On the Importance of 3D Surface Information for Remote Sensing Classification Tasks

There has been a surge in remote sensing machine learning applications that operate on data from active or passive sensors as well as multi-sensor combinations (Ma et al. (2019)). Despite this surge, however, there has been relatively little study on the comparative value of 3D surface information for machine learning classification tasks. Adding 3D surface information to RGB imagery can provide crucial geometric information for semantic classes such as buildings, and can thus improve out-of-sample predictive performance. In this paper, we examine in-sample and out-of-sample classification performance of Fully Convolutional Neural Networks (FCNNs) and Support Vector Machines (SVMs) trained with and without 3D normalized digital surface model (nDSM) information. We assess classification performance using multispectral imagery from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D Semantic Labeling contest and the United States Special Operations Command (USSOCOM) Urban 3D Challenge. We find that providing RGB classifiers with additional 3D nDSM information results in little increase in in-sample classification performance, suggesting that spectral information alone may be sufficient for the given classification tasks. However, we observe that providing these RGB classifiers with additional nDSM information leads to significant gains in out-of-sample predictive performance. Specifically, we observe an average improvement in out-of-sample all-class accuracy of 14.4% on the ISPRS dataset and an average improvement in out-of-sample F1 score of 8.6% on the USSOCOM dataset. In addition, the experiments establish that nDSM information is critical in machine learning and classification settings that face training sample scarcity

Zur Relevanz von Online-Intermediären für die Meinungsbildung

Suchmaschinen, Netzwerk- und Multimediaplattformen sowie Instant-Messaging-Dienste sind wesentliche Elemente des Kommunikations- und Informationsverhaltens online. Sie fungieren als Intermediäre, weil sie ihren Nutzerinnen und Nutzern Inhalte aus zahlreichen unterschiedlichen Quellen erschließen, unter Zuhilfenahme von Algorithmen filtern und zu personalisierten Informationsangeboten bündeln. Aus Nutzersicht unterstützen sie neben dem Informationsmanagement im engeren Sinne auch weitergehende Praktiken des Identitäts- und Beziehungsmanagements. Online-Intermediäre tragen damit wesentlich zum gegenwärtig beobachtbaren Wandel von Öffentlichkeit bei, doch die resultierenden Folgen für die Meinungsbildung zu gesellschaftlich relevanten Themen sind bislang nicht hinreichend untersucht und verstanden. Im Auftrag der Medienanstalten wurde eine qualitative Studie durchgeführt, die die Relevanz von Intermediären sowie die mit ihnen verbundenen Praktiken der informationsorientierten Nutzung ermitteln sollte

How Do Intermediaries Shape News-Related Media Repertoires and Practices? : Findings From a Qualitative Study

Online intermediaries such as search engines, social network sites, or video platforms provide access to diverse content; however, there is a school of thought that argues that they may also contribute to the structural deformation of the public sphere. To assess the impact of these Web-based services, research needs to address them not as isolated platforms but as part of broader media environments. Based on 6 group discussions and 18 interviews with German participants varying in age and political engagement, we mapped individual information repertoires with a particular focus on online intermediaries, reconstructed key episodes in which these services were used for gathering information on current news events, and investigated participants’ awareness of the architecture and mechanisms of these intermediaries. Findings show that for most participants, online intermediaries are an indispensable part of their media repertoires, but are seldom dominant, let alone the only source of information on political topics. Most respondents possessed some knowledge on the basic workings of the intermediaries they used, but were not familiar with details such as algorithmic personalization.publishe

Machine Learning for Defect Detection for PBFAM Using High Resolution Layerwise Imaging Coupled with Post-Build CT Scans

This paper develops a methodology based on machine learning to detect defects during Powder Bed Fusion Additive Manufacturing (PBFAM) processes using data from high resolution images. The methodology is validated experimentally using both a support vector machine (SVM) and a neural network (NN) for binary classification. High resolution images are collected each layer of the build, and the ground truth labels necessary for supervised machine learning are obtained from a 3D computed tomography (CT) scan. CT data is processed using image processing tools—extended to 3D—in order to extract xyz position of voids within the component. Anomaly locations are subsequently transferred from the CT domain into the image domain using an affine transformation. Multi-dimensional features are extracted from the images using data surrounding both anomaly and nominal locations. Using cross-validation strategies for machine learning and testing, accuracies of close to 90% could be achieved when using a neural network for in-situ anomaly detection.Mechanical Engineerin

PROTOTYPE OF A LINE-FOCUS X-RAY TUBE (LFXT) AS COMPACT SOURCE FOR MICROBEAM AND FLASH RADIOTHERAPY

Background: Microbeam (MRT) and x-ray FLASH radiotherapy have shown superior healthy tissue tolerance at tumour control rates comparable to those of conventional radiotherapy. As these methods place high demands on the radiation quality, most research and all possible clinical applications are currently limited to large third-generation synchrotrons, impeding a translation into clinical routine. Compact and less complex alternatives to synchrotrons are required to facilitate a translation of both techniques into clinical routine. The line focus x-ray tube (LFxT) is a high-power rotating-target x-ray tube that focuses an extremely asymmetric electron beam onto a rapidly rotating target – thus shifting operation into the heat-capacity limit and enabling the generation of so far unachievable dose rates at small focal spot sizes. The LFxT is a promising technique for hospital based MRT and x-ray FLASH therapy, but also advanced x-ray imaging techniques. Methods: We developed a first preclinical prototype of the LFxT designed for an electron beam current of 0.3 A at 300 kV acceleration voltage. To achieve this, we developed a low emittance electron source featuring a highly asymmetric focal spot and a target-rotor system allowing for surface velocities above 130 m/s, while being able to withstand the mechanical stresses produced by the rotational force and high thermal gradients. The radiological and thermal properties of the x-ray source were assessed in numerical simulations. A system of temperature and radiation detectors can monitor and validate the performance of the source experimentally. A powerful cooling system dissipates the enormous amounts of excess heat. We tackle the specific high-voltage and radiation risks with a bespoken shielding, safety and control system. Results: Simulations have shown a 50 μm wide and 20 mm long focal spot achieving a dose rate of 10 Gy/s at 20 cm from the focal spot. At maximum power, a duty-cycle of 20 s irradiation, during which the focal spot temperature exceeds 2000 °C, followed by 20 min cooling is reached. Using a NIR-camera and an x-ray detector, the focal spot will be measured with a resolution down to 5 μm. Conclusion: The prototype aims to prove the LFxT concept and validate the heat-capacity limit. Additionally, preclinical studies for MRT will be conducted and its imaging capabilities will be explored. In a next step, we will upgrade to a clinical prototype for an electron beam power of 1.5 MW at an acceleration voltage of 600 kV, achieving dose rates of more than 100 Gy/s