bluuebunny/crossref_metadata_2025 huggingface dataset:
GÊNERO E COMPORTAMENTO JUDICIAL NO SUPREMO TRIBUNAL FEDERAL: OS MINISTROS CONFIAM MENOS EM RELATORAS MULHERES?
Authors: Werneck Arguelhes Diego , Cesário Alvim Gomes Juliana , Nogueira Rafaela
Published: September 2018

Tribunais constitucionais com frequência se apresentam como instituições particularmente bem posicionadas para defender e promover direitos de minorias, incluindo a igualdade de gênero. No entanto, não é raro que ocorra discriminação de gênero dentro dessas próprias instituições. Embora estudos empíricos venham enfocando o impacto do gênero como variável explicativa das decisões judiciais, estereótipos e hierarquias de gênero podem influenciar do comportamento judicial para alem do conteúdo das decisões - como, por exemplo, na forma pela qual os juízes se relacionam entre si durante as deliberações. Este artigo enfoca uma faceta desse fenômeno no âmbito do processo decisório do Supremo Tribunal Federal. Examinando um banco de dados com todas as decisões tomadas no plenário e nas turmas do tribunal entre 2001 e 2013, analisamos o impacto da variável gênero em duas dimensões do comportamento judicial em um ambiente colegiado. Mais especificamente, testamos se o gênero do(a) ministro(a) afeta o modo como seus(suas) colegas se comportam quando se trata de (i) divergir ou não do voto do relator; e (ii) pedir ou não vista dos autos. Nossos resultados apontam para um possível impacto do gênero nas atitudes dos juízes em relação a mulheres relatoras em uma dessas duas dimensões. Quando o relator do caso é do sexo feminino, os outros juízes têm maior probabilidade de divergir do seu voto. Esses resultados sugerem que certos estereótipos de gênero - por exemplo, a idéia de que as mulheres são menos competentes ou confiáveis, e/ou menos capazes de retaliar - podem ajudar a explicar o comportamento dos juízes no STF e nos tribunais brasileiros em geral.
POLÍTICAS PÚBLICAS E A CONCRETIZAÇÃO DE DIREITOS SOCIAIS: TOMADA DE DECISÃO, ARQUITETURA DE ESCOLHAS E EFETIVIDADE
Authors: Reymao Ana Elizabeth Neirao , Caçapietra Ricardo Dos Santos
Published: September 2018

O texto objetiva discutir a utilização da ciência comportamental na implementação de políticas públicas e a importância de melhor estruturar o desenho das mesmas, contribuindo-se para a concretização de direitos sociais e observando-se critérios comportamentais e cognitivos dos indivíduos. A pesquisa apresentada é exploratória, de abordagem qualitativa, tendo como procedimento a revisão bibliográfica. O artigo aborda os principais modelos analíticos, expondo os avanços recentes da ciência comportamental, apresentando conceitos introdutórios sobre o julgamento e a tomada de decisão, a arquitetura de escolhas como uma técnica de desenho de políticas públicas capaz de incentivar melhores escolhas, bem como o método do experimento aleatório controlado como uma ferramenta de mensuração dos impactos. Ao final, expõe exemplos de insights comportamentais em políticas públicas. Com a escassez crescente de recursos públicos, é fundamental que os gestores se sensibilizem da necessidade de desenvolver ações eficientes e capazes de produzir resultados significativos na vida das pessoas, na materialização dos direitos sociais. Assim, o artigo pretende contribuir para a tomada de decisão, sob a ótica comportamental, destacando o uso de experimentos aleatórios controlados (EACs) como ferramenta para a tomada de decisão em políticas públicas.
BEHAVIORAL ECONOMICS E DIREITO DO CONSUMIDOR: NOVAS PERSPECTIVAS PARA O ENFRENTAMENTO DO SUPERENDIVIDAMENTO
Authors: Daura Samir Alves
Published: September 2018

O objetivo deste artigo é analisar a compatibilidade entre a Behavioral Economics e o Direito do Consumidor, especialmente no enfrentamento do superendividamento. Em primeiro lugar, o estudo procurou explicar a origem e o desenvolvimento da Economia Comportamental, notadamente a forma como a descoberta das heurísticas e dos vieses passaram a influenciar o pensamento econômico, com especial ênfase para as pesquisas de Daniel Kahneman e Amos Tvserky. Posteriormente, o estudo tratou do diálogo entre a Economia Comportamental e o Direito, denominado de Análise Econômico-Comportamental do Direito, assim como das novas perspectivas que essa abordagem interdisciplinar poderá propiciar ao campo jurídico. Foi conferida especial ênfase à valorização que a Economia Comportamental fornece ao princípio da vulnerabilidade, expressamente previsto pelo Código de Defesa do Consumidor no Brasil. Ainda, foram abordadas diversas heurísticas e vieses comumente relacionados ao problema do endividamento excessivo. Em conclusão, demonstrou-se que a Economia Comportamental poderá ser útil tanto para a identificação das causas do superendividamento, quanto para a elaboração de leis mais condizentes com a realidade e de políticas públicas mais eficientes. Adotou-se o método indutivo e a abordagem interdisciplinar. Finalmente, o presente artigo visou contribuir com os demais estudiosos que acreditam nos avanços que Economia Comportamental poderá propiciar ao Direito do Consumidor no Brasil.
DIVERGÊNCIAS DE PRINCÍPIO: ARGUMENTOS JURÍDICOS E MORAIS EM UM CENÁRIO DE DESACORDOS SOCIAIS
Authors: Oliveira André Matos de Almeida , Côrtes Pâmela De Rezende , Wykrota Leonardo Martins
Published: September 2018

O objetivo deste artigo é analisar possíveis problemas do uso de argumentos morais no direito, em especial numa pressuposição comum de teóricos: de que argumentos morais podem gerar convergência e consenso. Este pressuposto é o que chamaremos de “ideia de convergência”. Dividiremos nossa análise em algumas partes. Na primeira, mostraremos como teorias morais têm ganhado proeminência no direito atualmente, sendo defendidas e usadas em teorias filosóficas, manuais de doutrina e na jurisprudência. A seguir, mostraremos que uma parte central do uso de teorias morais no direito envolve a ideia de convergência. Na terceira parte do artigo, a mais substancial, passaremos a uma análise das evidências empíricas sobre os julgamentos morais humanos em áreas como a neurociência, a psicologia e a ciência política. Nossa revisão se focará em três linhas majoritárias de pesquisa: (i) estudos sobre correlatos neurais nas respostas a dilemas morais; (ii) estudos sobre polarização e influência afetiva do grupo na formação de ideologias; (iii) modelo social-intuicionista dos julgamentos morais humanos. Concluiremos que, da visão geral que se forma dessa análise, temos motivos para descartar, em muitos casos, a ideia de convergência, substituindo-a pela oposta, de que julgamentos morais tendem a criar divergências entre pessoas de grupos diferentes. Essa conclusão é especialmente relevante para teóricos e aplicadores do direito, já que uma das funções centrais das instituições jurídicas é resolver e evitar conflitos sociais. Esta função pode ser ameaçada com a aplicação de argumentos morais em casos controversos, o que justifica repensar o papel da moralidade no direito.
DESVIO DE CARÁTER OU SIMPLESMENTE HUMANO? ECONOMIA COMPORTAMENTAL APLICADA AO COMPORTAMENTO DESONESTO
Authors: Orghian Diana , Cabral Gabriel , Pinto Andre , Fontana Alessandra
Published: September 2018

Neste artigo, apresentamos uma abordagem de combate à corrupção e ao comportamento desonesto baseada na economia comportamental, psicologia cognitiva e psicologia social. A economia comportamental tem sido extensivamente aplicada à criação de políticas eficazes de alteração de comportamentos na área da saúde e educação, sendo, a aplicação ao combate à corrupção uma área ainda incipiente. Neste artigo, tendo por base uma revisão da literatura científica sobre o comportamento desonesto, demonstramos que as pessoas nem sempre agem de forma racional, coerente e previsível e nem sempre incorporam análises de custo-benefício nas suas decisões, tal como é previsto pelos modelos econômicos tradicionais. Também demonstramos que existem fatores inconscientes e mais sutis que podem estar contribuindo para o comportamento desonesto. Estes fatores são: i) a cognição humana e os vieses que afetam a tomada de decisão (racionalização motivada, licença moral, dissonância cognitiva etc.); ii) as características do contexto que nos podem influenciar em direção a determinado comportamento desonesto (ambiguidade nos procedimentos, falta de clareza, distância psicológica em relação ao dinheiro etc.); iii) os fatores sociais que decorrem da interação com as outras pessoas (reciprocidade, difusão da responsabilidade, normas sociais etc.). Uma vez conhecidos estes fatores, um rol de intervenções pode ser sugerido para conduzir as pessoas a agirem de forma mais honesta. Este artigo é direcionado a atores do setor público e privado interessados na temática do combate à corrupção e tem dois objetivos: i) descrever os fatores psicológicos que influenciam o comportamento desonesto e ii) apresentar soluções de fácil implementação e baixo custo (nudges), que podem ser integradas nas políticas de integridade e combate à corrupção.
A NEUROCIÊNCIA DA MORALIDADE NA TOMADA DE DECISÕES JURÍDICAS COMPLEXAS E NO DESENHO DE POLÍTICAS PÚBLICAS
Authors: Navarro Erik
Published: September 2018

O presente artigo trata da utilização da neurociência da moralidade (e do pragmatismo profundo, daí decalcado) como guia filosófico no desenho de políticas públicas. As matrizes teóricas aqui utilizadas são a economia do bem-estar, a análise econômica do direito e o utilitarismo. Relatando diversos experimentos neurocientíficos, demonstraremos como nosso cérebro incide sistematicamente em erros de julgamento que fazem crer na utilização preponderante de princípios de justiça no desenho de políticas públicas, em detrimento do aumento do bem-estar social. Inicialmente apresentaremos o novo campo filosófico conhecido como neurociência da moralidade. Após, descreveremos a forma dual de funcionamento do cérebro, demonstrando a origem de nossos instintos e sentimentos de justiça. Finalmente, revelaremos como o pragmatismo pode auxiliar no debate de questões difíceis para o desenho correto de políticas públicas, considerando a forma dual de funcionamento da mente, bem como os erros cognitivos a que estamos sujeitos, algo inédito no direito brasileiro. Concluiremos que teorias que conferem caráter preponderante a princípios de justiça nada mais são do que racionalizações de julgamentos intuitivos e imprecisos de nosso cérebro, de modo que devem ceder espaço para premissas utilitaristas no desenho de políticas públicas, que, por sua vez, precisam ser focadas exclusivamente no aumento do bem-estar social.
AS COMUNIDADES EPISTÊMICAS PENAIS E A PRODUÇÃO LEGISLATIVA EM MATÉRIA CRIMINAL
Authors: Enguéléguélé Stéphane Bobé
Published: September 2018

Ao redescobrir a riqueza da obra de Max Weber, os sociólogos do direito vão restituir ao direito sua característica de ficção operativa (de procedimento) no processo de regulação social. Esta renovação teórica conduz, em matéria penal, a um conjunto de estudos onde a produção normativa, tanto processual como de direito material, é descrita como uma oportunidade para a implantação de estratégias inéditas por atores, advogados e políticos profissionais, que submetem a norma penal à satisfação de interesses específicos. O conceito de “comunidade epistêmica penal” vai ajudar a avaliar a influência das ideias e conhecimentos sobre a definição da estratégia criminal e medir o potencial de ação de alguns atores do campo penal que, em várias configurações, imprimem à lei penal um estilo que se ajusta com as suas preferências teóricas.
NEUROIMAGIOLOGIA E AVALIAÇÃO DE RESPONSABILIDADE
Authors: Vincent Nicole A.
Published: September 2018

Poderiam evidências da neuroimagiologia nos ajudar a avaliar o grau de responsabilidade de uma pessoa por um crime que sabemos que ela cometeu? Este ensaio defende uma resposta afirmativa a essa questão. Uma série de típicas objeções a essa abordagem de alta tecnologia de avaliação da responsabilidade das pessoas é considerada e, depois, colocada de lado, mas também trago à luz e, em seguida, rejeito uma objeção original – uma objeção que somente é encontrada quando uma neuroimagem funcional (em vez de estrutural) é usada para aferir a responsabilidade das pessoas.
Organosulfates in Atlanta, Georgia: Anthropogenic influences on biogenic secondary organic aerosol formation
Authors: Hettiyadura Anusha Priyadarshani Silva , Al-Naiema Ibrahim M. , Hughes Dagen D. , Fang Ting , Stone Elizabeth A.
Published: September 2018

Abstract. This study examines the anthropogenic influence on biogenic organosulfate formation at an urban site in Atlanta, GA in the Southeastern United States. Organosulfates were analyzed in fine particulate matter (PM2.5) collected during August 2015 in Atlanta using hydrophilic interaction liquid chromatography (HILIC), tandem mass spectrometry (MS/MS), and high-resolution time-of-flight (ToF) mass spectrometry. By their MS/MS response, 32 major organosulfate species were identified, selected species were quantified, and other species were semi-quantified using surrogate standards. Organosulfates accounted for 16.5 % of PM2.5 organic carbon (OC). Isoprene-derived organosulfates were the most abundant, dominated by methyltetrol sulfate which accounted for 12.6 % of PM2.5 OC. Together, the isoprene-derived organosulfates accounted for the majority of the isoprene-derived secondary organic aerosols (SOA) that had been previously observed in Atlanta, but had not been identified at the molecular level. Other major species included seven monoterpene-derived organosulfates, five diesel and/or biodiesel-derived organosulfates, and three new organosulfates that are also expected to derive from isoprene. Organosulfate species and concentrations in Atlanta were compared to those in a rural forested site in Centreville, AL during summer 2013, which were also dominated by isoprene-derived organosulfates. In Atlanta, isoprene-derived organosulfate concentrations were two to six times higher and accounted for twice as much OC. The greatest enhancement in concentration was observed for 2-methylglyceric acid sulfate, a tracer for isoprene high-NOx SOA. The isoprene-derived organosulfates indicated a stronger influence of NOx in Atlanta compared to Centreville. Overall, these results suggest that SOA in the Southeastern US can be reduced by controlling NOx and SO2 emissions from fossil fuel combustion. This study gives insights into the major organosulfate species that should be targets for future measurements in urban environments and standard development.
Volunteered geographical information, crowdsourcing, citizen science and neogeography are not the same
Authors: Cooper Antony K. , Coetzee Serena , Kourie Derrick G.
Published: September 2018

Abstract. The terms user-generated content, volunteered geographical information, crowdsourcing, citizen science and neogeography are elucidated. The purpose is to expose the range of meanings associated with these terms, and to promote consensus as to their scope and meaning, thereby enhancing the clarity with which discourse around these phenomena may occur, both in the research and popular context.
DEVELOPMENT OF A PORTABLE HIGH PERFORMANCE MOBILE MAPPING SYSTEM USING THE ROBOT OPERATING SYSTEM
Authors: Blaser S. , Cavegn S. , Nebiker S.
Published: September 2018

Abstract. The rapid progression in digitalization in the construction industry and in facility management creates an enormous demand for the efficient and accurate reality capturing of indoor spaces. Cloud-based services based on georeferenced metric 3D imagery are already extensively used for infrastructure management in outdoor environments. The goal of our research is to enable such services for indoor applications as well. For this purpose, we designed a portable mobile mapping research platform with a strong focus on acquiring accurate 3D imagery. Our system consists of a multi-head panorama camera in combination with two multi-profile LiDAR scanners and a MEMS-based industrial grade IMU for LiDAR-based online and offline SLAM. Our modular implementation based on the Robot Operating System enables rapid adaptations of the sensor configuration and the acquisition software. The developed workflow provides for completely GNSS-independent data acquisition and camera pose estimation using LiDAR-based SLAM. Furthermore, we apply a novel image-based georeferencing approach for further improving camera poses. First performance evaluations show an improvement from LiDAR-based SLAM to image-based georeferencing by an order of magnitude: from 10–13 cm to 1.3–1.8 cm in absolute 3D point accuracy and from 8–12 cm to sub-centimeter in relative 3D point accuracy.
COPERNICUS SENTINEL-2 DATA FOR THE DETERMINATION OF GROUNDWATER WITHDRAWAL IN THE MAGHREB REGION
Authors: Dubois C. , Stoffner F. , Kalia A. C. , Sandner M. , Labiadh M. , Mimouni M.
Published: September 2018

Abstract. Agriculture plays an important role in the economy of the Maghreb region. Most of the water needed for irrigation comes from pumping of the aquifers. A controlled pumping of the groundwater resources does not exist yet, thus, estimating the total water consumption for agricultural use only with in situ data is nearly impossible. In order to overcome this lack of information, Copernicus data are used for determining the groundwater withdrawal through agriculture in the Maghreb region. This paper presents an approach for estimating and monitoring crop water requirements in Tunisia based on multitemporal Sentinel-2 data. Using this multitemporal information, a thorough analysis of the different culture types over time is possible, from which a set of additional multitemporal features is deduced for crop type classification. In this paper, the contribution of those features is analyzed, showing a classification accuracy enhanced by 10 % with the multitemporal features. Furthermore, relying on existing methods and FAO standards for the estimation of crop water needs, the methodology aims to estimate the specific crop water consumption. The results of the water estimates are validated against delimited areas where estimates of the water consumption are available from the authorities. Finally, as the study is conducted within the framework of an international technical cooperation, the methodology aims to be reproducible and sustainable for local authorities. The particularity of the results presented here is that they are achieved through automatic processing and using exclusively Open Source solutions, deployable on simple workstations.
TERRAIN AWARE IMAGE CLIPPING FOR REAL-TIME AERIAL MAPPING
Authors: Hein D. , Berger R.
Published: September 2018

Abstract. Many remote sensing applications demand for a fast and efficient way of generating orthophoto maps from raw aerial images. One prerequisite is direct georeferencing, which allows to geolocate aerial images to their geographic position on the earth’s surface. But this is only half the story. When dealing with a large quantity of highly overlapping images, a major challenge is to select the most suitable image parts in order to generate seamless aerial maps of the captured area. This paper proposes a method that quickly determines such an optimal (rectangular) section for each single aerial image, which in turn can be used for generating seamless aerial maps. Its key approach is to clip aerial images depending on their geometric intersections with a terrain elevation model of the captured area, which is why we call it terrain aware image clipping (TAC). The method has a modest computational footprint and is therefore applicable even for rather limited embedded vision systems. It can be applied for both, real-time aerial mapping applications using data links as well as for rapid map generation right after landing without any postprocessing step. Referring to real-time applications, this method also minimizes transmission of redundant image data. The proposed method has already been demonstrated in several search-and-rescue scenarios and real-time mapping applications using a broadband data link and diffent kinds of camera and carrier systems. Moreover, a patent for this technology is pending.
AN EVALUATION PIPELINE FOR INDOOR LASER SCANNING POINT CLOUDS
Authors: Karam S. , Peter M. , Hosseinyalamdary S. , Vosselman G.
Published: September 2018

Abstract. The necessity for the modelling of building interiors has encouraged researchers in recent years to focus on improving the capturing and modelling techniques for such environments. State-of-the-art indoor mobile mapping systems use a combination of laser scanners and/or cameras mounted on movable platforms and allow for capturing 3D data of buildings’ interiors. As GNSS positioning does not work inside buildings, the extensively investigated Simultaneous Localisation and Mapping (SLAM) algorithms seem to offer a suitable solution for the problem. Because of the dead-reckoning nature of SLAM approaches, their results usually suffer from registration errors. Therefore, indoor data acquisition has remained a challenge and the accuracy of the captured data has to be analysed and investigated. In this paper, we propose to use architectural constraints to partly evaluate the quality of the acquired point cloud in the absence of any ground truth model. The internal consistency of walls is utilized to check the accuracy and correctness of indoor models. In addition, we use a floor plan (if available) as an external information source to check the quality of the generated indoor model. The proposed evaluation method provides an overall impression of the reconstruction accuracy. Our results show that perpendicularity, parallelism, and thickness of walls are important cues in buildings and can be used for an internal consistency check.
MODELLING ERRORS IN X-RAY FLUOROSCOPIC IMAGING SYSTEMS USING PHOTOGRAMMETRIC BUNDLE ADJUSTMENT WITH A DATA-DRIVEN SELF-CALIBRATION APPROACH
Authors: Chow J. C. K. , Lichti D. D. , Ang K. D. , Al-Durgham K. , Kuntze G. , Sharma G. , Ronsky J.
Published: September 2018

Abstract. X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluoroscopic imaging can further acquire X-ray images at video frame rates, thus enabling non-invasive in-vivo motion studies of joints, gastrointestinal tract, etc. For both the qualitative and quantitative analysis of static and dynamic X-ray images, the data should be free of systematic biases. Besides precise fabrication of hardware, software-based calibration solutions are commonly used for modelling the distortions. In this primary research study, a robust photogrammetric bundle adjustment was used to model the projective geometry of two fluoroscopic X-ray imaging systems. However, instead of relying on an expert photogrammetrist’s knowledge and judgement to decide on a parametric model for describing the systematic errors, a self-tuning data-driven approach is used to model the complex non-linear distortion profile of the sensors. Quality control from the experiment showed that 0.06 mm to 0.09 mm 3D reconstruction accuracy was achievable post-calibration using merely 15 X-ray images. As part of the bundle adjustment, the location of the virtual fluoroscopic system relative to the target field can also be spatially resected with an RMSE between 3.10 mm and 3.31 mm.
GLOBAL REFINEMENT OF TERRESTRIAL LASER SCANNER DATA REGISTRATION USING WEIGHTED SENSOR POSES
Authors: dos Santos D. R. , Freiman F. P. , Pavan N. L.
Published: September 2018

Abstract. Terrestrial laser scanner (TLS) sensor captures highly dense and accurate point clouds quite useful for indoor and outdoor mapping, navigation, 3D reconstruction, surveillance, industrial projects, infrastructure management, and others. In this paper, we present a global registration method that weights the sensor poses for refinement of TLS data registration. Our global refinement method assumes that the variance-covariance matrix that describes the uncertainty of sensor poses is available to refine the registration errors. The effectiveness of the proposed method is demonstrated with TLS dataset obtained into outdoor environment. Our results show that the weighting the sensor poses obtained in registration task improves the positional accuracy of TLS sensor.
BUILDING BOUNDARY EXTRACTION FROM LIDAR DATA USING A LOCAL ESTIMATED PARAMETER FOR ALPHA SHAPE ALGORITHM
Authors: dos Santos R. C. , Galo M. , Carrilho A. C.
Published: September 2018

Abstract. The α-shape algorithm is a very common option to extract building boundaries from LiDAR data. This algorithm is normally executed in 2D space considering a parameter α as a binary classifier which controls the distinctiveness of points whether or not they belong to the object boundary. For point cloud data, this parameter is directly related to the local point density and the level of detail of building boundaries. Studies that have explored this concept usually consider a unique parameter α to extract all buildings in the dataset. However, the point density can have a considerable variation along the point cloud and, in this case, the use a global parameter may not be the best choice. Alternatively, this paper proposes a data-driven method that estimates a local parameter for each building. The method evaluation considered six test areas with different levels of complexity, selected from a LiDAR dataset acquired over the city of Presidente Prudente/Brazil. From the qualitative and quantitative analysis, it could be seen that the proposed method generated better results than when a global parameter is used. The proposed method was also able to withstand density variation among the LiDAR data, having a positional accuracy around 0.22 m, against 0.40 m of global parameter.
DEEP LEARNING AND ANTHROPOMETRIC PLANE BASED WORKFLOW MONITORING BY DETECTING AND TRACKING WORKERS
Authors: Gard N. A. , Chen J. , Tang P. , Yilmaz A.
Published: September 2018

Abstract. The worker productivity, a critical variable in project management, significantly affects the progress of a project. The key to measuring productivity is analysis of activities, which provides necessary information by identifying how workers spend their time at certain areas in the site. In this work, we propose a novel joint image-trajectory space for automatic detection and tracking of workers using a single fixed camera. A two-branch convolutional neural network detects workers and their body joints. Instead of tracking the body joints in the image space, we transform detected joints onto virtual parallel planes called “Anthropometric Planes”. The detected joints are, then, tracked using a Kalman Filter on these planes which are created based on anthropometric measures of an average American male. Finally, an uncertainty measure is introduced to reduce the number of identity changes and to handle missing joints. The experiments conducted on an image sequence captured in a nuclear plant shows promising detection and tracking results.
FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING
Authors: Gašparović M. , Medak D. , Pilaš I. , Jurjević L. , Balenović I.
Published: September 2018

Abstract. Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.
DWELLING EXTRACTION IN REFUGEE CAMPS USING CNN – FIRST EXPERIENCES AND LESSONS LEARNT
Authors: Ghorbanzadeh O. , Tiede D. , Dabiri Z. , Sudmanns M. , Lang S.
Published: September 2018

Abstract. There is a growing use of Earth observation (EO) data for support planning in humanitarian crisis response. Information about number and dynamics of displaced population in camps is essential to humanitarian organizations for decision-making and action planning. Dwelling extraction and categorisation is a challenging task, due to the problems in separating different dwellings under different conditions, with wide range of sizes, colour and complex spatial patterns. Nowadays, so-called deep learning techniques such as deep convolutional neural network (CNN) are used for understanding image content and object recognition. Although recent developments in the field of computer vision have introduced CNN networks as a practical tool also in the field of remote sensing, the training step of these techniques is rather time-consuming and samples for the training process are rarely transferable to other application fields. These techniques also have not been fully explored for mapping camps. Our study analyses the potential of a CNN network for dwelling extraction to be embedded as initial step in a comprehensive object-based image analysis (OBIA) workflow. The results were compared to a semi-automated, i.e. combined knowledge-/sample-based, OBIA classification. The Minawao refugee camp in Cameroon served as a case study due to its well-organised, clearly distinguishable dwelling structure. We use manually delineated objects as initial input for the training samples, while the CNN network is structured with two convolution layers and one max pooling.
MOBILE MAPPING SYSTEM BASED ON ACTION CAMERAS
Authors: Gonçalves J. A. , Pinhal A.
Published: September 2018

Abstract. Action cameras can operate in outdoor conditions, such as outside a car, and provide good quality imagery that can be exploited to collect geospatial data by photogrammetric means. Recent models include GPS, which can deliver position and time of individual images and video frames. That is the case of the very popular camera, Gopro Hero 5. This paper describes the implementation of a mobile mapping system, based on a GoPro Hero 5 camera mounted on the side rearview mirror of a car. Although the system can be dependent on the camera GPS positions only, it was developed to include a GNSS dual frequency receiver, carried inside the car, on the dashboard. Within good observation conditions, without tall buildings, differential positioning (either RTK or PPK) provides the trajectory with accuracy of a few centimetres. The precise time of individual frames is obtained from the camera GPS and positions are interpolated from the GNSS receiver. Assuming the car moves in a horizontal plane and the camera has no significant tilts, the system is treated in planimetric terms, with camera axis azimuth derived from the vehicle trajectory. Positions of observed objects, such as traffic signs, are derived from consecutive frames. Tests carried out in a sparse urban environment have shown planimetric accuracy better than 40 cm, appropriate for large scale mapping, such as 1 : 2000. The system can be improved in several forms, through processing techniques, such as structure from motion, but without the incorporation of additional hardware.
GLIBERTY-GENERALIZED MODEL FOR SPECTRAL CHARACTERISTIC OF CONIFEROUS LEAVES AND BROAD-LEAVED LEAVES
Authors: Guo Y. , An G. , Feng C. , Xie Q. , Li J. , Zhou F.
Published: September 2018

Abstract. The application of different leaf physical models will result in the low accuracy in the process of the vegetation information inversion of the expressway, as well as poor efficiency in the process. The GLIBERTY model attempts to simulate the leaf reflectance and transmittance by the use of hierarchical computing theory, through the analysis of the optical properties of the single cell and the introduction of monolayer cells and cells of infinite layer between the reflectance and transmittance mathematical relationship, based on the radiative transfer theory. The study established the completed GLIBERTY model calculation theory system after the impaction of leaf structure parameters. The results show that the GLIBERTY model can accurately simulate the spectrum information of broadleaf and needle even in the case of less input parameters.
AN EFFICIENT WEED DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM FOR PRECISION AGRICULTURE APPLICATIONS
Authors: Hassanein M. , El-Sheimy N.
Published: September 2018

Abstract. The use of Unmanned Aerial Vehicle (UAV) imagery systems for Precision Agriculture (PA) applications drew a lot of attention through the last decade. UAV as a platform for an imagery sensor is providing a major advantage as it can provide high spatial resolution images compared to satellite platform. Moreover, it provides the user with the ability to collect the needed images at any time along with the ability to cover the agriculture fields faster than terrestrial platform. Therefore, such UAV imagery systems are capable to fit the gap between aerial and terrestrial Remote Sensing. One of the important PA applications that using UAV imagery system for it showed great potentials is weed management and more specifically the weed detection step. The current weed management procedure depends on spraying the whole agriculture field with chemical herbicides to execute any weed plants in the field. Although such procedure seems to be effective, it has huge effect on the surrounding environment due to the excessive use of the chemical, especially that weed plants don’t cover the whole field. Usually weed plants spread through only few spots of the field. Therefore, different efforts were introduced to develop weed detection techniques using UAV imagery systems. Though the different advantages of the UAV imagery systems, they systems didn’t draw the users interest due to many limitations including the cost of the system. Therefore, the proposed paper introduces a new weed detection methodology from RGB images acquired by low-cost UAV imagery system. The proposed methodology adopts detecting the high-density vegetation spots as indication for weed patches spots. The achieved results showed the potential of the proposed methodology to use low-cost UAV imagery system equipped with low-cost RGB imagery sensor for detecting weed patches in different cropped agriculture fields even from different flight height as 20, 40, 80, and 120 meters.
FUSION OF 3D POINT CLOUDS WITH TIR IMAGES FOR INDOOR SCENE RECONSTRUCTION
Authors: Hoegner L. , Abmayr T. , Tosic D. , Turzer S. , Stilla U.
Published: September 2018

Abstract. Obtaining accurate 3D descriptions in the thermal infrared (TIR) is a quite challenging task due to the low geometric resolutions of TIR cameras and the low number of strong features in TIR images. Combining the radiometric information of the thermal infrared with 3D data from another sensor is able to overcome most of the limitations in the 3D geometric accuracy. In case of dynamic scenes with moving objects or a moving sensor system, a combination with RGB cameras and profile laserscanners is suitable. As a laserscanner is an active sensor in the visible red or near infrared (NIR) and the thermal infrared camera captures the radiation emitted by the objects in the observed scene, the combination of these two sensors for close range applications are independent from external illumination or textures in the scene. This contribution focusses on the fusion of point clouds from terrestrial laserscanners and RGB cameras with images from thermal infrared mounted together on a robot for indoor 3D reconstruction. The system is geometrical calibrated including the lever arm between the different sensors. As the field of view is different for the sensors, the different sensors record the same scene points not exactly at the same time. Thus, the 3D scene points of the laserscanner and the photogrammetric point cloud from the RGB camera have to be synchronized before point cloud fusion and adding the thermal channel to the 3D points.
SEGMENT-AND-COUNT: VEHICLE COUNTING IN AERIAL IMAGERY USING ATROUS CONVOLUTIONAL NEURAL NETWORKS
Authors: Azimi S. , Vig E. , Kurz F. , Reinartz P.
Published: September 2018

Abstract. High-resolution aerial imagery can provide detailed and in some cases even real-time information about traffic related objects. Vehicle localization and counting using aerial imagery play an important role in a broad range of applications. Recently, convolutional neural networks (CNNs) with atrous convolution layers have shown better performance for semantic segmentation compared to conventional convolutional aproaches. In this work, we propose a joint vehicle segmentation and counting method based on atrous convolutional layers. This method uses a multi-task loss function to simultaneously reduce pixel-wise segmentation and vehicle counting errors. In addition, the rectangular shapes of vehicle segmentations are refined using morphological operations. In order to evaluate the proposed methodology, we apply it to the public “DLR 3K” benchmark dataset which contains aerial images with a ground sampling distance of 13 cm. Results show that our proposed method reaches 81.58 % mean intersection over union in vehicle segmentation and shows an accuracy of 91.12 % in vehicle counting, outperforming the baselines.
LOCALIZATION OF A CAR BASED ON MULTI-SENSOR FUSION
Authors: Kim H. , Lee I.
Published: September 2018

Abstract. The vehicle localization is an essential component for stable autonomous car operation. There are many algorithms for the vehicle localization. However, it still needs much improvement in terms of its accuracy and cost. In this paper, sensor fusion based localization algorithm is used for solving this problem. Our sensor system is composed of in-vehicle sensors, GPS and vision sensors. The localization algorithm is based on extended Kalman filter and it has time update step and measurement update step. In the time update step, in-vehicle sensors are used such as yaw-rate and speed sensor. And GPS and vision sensor information are used to update the vehicle position in the measurement update step.We use visual odometry library to process vision sensor data and generate the moving distance and direction of the car. Especially, when performing visual odometry we use georeferenced image database to reduce the error accumulation. Through the experiments, the proposed localization algorithm is verified and evaluated. The RMS errors of the estimated result from the proposed algorithm are about 4.3 m. This result shows about 40 % improvement in accuracy even in comparison with the result from the GPS only method. It shows the possibility to use proposed localization algorithm. However, it is still necessary to improve the accuracy for applying this algorithm to the autonomous car. Therefore, we plan to use multiple cameras (rear cameras or AVM cameras) and more information such as high-definition map or V2X communication. And the filter and error modelling also need to be changed for the better results.
DLRAD – A FIRST LOOK ON THE NEW VISION AND MAPPING BENCHMARK DATASET FOR AUTONOMOUS DRIVING
Authors: Kurz F. , Waigand D. , Pekezou-Fouopi P. , Vig E. , Henry C. , Merkle N. , Rosenbaum D. , Gstaiger V. , Azimi S. , Auer S. , Reinartz P. , Knake-Langhorst S.
Published: September 2018

Abstract. DLRAD – a new vision and mapping benchmark dataset for autonomous driving is under development for the validation of intelligent driving algorithms. Stationary, mobile, and airborne sensors monitored simultaneously the environment around a reference vehicle, which was driving on urban, suburb and rural roads in and around the city of Braunschweig/Germany. Airborne images were acquired with the DLR 4k sensor system mounted on a helicopter. The DLR research car FASCarE is equipped with the latest sensor technology like front/rear radar, ultrasound and laser sensors, optical single and stereo cameras, and GNSS/IMU. Additionally, stationary terrestrial sensors like induction loops, optical mono and stereo cameras, radar and laser scanners monitor defined sections of the path from the ground. Simultaneously, the helicopter with the 4k sensor systems follows the reference car by keeping it all the time in the central nadir view. A next crucial step in the construction of the DLRAD benchmark dataset is the annotation of all objects in the reference dataset.The DLRAD benchmark dataset enables a huge variety of validation capabilities and opens a wide field of possibilities for the development, training and validation of machine learning algorithms in the context of autonomous driving. In this paper, we will present details of the sensor configurations and the acquisition campaign, which had taken place between the 18th July and 20th July 2017 in Braunschweig/Germany. Also, we show a first analysis of the data including the completeness and geometrical quality. The dataset will be published as soon as the coregistration and annotations are complete.
INITIAL EVALUATION OF 3D RECONSTRUCTION OF CLOSE OBJECTS WITH SMARTPHONE STEREO VISION
Authors: Masiero A. , Fissore F. , Piragnolo M. , Guarnieri A. , Pirotti F. , Vettore A.
Published: September 2018

Abstract. The Worldwide spread of relatively low cost mobile devices embedded with dual rear cameras enables the possibility of exploiting smartphone stereo vision for producing 3D models. Despite such idea is quite attractive, the small baseline between the two cameras restricts the depth discrimination ability of this kind of stereo vision systems. This paper presents the results obtained with a smartphone stereo vision system by using two rear cameras with different focal length: this operating condition clearly reduces the matchable area. Nevertheless, 3D reconstruction is still possible and the obtained results are evaluated for several camera-object distances.
TOWARDS STANDARDIZED EVALUATION OF IMAGE QUALITY FOR AIRBORNE CAMERA SYSTEMS
Authors: Meißner H. , Cramer M. , Reulke R.
Published: September 2018

Abstract. Digital airborne camera systems and their high geometric resolution demand for new algorithms and procedures of image data analysis and interpretation. Parameters describing image quality are necessary for various fields of application (e.g. sensor and mission design, sensor comparison, algorithm development, in-orbit-behaviour of instruments). The effective sensor resolution is one important parameter which comprehensively estimates the optical quality of a given imaging sensor-lens combination. Although determination of resolving power is a well-studied field of research, there are still some scientific questions to be answered when it comes to a standardized (eventually absolute) determination. This is also research object of a committee of the “German Institute for Standardization” and the given contribution outlines the current state of investigation concerning effective resolving power for airborne camera systems. Therefore an approach using signal processing techniques to calculate the effective image resolution will be described. The open scientific issues will be introduced, explained and answered to some extend.
REAL-TIME ON-BOARD OBSTACLE AVOIDANCE FOR UAVS BASED ON EMBEDDED STEREO VISION
Authors: Ruf B. , Monka S. , Kollmann M. , Grinberg M.
Published: September 2018

Abstract. In order to improve usability and safety, modern unmanned aerial vehicles (UAVs) are equipped with sensors to monitor the environment, such as laser-scanners and cameras. One important aspect in this monitoring process is to detect obstacles in the flight path in order to avoid collisions. Since a large number of consumer UAVs suffer from tight weight and power constraints, our work focuses on obstacle avoidance based on a lightweight stereo camera setup. We use disparity maps, which are computed from the camera images, to locate obstacles and to automatically steer the UAV around them. For disparity map computation we optimize the well-known semi-global matching (SGM) approach for the deployment on an embedded FPGA. The disparity maps are then converted into simpler representations, the so called U-/V-Maps, which are used for obstacle detection. Obstacle avoidance is based on a reactive approach which finds the shortest path around the obstacles as soon as they have a critical distance to the UAV. One of the fundamental goals of our work was the reduction of development costs by closing the gap between application development and hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for porting our algorithms, which are written in C/C++, to the embedded FPGA. We evaluated our implementation of the disparity estimation on the KITTI Stereo 2015 benchmark. The integrity of the overall realtime reactive obstacle avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in conjunction with two flight simulators.
EVALUATION OF DYNAMIC AD-HOC UWB INDOOR POSITIONING SYSTEM
Authors: Sakr M. , Masiero A. , El-Sheimy N.
Published: September 2018

Abstract. Ultra-wideband (UWB) technology has witnessed tremendous development and advancement in the past few years. Currently available UWB transceivers can provide high-precision time-of-flight measurements which corresponds to range measurements with theoretical accuracy of few centimetres. Position estimation using range measurement is determined by measuring the ranges from a rover or a dynamic node, to a set of anchor points with known positions. However, building a flexible and accurate indoor positioning system requires more than just accurate range measurements. The performance of indoor positioning system is affected by the number and the configuration of the anchor points used, along with the accuracy of the anchor positions.This paper introduces LocSpeck, a dynamic ad-hoc positioning system based on the DW1000 UWB transceiver from Decawave. LocSpeck is composed of a set of identical nodes communicating on a common RF channel, forming a fully or partially connected network where the positioning algorithm run on each node. Each LocSpeck node could act as an anchor or a rover, and the role could change dynamically during the same session. The number of nodes in the network could change dynamically, since the firmware of LocSpeck supports adding and removing nodes on-the-fly. The paper compares the performance of the LocSpeck system with commercially available off-the-shelf UWB positioning system. Different operating scenarios are considered when evaluating the performance of the system, including cases where collaboration between the two systems is considered.