrammetric UAV flight surveys provided the input data of PCA while an extra dataset was used to validate the results. Once PCA was completed, a synthetic index was proposed and the coefficient of determination was calculated between the index and error components. Synthetic indices values for the various datasets were applied as baseline to detect a predictive function able to assess the northern and eastern error components with a deviation of 0.005 m and 0.003 m, respectively. The proposed approach shows promising and satisfying results for predicting 3D models accuracy.
POWER TRANSMISSION LINE RECONSTRUCTION FROM SEQUENTIAL OBLIQUE UAV IMAGES
Authors: Wu Z. , Lei W. , Sun W. , Chen C. , Wang H. , Yu W. , Zhong H. , Zhu C. , Xi J. , Yang B.
Published: August 2020

Abstract. With the accelerated development of the global economy, the scale of UHV (Ultra High Voltage) large-capacity transmission lines has increased dramatically. Nevertheless, existing transmission line inspection methods rely on human inspectors detecting abnormal facilities from ground level or helicopters. Those methods not only require extremely high labor and equipment costs, but also are time-consuming. In this paper, a thin object 3D reconstruction method is proposed for the extraction of 3D structures of power lines from sequential oblique UAV images. The proposed method recovers the 3D structure of the power lines from three sequential images that depict the structure of power lines in 2D. Proposed method was tested in a typical transmission line corridor and the reconstructed results show the effectiveness of the proposed method.
GEOMETRIC QUALITY EVALUATION METHOD FOR MULTI-VIEW SATELLITE IMAGES IN 3D RECONSTRUCTION
Authors: Yi H. , Chen X. , Wang D. , Du S. , Xu B. , Wang D.
Published: August 2020

Abstract. The quality of the 3D model reconstructed using multi-view satellite image depends on the quality of the image. To evaluate the geometric quality of the satellite image, we proposed a method to evaluate the geometric distortion for satellite images and defined the deviation coefficient as a metric to evaluate the bending degree of a curve. After projecting a ground grid consisting of straight lines into the image space, the geometric distortion of the image can be evaluated quantitatively by calculating the deviation coefficients of the projection trajectories. Experiments have been carried out with three datasets obtained by JiLin-1, GaoFen-2, and WorldView-3 respectively. The results show that the proposed method can used to evaluate the geometric quality of satellite images effectively, and this evaluation method will be useful in image selecting in 3D reconstruction using multi-view satellite images.
OPTIMIZATION ALGORITHM OF TILTED IMAGE MATCHING BASED ON ADAPTIVE INITIAL OBJECT ASPECT
Authors: Zhang C. , Ge Y. , Zhang Q. , Guo B.
Published: August 2020

Abstract. When adopting the matching method of the least squares image based on object-patch to match tilted images, problems like the low degree of connection points for images with the discontinuity of depth or the discrepancy in elevation or low availability of aerotriangulation points would frequently appear. To address such problems, a tilted-image-matching algorithm based on an adaptive initial object-patch is proposed by this paper. By means of the existing initial values of the interior and exterior orientation elements of the tilted image and the information of object points generated in the matching process, the algorithm takes advantage of the method of multi-patch forward intersection and object variance partition so as to adaptively calculate the elevation of the object-patch and the initial value of the normal vector direction angle. Furthermore, this algorithm aims to solve the problem of difficulties in matching the tilted image with its corresponding points brought about by the low accuracy of the initial value of the tilted image when adopting the matching method of the least squares image based on object-patch to match the tilted image with high discrepancy in elevation. We adopt the algorithm as proposed in this paper and the least squares image matching method in which the initial state of the object-patch is horizontal to the object-patch respectively to conduct the verification process of comparing and matching two groups of tilted images. Finally, the effectiveness of the algorithm as proposed in this paper is verified by the testing results.
SYNTHETIC VISION SYSTEM CALIBRATION FOR CONFORM PROJECTION ON THE PILOT’S HEAD-UP DISPLAY
Authors: Danilov S. , Kozyrev M. , Grechanichenko M. , Grodzitskiy L. , Mizginov V. , Kniaz V. V.
Published: August 2020

Abstract. Situational awareness of the crew is critical for the safety of the air flight. Head-up display allows providing all required flight information in front of the pilot over the cockpit view visible through the cockpit’s front window. This device has been created for solving the problem of informational overload during piloting of an aircraft. While computer graphics such as scales and digital terrain model can be easily presented on the such display, errors in the Head-up display alignment for correct presenting of sensor data pose challenges. The main problem arises from the parallax between the pilot’s eyes and the position of the camera. This paper is focused on the development of an online calibration algorithm for conform projection of the 3D terrain and runway models on the pilot’s head-up display. The aim of our algorithm is to align the objects visible through the cockpit glass with their projections on the Head-up display. To improve the projection accuracy, we use an additional optical sensor installed on the aircraft. We combine classical photogrammetric techniques with modern deep learning approaches. Specifically, we use an object detection neural network model to find the runway area and align runway projection with its actual location. Secondly, we re-project the sensor’s image onto the 3D model of the terrain to eliminate errors caused by the parallax. We developed an environment simulator to evaluate our algorithm. Using the simulator we prepared a large training dataset. The dataset includes 2000 images of video sequences representing aircraft’s motion during takeoff, landing and taxi. The results of the evaluation are encouraging and demonstrate both qualitatively and quantitatively that the proposed algorithm is capable of precise alignment of the 3D models projected on a Head-up display.
VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VIDEOS
Authors: Shahbazi M. , Simeonova S. , Lichti D. , Wang J.
Published: August 2020

Abstract. In this paper, a solution for vehicle speed estimation using unmanned aerial videos is described. First, convolutional neural networks and Kalman filtering using deep features are used for detecting and tracking vehicles. Then, a photogrammetric approach is developed for estimating the three-dimensional (3D) position of the tracked vehicles on the road, which allows determining their speed. No assumptions are made about either the 3D structure of the road (e.g., constraining it to be a planar surface) or the camera pose (e.g., restricting it to be stationary). Therefore, this solution applies to videos acquired by a moving unmanned aerial vehicle from complex road structures (e.g., multi-level highways). This solution is also robust to changes of viewpoint and scale, which makes it applicable to situations where cars undergo orientation and resolution changes as observed from the sky (e.g., in roundabouts). Experiments showed that a high detection accuracy could be achieved with an F1-score of 94.54%. Besides, the tracking technique performed well, with a multiple-object tracking accuracy of 89.8% at a speed of 11 frames per second on videos of 2720×1530 pixels. Vehicle positioning (and thus, speed estimation) could be performed with an average accuracy of 0.6 m.
AUTOMATIC GEOREFERNCING OF CLOSE-RANGE FAÇADE IMAGES ACQUIRED IN AN NARROW AND LONG ALLEYWAY USING RTK DRONE IMAGES
Authors: Park K. , Ham S. , Lee I.
Published: August 2020

Abstract. The city of Seoul has selected Sewoon market building and its surrounding district as part of the urban regeneration zone, and currently has been promoting the project. To monitor results of the project regularly, the city has been trying to utilize a 3 dimension model of the area. In the case of buildings placed in narrow alleyways in the district, however, it is limited to generate 3D model of the buildings due to some factors. Therefore, in this study, a 3D model of façade of the building was created, using a RTK drone and action camera only. First method is to estimate of location of conjugate points using Structure from Motion, after setting conjugate points between images of the drone. Second method is to georeference action camera images by setting drone images as the reference images itself without the process of estimating location of the conjugate points. As a result of preliminary experiments to verify the two methods, the error of each method did not exceed a maximum of 0.030 m. Based on the result, we created 3D models of façade of the building in the alleyway, which is located at the intersection of Donhwamoon-ro 2 gil and Jong-ro 24 gil, and calculated absolute distance between the models. And the comparison showed that the difference was about 0.010 m on average.
UNDERSTANDING 3D POINT CLOUD DEEP NEURAL NETWORKS BY VISUALIZATION TECHNIQUES
Authors: Cao Y. , Previtali M. , Scaioni M.
Published: August 2020

Abstract. In the wake of the success of Deep Learning Networks (DLN) for image recognition, object detection, shape classification and semantic segmentation, this approach has proven to be both a major breakthrough and an excellent tool in point cloud classification. However, understanding how different types of DLN achieve still lacks. In several studies the output of segmentation/classification process is compared against benchmarks, but the network is treated as a “black-box” and intermediate steps are not deeply analysed. Specifically, here the following questions are discussed: (1) what exactly did DLN learn from a point cloud? (2) On the basis of what information do DLN make decisions? To conduct such a quantitative investigation of these DLN applied to point clouds, this paper investigates the visual interpretability for the decision-making process. Firstly, we introduce a reconstruction network able to reconstruct and visualise the learned features, in order to face with question (1). Then, we propose 3DCAM to indicate the discriminative point cloud regions used by these networks to identify that category, thus dealing with question (2). Through answering the above two questions, the paper would like to offer some initial solutions to better understand the application of DLN to point clouds.