Thomas J. Lampoltshammer

Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data

Abstract: 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).

Improving the Computational Performance of Ontology-Based Classification Using Graph Databases

The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protege environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protege-based approach.

Increasing collaboration and participation in smart city governance: a cross-case analysis of smart city initiatives

ABSTRACT This study addresses the concept of smart governance in the context of smart cities, with a focus on analyzing the phenomenon of smart collaboration. Relying on the existing collaboration and participation concepts in the smart city domain, an empirical analysis was undertaken of how ICT can promote collaborative governance and increase the participation and engagement in government. The multiple case studies focus on three cities in Brazil that run municipal operations centers in an effort to “become smarter”: Rio de Janeiro, Porto Alegre, and Belo Horizonte. Interviews with directors, managers, and technicians shed light on the contribution that ICT makes in promoting an environment of collaboration in the government. The findings have revealed that ICT has an important role in supporting information sharing and integration between government agencies and external stakeholders, including citizens, especially in developing countries.

Spatial-temporal Modeling of Linguistic Regions and Processes with Combined Indeterminate and Crisp Boundaries

The paper elaborates on the spatial-temporal modeling of linguistic and dialect phenomena. Language Geography—a branch of Human Geography—tries to enhance the visual exploration of linguistic data, and utilizes a number of methodologies from GIScience, whereas publications focusing on analyzing linguistic data in GIScience are hard to find. This research work highlights the representation of language and/or dialect regions with combined indeterminate and crisp boundaries—i.e. frontiers and borders. Both boundary “types” are necessary in order to model the spatial-temporal dynamics of language phenomena. The article analyzes the emerging, ending, moving and merging of linguistic/dialect regions and phenomena with respect to space and time and the boundary types. In order to represent frontiers or indeterminate boundaries, fuzzy logic is employed.

The Open Data Landscape

The opening of data has grown tremendously over the past decade. More and more datasets have been opened to the public, application programming interfaces (APIs) gave been design for enabling the public to make use of real-time data and new apps based on this data have been developed. Data about policy-making, software code (open sources), documents, minutes, financial data and so on has been opened resulting in a large repository of government data that can be on open data portals and government websites. Nevertheless the potential is even more higher, as most of the data still are closed and not directly accessible by the public. Furthermore, more and more data is collected and can be share in nowadays words driven by The Internet of Things (IoT). The IoT consist of devices that are able to collect data such as GPS (geographical location), Compass, temperature, movement, pollution and so on. Devices collecting data combined with data analytics are expected to transform the government and society. This can provide insight into the energy consumption of smart cities (https://amsterdamsmartcity.com/projects/energy-atlas) or the pollution (http://airindex.eea.europa.eu/). These initiates are all driven by the opening of data and extended by user-friendly apps to enable a large use by the public.

Open Government Data: Areas and Directions for Research

The concept of open data itself is strongly associated with innovative capacity and transformative power (Davies, Perini, & Alonso, 2013). It is increasingly recognized that proactively opening public data can create considerable benefits for several stakeholders, such as firms and individuals interested in the development of value added digital services or mobile applications, by combining various types of Open Government Data (OGD), and possibly other private data. On the other hand, OGD also empowers scientists, journalists and active citizens who want to understand various public issues and policies through advanced data processing and production of analytics (Janssen, 2011a; Zuiderwijk, Helbig, Gil-Garcia, & Janssen, 2014).

Open Data Value and Business Models

The chapter focuses on innovation processes aspiring to generate value through a purposeful and effective exploitation of data released in an open format. On the one hand, such processes represent a great opportunity for private and public organizations while, on the other, they pose a number of challenges having to do with creating the technical, legal and procedural preconditions as well as identifying appropriate business models that may guarantee the long term financial viability of such activities. As a matter of fact, while information sharing is widely recognized as a value multiplier, the release of information in an open data format through creative common licenses generates information-based common goods characterized by nonrivalry and nonexcludability in fruition. An aspect posing significant challenges for the pursuit of sustainable competitive advantages.

Open Data Interoperability

Semantic technologies enable open data interoperability beyond the point of pure format and structure alignment.

The Multiple Life Cycles of Open Data Creation and Use

Open data can be defined as data that is free of charge or provided at marginal cost, under an open licence , machine readable, and provided in an open format

Open Data Directives and Policies

In developing open data policies, organizations aim to stimulate and guide the publication and use of data and to gain advantages from this. Often open data policies are guided by a high-level directive, such as those of the United States (Obama, 2009b) and the European Commission (European Commission, 2013c). Open data policies are important, as their purpose is often to ensure the long-term availability of government information to create transparency and thereby to contribute to citizens’ rights to public access to government information. This right is considered a fundamental tenet of democracy (Allen, 1992). Moreover, open data policies have the potential to increase the participation, interaction, self-empowerment and social inclusion of open data users (e.g. citizens) and providers alike, stimulating economic growth and innovation and realizing many other advantages.