Accurate information on urban building types plays a crucial role for urban development, planning, and management. In this paper, we apply Object-Based Image Analysis (OBIA) methods to extract buildings from Airborne Laser Scanner (ALS) data and investigate the possibility of classifying detected buildings into “Residential/Small Buildings”, “Apartment Buildings”, and “Industrial and Factory Building” classes by means of domain ontology and machine learning techniques. The buildings objects are classified using exclusively the information computed from the ALS data. To select the relevant features for predicting the classes of interest, the Random Forest classifier has been applied. The ontology-based classification yielded convincing results for the “Residential/Small Buildings” class (F-Measure 97.7%), whereas the “Apartment Buildings” and “Industrial and Factory Buildings” classes achieved less accurate results (F-Measure 60% and 51%, respectively).

Digitalisation has changed society, and, as a result, public administrations are required to undergo significant changes to satisfy emergent societal needs. These changes impact all areas of the public sector, including the development and provision of digital services, the design of processes, and the development of policy. To implement the digital strategies and transformation requirements, public administrations must rethink the competences that their workforce as well as the external stakeholders may need. To understand how one nation implements its digital strategy and upskills its civil servants, we conducted a qualitative analysis of 41 Austrian expert interviews. The research shows that different stakeholders require a variety of competences to participate in the digital transformation of its processes and services. The results demonstrate the high level of diversity and the need for a holistic approach to tackle the complexity of the digital public sector, where leadership plays the most important role. In addition, the study shows that the use of competence frameworks for measurement and monitoring needs to be adapted to the local context.

Reverse geocoding is defined as the extraction of textual information, such as a name or an address, from geographic coordinates. This technique is common in many geo-application scenarios, e.g., in freely available online-based mapping services. However, if personal data are mapped, confidentiality issues may arise, such as if the data are derived from a crime-incidents database. This research provides an evaluation of seven freely available Web mapping services in regard to reverse geocoding accuracy and privacy aspects. The services' textual accuracy is measured using crime data from Vienna, Austria. The accuracy associated with reverse geocoding is derived for districts with different residential densities to determine whether any error variation is associated with the underlying residential structure. Furthermore, this research investigates the reidentification risk when compromising personal information. Using a free online telephone database and addresses in a crime-incidents dataset, the probable names that are linked to crime victims are identified. The results showed that the accuracy ranges from 0% to 23% depending on the predefined precision level. Comparing seven online reverse geocoding services (TravelGIS, Google Maps, nominatim, ArcGIS, Yahoo, Bing, and Geonames), TravelGIS performs better at the highest precision level, whereas Geonames performs better at a lower precision level. However, the results showed no difference among the districts with different residential densities. As for the reidentification risk, 68% probable victims were identified from the database. This work discusses the specific tasks that users have to complete when retrieving addresses associated with crime-incident locations. The article concludes with an evaluation of the reverse geocoding process and a discussion of the privacy risk associated with it.

Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects’ properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

While migration research is at the peak of its productivity, a substantial gap persists between scientific evidence and policy action. As societal complexity increases, migration theory loses track on the numerous factors of human migration; the information on the most relevant factors affecting human migration (i.e., migration drivers), essential for policy decision-making, are hidden and dispersed across the ever-growing literature. Introducing a novel approach to conducting a literature review, emphasizing an unbiased selection of literature and the approach to analysing literature by coding, we collect evidence on the most pertinent migration factors. The study establishes a methodology for a quick but rigorous, collaborative gathering of evidence, as well as an initial inventory and an interactive map of nearly 200 factors working at different migration corridors.