Place making for knowledge generation and innovation: Planning and branding Brisbane’s knowledge community precincts

Knowledge generation and innovation have been a priority for global city administrators particularly during the last couple of decades. This is mainly due to the growing consensus in identifying knowledge-based urban development as a panacea to the burgeoning economic problems. Place making has become a critical element for success in knowledge-based urban development as planning and branding places is claimed to be an effective marketing tool for attracting investment and talent. This paper aims to investigate the role of planning and branding in place making by assessing the effectiveness of planning and branding strategies in the development of knowledge and innovation milieus. The methodology of the study comprises reviewing the literature thoroughly, developing an analysis framework, and utilizing this framework in analyzing Brisbane’s knowledge community precincts—namely Boggo Road Knowledge Precinct, Kelvin Grove Urban Knowledge Village, and Sippy Downs Knowledge Town. The analysis findings generate invaluable insights in Brisbane’s journey in place making for knowledge and innovation milieus and communities. The results suggest as much as good planning, branding strategies and practice, the requirements of external and internal conditions also need to be met for successful place making in knowledge community precincts.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.