On Improving Urban Environment Representations

Computer Graphics has evolved into a mature and powerful field that offers many opportunities to enhance different disciplines, adapting to the specific needs of each. One of these important fields is the design and analysis of Urban Environments. In this article we try to offer a perspective of one of the sectors identified in Urban Environment studies: Urbanization. More precisely we focus on geometric and appearance modeling, rendering and simulation tools to help stakeholders in key decision stages of the process

Visualisation techniques, human perception and the built environment

Historically, architecture has a wealth of visualisation techniques that have evolved throughout the period of structural design, with Virtual Reality (VR) being a relatively recent addition to the toolbox. To date the effectiveness of VR has been demonstrated from conceptualisation through to final stages and maintenance, however, its full potential has yet to be realised (Bouchlaghem et al, 2005). According to Dewey (1934), perceptual integration was predicted to be transformational; as the observer would be able to ‘engage’ with the virtual environment. However, environmental representations are predominately focused on the area of vision, regardless of evidence stating that the experience is multi sensory. In addition, there is a marked lack of research exploring the complex interaction of environmental design and the user, such as the role of attention or conceptual interpretation. This paper identifies the potential of VR models to aid communication for the Built Environment with specific reference to human perception issues

Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation

Recent successes in machine learning have led to a shift in the design of autonomous systems, improving performance on existing tasks and rendering new applications possible. Data-focused approaches gain relevance across diverse, intricate applications when developing data collection and curation pipelines becomes more effective than manual behaviour design. The following work aims at increasing the efficiency of this pipeline in two principal ways: by utilising more powerful sources of informative data and by extracting additional information from existing data. In particular, we target three orthogonal fronts: imitation learning, domain adaptation, and transfer from simulation.Comment: Dissertation Summar

Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the information required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier capable of combining them. In this paper we explore several methods of creating such a classifier, including early, late, hybrid fusion and representation learning using multimodal graphs. We evaluate performance on a real world dataset obtained from a live citizen reporting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demonstrate that our hybrid combination of early and late fusion with multimodal embeddings performs best in classification of urban micro-events

Developing a sustainability KM strategy for HA planned works

Effective management of sustainability-related knowledge is central to the development of sustainable construction practices. Despite progress In other contexts, existing knowledge management efforts have been of limited value to contexts such as housing association planned works (cyclical replacement of housing components) due to an inability to reflect the specificities of these projects. This paper presents the development of a structured strategy to improve the capture, storage, retrieval and exchange of sustainability-related knowledge within housing association planned works. Knowledge mapping exercises based on semi-structured interviews were carried out within four different sized Scottish housing associations. Sustainability-related knowledge maps were developed for each activity focusing on managerial, economic, social, environmental aspects and overall flow of knowledge providing the basis for recommendations to improve the management of sustainability-related knowledge during planned works. The strategy promotes a structured approach providing housing associations with the opportunity to tailor the strategy to reflect their context and requirements. Practitioners from the case studies confirmed its usefulness especially for housing associations committed to sustainability but struggling to engage with high-level policy and strategies. One case study association has implemented the high-level principles to support its wider sustainability policy and is piloting a strategy for its planned works. </jats:p