Background: Settlements induced by tunneling in inner urban areas can easily damage above ground structures. This already has to be considered in early planning of tunneling routes. Assessing the risk of damages to structures on hypothetical tunneling routes inflicted by such settlements beforehand enables routes' comparability. Hereby, it facilitates the choice of the optimal tunneling route in terms of potential damages and of suitable countermeasures. Risk analyses of structures establishing the assessment obtain relevant data from various sources. Some data even has to be gathered manually. Virtual building models could ease this process and facilitate analyses for entire districts as they combine several required information in a single data set. Commonly, these are yet modelled very coarse. Relevant details like facade openings, which highly affect a structures stiffness, are not included. Methods: In this paper, we propose a system which detects windows in facade images. This is used to subsequently enrich existing virtual building models allowing for a precise risk assessment. For this, we apply a sliding window detector which employs a cascaded classifier to obtain windows in images patches. Results: Our system yields sufficient results on facade images of several countries showing its general applicability despite regional and architectural variation in the facades' and windows' appearance. In an ensuing case study, we assess the risk of damages to structures based on detections of our system using different analysis methods. Conclusions: We contrast these results to assessments using manually gathered data. Hereby, we show that the detection rate of our proposed system is sufficient for a reliable estimation of a structure's damage class.
Developing an Internet of Things (IoT) system requires knowledge in many different technologies like embedded programming, web technologies, and data science. Model-Driven Engineering (MDE) techniques have been used as a concrete alternative to boost IoT application development. However, the current MDE-to-IoT solutions require expertise from the end-users in MDE concepts and sometimes even in specific tools, such as the Eclipse Modelling Framework, which may hinder their adoption in a broader context. To tackle this problem, this work proposes AutoIoT, a framework for creating IoT applications based on a user-driven MDE approach. The proposed framework allows users to model their IoT systems using a simple JSON file and, through internal model-to-model and model-to-text transformations, generates a ready-to-use IoT serverside application. The proposed approach was evaluated through an experiment, in which 54 developers used AutoIoT to create a server-side application for a real-world IoT scenario and answered a post-study questionnaire. The experiment reports the efficacy of AutoIoT and user satisfaction of more than 80% through 6 out of 7 evaluated criteria.
-A major part of recent developments in civil engineering in the urban context evolved around building and city models. Especially for a precise risk assessment of damages to existing buildings induced by ground movements, accurate models are inevitable. Beside the shape of a building, the focus is also on components compromising a building's stiffness. Particularly, by including wall openings such as windows into risk analyses, these can be improved to provide more reliable predictions. However, most publicly available data sources only provide simple block models of existing buildings sometimes extended by roof shapes. As a consequence, any information concerning the windows of a building must be integrated into the model using other data sources. Whereas numerous approaches address the refinement of building shapes, their windows and other components are commonly disregarded. Although cascaded classifiers already turned out to yield good results in general and applying them to window detection seems promising, such approaches are yet insufficient to reliably extend building models. Drawing on previous findings, we present an approach to window detection in facade images satisfying the needs of risk assessment analyses. Our detection system combines a soft cascaded classifier consisting of thresholded Haar-like features with a sliding window detector extracting image patches for classification. The soft cascaded design improves the detection rate over previously made approaches while coincidentally reducing the amount of required features. Further, we evaluate the effect of a rectified dataset on the classification results compared to its counterpart with images taken from varying angles.
Die zunehmende Digitalisierung und Mediatisierung der Lebenswelt sind auf gesellschaftlicher und individueller Ebene mit grundlegenden und unausweichlichen Wandlungsprozessen verbunden. Somit stellt sich die Frage, welche Aufgaben Erziehung und Bildung unter diesen Rahmenbedingungen zukommen. Bildungspolitisch besteht Konsens darüber, dass Schulen bei der Vermittlung von Medienkompetenz eine wichtige Rolle spielen (müssen). Die konkrete Umsetzung soll dabei nicht in einem isolierten Lernbereich, sondern in der Verantwortung aller (Unterrichts-)Fächer liegen. Setzt man bildungspolitisch auf eine integrative Realisierung von Medienbildung, d.h. ohne einen eigenen Lernbereich oder ein eigenes Fach, so bedeutet dies, dass entsprechend abgestimmte schulische Lerngelegenheiten geschaffen werden müssen, die in der Summe der Beiträge aller Unterrichtsfächer einen umfassenden Kompetenzerwerb ermöglichen. Rahmenbedingungen, Vorgaben und Voraussetzungen für eine erfolgreiche Bewältigung dieser Aufgabe werden im Beitrag sowohl aus medienpädagogischer als auch aus fachdidaktischer Perspektive aufgegriffen und mit Blick auf Chancen und Herausforderungen beleuchtet.
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