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Building semantic segmentation is a crucial task for building information modeling (BIM). Current research generally exploits terrestrial image data, which provides only limited view of a building. By contrast, oblique imagery acquired by unmanned aerial vehicle (UAV) can provide richer information of both the building and its surroundings at a larger scale. In this paper, we present a novel pipeline for building semantic segmentation from oblique UAV images using a fully convolutional neural network (FCN). To cope with the lack of UAV image annotations at facade level, we leverage existing ground-view facades databases to simulate various aerial-view images based on estimated homography, yielding abundant synthetic aerial image annotations as training data. The FCN is trained end-to-end and tested on full-tile UAV images. Experiments demonstrate that the incorporation of simulated views can significantly boost the prediction accuracy of the network on UAV images and achieve reasonable segmentation performance.

The phenomenon of crowdsourcing has emerged as a new pattern of digitally mediated collaboration. This novel socio-technical arrangement changes the organization of work as well as its general nature and takes place in information systems (IS) in which humans face many threats to their dignity. For this reason, the importance of ethical issues within this new form of employment arises. Hence, in this paper we focus on the ethical issues in crowd work – a perspective that has been largely neglected by current crowdsourcing research. We analyze recent crowdsourcing literature and extract ethical issues by following the PAPA (privacy, accuracy, property and accessibility of information) concept, a well-established approach in IS. The review focuses on the individual perspective of crowdworkers, which addresses their working conditions and benefits. Although, the literature review exhibits that there are PAPA dimensions in crowdsourcing, only few focus on the crowdworkers as individuals. Our findings contribute to further research in crowdsourcing by introducing an ethical framework and give practical insight into how to design sustainable and ethical crowd work.

Very high resolution satellite imagery used to be a rare commodity, with infrequent satellite pass-over times over a specific area-of-interest obviating many useful applications. Today, more and more such satellite systems are available, with visual analysis and interpretation of imagery still important to derive relevant features and changes from satellite data. In order to allow efficient, robust and routine image analysis for humanitarian purposes, semi-automated feature extraction is of increasing importance for operational emergency mapping tasks. In the frame of the European Earth Observation program COPERNICUS and related research activities under the European Union's Seventh Framework Program, substantial scientific developments and mapping services are dedicated to satellite based humanitarian mapping and monitoring. In this paper, recent results in methodological research and development of routine services in satellite mapping for humanitarian situational awareness are reviewed and discussed. Ethical aspects of sensitivity and security of humanitarian mapping are deliberated. Furthermore methods for monitoring and analysis of refugee/internally displaced persons camps in humanitarian settings are assessed. Advantages and limitations of object-based image analysis, sample supervised segmentation and feature extraction are presented and discussed.